esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
A femtosecond laser, which has the advantage of precise ablation with little or no thermal impact on surrounding materials [11; 12], can overcome the above shortcomings as an ultra-short-pulse laser. A femtosecond laser has been widely used in many fields, such as the processing of various materials [13; 14] and the fabrication of micro-components [15; 16; 17], because of its unique processing characteristics. [PERSON], [PERSON] et al. [18] used a femtosecond laser to process a thin film of silver nanowires and CNTs. The edge of the cutting position is not uniform due to the different thresholds of Ag and CNTs, but it can be reduced by laser beam shaping in the edge deformation zone of the composite film. Cumulative effects induced by increasing number of laser pulses and ripples originate from the interference between the incident laser light and the scattered tangential wave are observed in the ablation of CNT film [19]. The material at the edge of the cutting position are damaged by femtosecond laser processing. [PERSON], [PERSON] et al. [20] used Raman spectroscopy to detect the cutting position under the femtosecond laser direct patterning of graphene film. D band intensity increased significantly, since there was a relaxation of the backscattering condition with an induced defect at the edge of the graphene sheet. However, there has been relatively little research on the influence of laser parameters on cutting quality and defects in the femtosecond laser machining of CNT film. This work aimed to explore the mechanism and influencing factors in the ablation and cutting process for the femtosecond laser machining of CNT film. Experiments were conducted to obtain the threshold of CNT film and the influence of cutting parameters on machining results. Specific features of the SEM and Raman spectrum were discussed to analyze the ablation mechanism and machining quality. Furthermore, preferred processing parameters were selected to cut the microstructure of CNT film with good cutting quality. This paves the way to advance the possible applications of CNT film in the field of the flexible electronic devices and conductive film.
## 3 Optimal Energy Consumption Model in HWSNs
### Average Energy Consumption
Recently most algorithms are based on omnidirectional antenna technology, however, studies for using directional antennas in wireless sensor networks have gradually appeared. Compared with the omnidirectional antenna, a directional antenna model can improve the multiplexing rate of the network spatial coverage, increase the coverage area, and cost less energy consumption, _etc._[24,25]. Once the cluster heads turn into the directional mode to communicate with a sink, Equation (3) should be changed to:  
\[E_{toBS}=lE_{elce}+\theta l\epsilon_{mp}R_{i}^{4} \tag{7}\]  
where \(\theta\) is the conversion factor for energy consumption in case of switching to the directional antenna model. In a cluster, the energy consumption of the CH for receiving messages from its member nodes is equal to:  
\[E_{rec}=lE_{elce}(\pi r_{i}^{2}\sigma-1) \tag{8}\]Hence the average energy of the area consumed by each node is estimated as:  
\[\widetilde{E}=\frac{E_{cluster-in}}{\left.\frac{1E_{elec}(\pi\sigma_{i}^{2}\sigma -1)+1E_{elec}+\partial l\epsilon_{mp}R_{i}^{4}+l\pi\sigma(E_{elec}r_{i}^{2}+ \frac{1}{2}\epsilon_{fs}r_{i}^{4})}{\pi\sigma_{i}^{2}\sigma}\right. \tag{9}\]  
By calculating the derivative of \(\widetilde{E}\) with respect to \(r_{i}\) at zero, we can get optimal transmission radius:  
\[r_{i}=\sqrt[4]{\frac{\partial\epsilon_{mp}}{\pi\sigma\epsilon_{fs}}}R_{i} \tag{10}\]  
Substituting Equation (10) into (4), we can obtain an approximation of the total energy consumed by a single node within a cluster. According to [26], we can get the optimal cluster number \(k_{opt}\) for our cluster based on the network where \(N\) sensor nodes are distributed randomly in a \(M\times\ M\) sensor field as follows:  
\[k_{opt}=\sqrt{\frac{N}{2\pi}}\times\sqrt{\frac{\epsilon_{fs}}{\epsilon_{mp}}} \times\frac{M}{d^{2}} \tag{11}\]  
Thus, the estimated value of the entire network lifetime is:  
\[RT=\frac{E_{total}}{k_{opt}*\widetilde{E}_{cluster-in}} \tag{12}\]  
In the later experiments, we consider the effect of random factors in which all nodes will not die at the same time. Then, \(RT\) in the Equation (12) is taken to be 1.5 times the estimated lifetime value. Suppose that each node's energy is consumed uniformly in every round, then the average energy in the round \(rt\) can be estimated as:  
\[E_{cluster-in}=E_{toCH}+E_{toBS}+E_{rec} \tag{4}\]0  
In order to prolong the lifetime of the whole network, we should ensure that all nodes die at the same time. Obviously, the nodes with higher residual energy should possess a much higher average probability \(p_{i}\) for cluster head selection than the other sensors, which can make the network consume energy evenly.
## 3 Results and Discussion
### Effects of Fretting and Torsional Parameters on Dissipation Energy Characteristics
#### 3.2.2 Hysteresis Loops of Torque versus Torsion Angle
At loading cycles of 20,000, dissipation energies in cases of crossing angles of \(90^{\circ}\), \(70^{\circ}\), \(45^{\circ}\) and \(26^{\circ}\) are \(9.97\times 10^{-3}\) J, \(9.36\times 10^{-3}\) J, \(6.95\times 10^{-3}\) J and \(6.37\times 10^{-3}\) J, respectively. Therefore, an increase of crossing angle induces increases in the dissipation energy and thus the fretting contact surface damage of cyclic loaded wire. Figure 11: _Cont._  
Figure 10: Evolutions and dissipation energy of torque versus torsion angle during tension–torsion cyclic loading at different torsion angles. (**a**) \(\theta=\pm 1^{\circ}\). (**b**) \(\theta=\pm 1.5^{\circ}\). (**c**) \(\theta=\pm 2^{\circ}\). (**d**) \(\theta=\pm 2.5^{\circ}\).
# Research on a Dynamic Calibration Method for Photogrammetry Based on Rotary Motion
## 4 Experiment and Analysis  
### The Main Use Method of the Circumference Dynamic Calibration Device  
1. Measuring and controlling the field layout. The marking points suitable for calibrating the photogrammetry system are pasted on the device disc -LRB-; usually, the coding points are used for a quick tracking of the measuring system, and the marking points are the shooting objects of the measured digital photogrammetry system, which provides the location criteria through prior static calibration. The number of marks is two or more. The marks are pasted on the surface of the turntable with circular motion. When the turntable moves, the marks also maintains a corresponding movement. A sufficient number of marking points and control points are arranged around the turntable as the measuring control field, and at least one datum ruler is placed as the length datum.
2. Measurement preparation of the photogrammetry system. Connect and arrange the camera, the controller, and the light source according to the system instruction. Additionally, according to the system operating instructions, the camera distance (recommended camera distance for the calibration device references a distance of 3 M), the camera distribution, and the rendezvous angle are selected. The system installation, layout, connection, and parameter setting are included. The field of view of the two cameras is aligned and held fixed at the middle position of the upper surface of the disc of the calibrated device. When the calibration device rotates, the photographic target is within the effective measuring range of the photogrammetry system.
3. Dynamic calibration. Set the running speed of the dynamic calibration device, control the movement of the turntable, and use the calibrated photogrammetry system to  
Figure 5: The 200 rpm oscilloscope waveform.  
take continuous photographs of the moving photographic target. The position measurement results of the photogrammetry system at the corresponding velocity are obtained by processing and calculating the measurement using the software provided by the calibrated system.
4. Result analysis. The measured results are compared with the standard values to verify the dynamic measurement performance of the digital photogrammetry system, including its length measurement error, motion trajectory, and velocity in a dynamic environment.
# MA_W-Net-Based Dual-Output Method for Microseismic Localization in Strong Noise Environments
## 4 Conclusions  
We propose a deep learning method for the localization of microseismic sources within noisy data environments. Based on multi-task learning, a decoder responsible for the auxiliary task is added in addition to the main task of seismic source localization, which improves the model's localization accuracy at low SNRs. Furthermore, CBAMs were integrated into the encoder; CBAM effectively integrates both spatial and channel dimensions of the feature map, which can improve the generalization ability and performance of the model. CA and SE attention modules were also integrated into the decoder, and CA can enhance the localization of microseismic source locations. SE can learn to use global information to selectively emphasize informative features and suppress useless ones, enhancing the denoising capability of the network. We use a Gaussian mixture model to represent source location information for microseisms. To improve the stability and prediction ability of the network, the weighted mean square error is used as the loss function for the main task and different weights are assigned to the losses of the two tasks. The self-supervised assisted denoising task overcomes the difficulty of obtaining noise-free data as labels. Our method can improve the accuracy of seismic recordings with a SNR of \(-15\) from 78% to 93.5% and the average error from 2.60 m to 0.76 m comparing with a normal U-net network. Experimental results show that our method can localize microseismic events with low SNRs and can keep the error within a small range. We plan to introduce real seismic data in future research to further validate the model's effectiveness and generalization ability in practical applications.  
Conceptualization, Q.L., F.Z. and L.H.; Methodology, Q.L., F.Z. and L.H.; Software, Q.L.; Validation, Q.L., F.Z. and L.H.; Formal analysis, Q.L.; Investigation, Q.L.; Writing--original draft, Q.L., F.Z. and L.H.; Writing--review & editing, Q.L., F.Z. and L.H.; Visualization, Q.L.; Supervision, F.Z.; Project administration, F.Z. and L.H.; Funding acquisition, F.Z. and L.H. All authors have read and agreed to the published version of the manuscript.  
Please add: This research is supported by the National Natural Science Foundation of China (No.42130805, No.42074154, No.41974138).  
Figure 16: Noise suppression and localization results under three events, black crosses indicate the real source location: (**a**) original seismic record; (**b**) seismic record with added noise; (**c**) suppressed seismic record; (**d**) localization results.
## 1 Introduction and Related Works
The traffic conditions the bus experiences are not generally considered as proper representations of current traffic conditions. An artificial neural network (ANN) simulates a human brain that consists of neurons and excels at pattern recognition rather than analytically processing and formulating for a given phenomenon. Conventional GPS receivers are only capable of directly measuring location information from which acceleration data can only be post-estimated. Recent smartphone models have accelerometers that directly measure the acceleration or deceleration values. In addition, smartphones are also equipped with magnetometers that can measure the strength of electromagnetic fields in the vicinity. A well-trained ANN could determine the mode of transportation a user is in by observing the user's GPS, accelerometer, and magnetometer related information such as speed, location, quality of GPS data, acceleration, and electromagnetic field measurements. As an analogy, a man with his eyes and ears covered may still be able to \"feel\" and determine whether he is in a typical car (auto mode) or a bus by sensing the physical characteristics around him, such as acceleration and deceleration patterns. A geographic information system (GIS) is a platform on which traffic conditions can be spatially managed and is an important urban analysis tool in geoinformatics. Applying GIS-based spatial analyses on traffic-related data can enhance mode detection accuracies by considering available modes near the location where a mode estimation is made. Dubai is a city with unique environmental factors that can affect mode detection accuracies. Due to the high density of high-rise buildings typically with a ratio of a building height to a street-canyon-width of above 100 to 1, GPS signals are prone to multi-path errors. The extremely hot outdoor temperature has introduced indoor walk modes at pathways connecting their metro and major destinations including the Dubai Mall, known as a Metro Link Bridge ([[https://thedubaimall.com/en/plan-your-visit/getting-here](https://thedubaimall.com/en/plan-your-visit/getting-here)]([https://thedubaimall.com/en/plan-your-visit/getting-here](https://thedubaimall.com/en/plan-your-visit/getting-here))). It is interesting to see how the unique geographic factors play their roles as far as the ANN-based mode detection rates are concerned. For example, the high-rise buildings may tend to introduce noise in the GPS signals making location determination and associated speed information less accurate. The indoor pathways for pedestrians may block the direct line of sight to GPS satellites, which may also deteriorate the strength of the signal.
# Wireless Energy Harvesting for Internet-of-Things Devices Using Directional Antennas
## 6 Conclusions  
In this work, we combine beamforming and directional transmission technology to decrease the charging time and overflow energy. We propose two algorithms for RF energy harvesting. The proposed greedy algorithm is designed to minimize the charging time. The proposed differential evolution algorithm further reduces the overflow energy to distribute the completion time of fully charged devices. It also has the advantages of fast convergence and the ability to search for global optimal solutions. We verify the performance of the proposed algorithms with respect to different scenarios, including transmission and offset angles, different topologies, and charging ratios. The simulation results show that the proposed greedy algorithm can reduce the charging time by up to 55% compared to the baseline algorithms. Although the proposed DE algorithm requires about 10% longer charging time than the greedy algorithm, it also reduces the overflow energy by 10%. Moreover, by reducing the number of fully charged devices at the same time, the proposed DE algorithm can drastically improve the data rate of devices by 160% or more. In summary, the proposed greedy algorithm is suitable for applications without the demand of the massive data transmission. For applications which require a high data rate, the DE algorithm can lower the energy overflow to significantly improve the performance of data transmission.  
In our future work, we will consider the computation offloading problem for the energy-harvesting devices. The lifetime of the devices will be jointly considered with their computation tasks. More practical limitations of energy-harvesting devices will also be considered, e.g., variable energy conversion efficiency and channel gain.  
Figure 23: The total data rate of fully charged devices in different iterations. (**a**) Ratio = 1, (**b**) Ratio = 0.8, (**c**) Ratio = 0.6.  
**Author Contributions:** Conceptualization, H.-C.C. and P.-C.W.; methodology, H.-C.C.; software, H.-C.C.; validation, H.-C.C.; formal analysis, H.-C.C.; investigation, H.-C.C.; resources, P.-C.W.; data curation, H.-T.L.; writing--original draft preparation, H.-C.C.; writing--review and editing, P.-C.W.; visualization, H.-C.C.; supervision, H.-T.L.; project administration, P.-C.W.; funding acquisition, P.-C.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by National Science and Technology Council, grant number NSTC 111-2221-E-005-045.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 2 Literature Review  
### Safety Potential Fields  
The safety potential field (SPF) model is a concept used in the field of autonomous vehicles and robotics to enhance safety and navigation efficiency. In practical terms, this model works by calculating the forces acting on a vehicle based on its proximity to objects or boundaries. When a vehicle approaches an obstacle, the repulsive force from the obstacle pushes the vehicle away to prevent collisions. By employing the SPF model, autonomous vehicles can navigate complex environments with greater safety and efficiency. This model is crucial for ensuring that vehicles can move smoothly, avoid collisions, and reach their destinations effectively [27].  
The SPF leverages field theory to encapsulate risk factors originating from drivers, vehicles, road conditions, and other traffic factors. This approach is instrumental in assessing potential driving risks in actual traffic scenarios and forecasting trends in driving safety due to dynamic changes [28]. Building on this concept, [PERSON] et al. [29] took into account a comprehensive model of human-vehicle-road interaction and introduced a vehicle collision warning algorithm based on the driving-safety field model. Their work delved into both the modelling and practical application of driving-safety field theory. [PERSON] et al. [30] crafted a dynamic authority allocation strategy between the driver and the trajectory tracking controller, based on game theory and driving-safety field theory. They developed a safety control model for assessing driver risk, with experimental results indicating that their strategy effectively aids vehicles in achieving robust obstacle avoidance performance. The strategy also empowers the controller to proactively take control of the vehicle, guided by the assessment of driving risk under current conditions.  
Further advancing this field, [PERSON] et al. [31] proposed a risk perception and warning strategy based on a safety potential field model. This model delineates the spatial distribution of vehicle driving risk by creating a dynamic model of vehicle operation. Additionally, [PERSON] and [PERSON] [32] established a lane-changing model using safety potential field theory. Numerical simulation analysis showed that this model proficiently reflects the impact of various motion parameters on lane-changing results. Expanding their research, they [33] also developed a macroscopic model for heterogeneous traffic flow in an interconnected automated environment, based on safety potential field theory. Their results underscored that the penetration rate of such technology significantly impacts road capacity, highlighting the model's relevance in understanding and managing complex traffic dynamics. Hence, we integrate SPF with microscopic simulation models to comprehensively describe microscopic driving behaviours using a dynamic driving risk potential field, further characterising the level of safety risk during the driving process.
## 3 Experimental Setup
### Test Conditions  
The experimental test campaigns carried out took into account several considerations, such as various longitudinal tilt angles, various wind speeds, various thermal powers, the same electrical efficiency, similar ambient temperatures, the same water flow rate, and the same duration of each test. These considerations are described below.
## 3 Video Monitoring & Internet of Things
### Internet of Things  
The Internet of Things (IoT) is a network of various gadgets that share and transmit information from the real world to offer services to people, businesses, and society [12; 58].  
Advantages of the Data  
One major question relevant to the IoT is how it could influence our daily life. Continuous feeds are sent and received by every sort of sensor to track and automate operations [59]. Intending to bring about bold change or enhance current services, there are various possibilities where the streaming data could open up new markets. Several examples of fields that constitute the heart of this progress are listed below [60]: IoT-(a) Smart solutions in the bucket of transport, IoT-(b) Smart power grids incorporating more renewable, IoT-(c) Remote monitoring of patients, IoT-(d) Sensors in homes and airports, IoT-(e) Engine monitoring sensors that detect and predict maintenance issues.
# [PERSON] Glacier Status Analysis Based on Historical Data and Moving Detection Using RPAS
## 4 Used Instruments  
Remotely piloted aircraft system (RPAS) is a less used but correct acronym for a drone [13]. The eBee drone, senseFly, from Switzerland, was used in this case for different reasons. (1) It is light, made from Styrofoam, easy to use, takes off by hand, and lands without any special landing pad, (2) it is easy to transport (in a padded backpack), can be divided into several parts for transport, is electrically powered, and uses small batteries, (3) it has a relatively long time of flight up to 40 min and altitude access of hundreds of m, because it is a winged drone, and (4) it flies with full autonomy based on GNSS (Global Navigation Satellite System) and IMU (Inertial Measurement Unit) devices and the flight plan, and has changeable cameras (Figures 5 and 6). However, the drone weighs less than one kilogram and the flight can be affected by gusts of wind, and furthermore, cameras used in this type is low-cost only. Unfortunately, the type used was not a GNSS RTK (Real Time Kinematic) system, which allows precise georeferencing of outputs without geodetically measured ground control points (GCP).  
Figure 4: Knud Rasmussen Glacier; it appears to be a small glacier, but its width is over 2 km.  
Figure 5: The Remotely piloted aircraft system (RPAS) eBee.  
Figure 6: (**a**) Spectral response of the Canon PowerShot ELPH 110 HS (NIR) and (**b**) the changeable cameras for the eBee drone: near-infrared (NIR), red-edge (RE), and red-green-blue (RGB). The camera has a \(1/1.7\)” back-illuminated CMOS (Complementary metal–oxide–semiconductor) sensor, image size 12.1 MPaix, camera weight 153g excl. battery.
## Materials and Methods
### Data
#### 2.2.4 Meteorological and Ancillary Data  
Meteorological data were acquired from the meteorological network of the National Institute of Forestry Agriculture and Livestock Research (INIFAP, Mexico City, Mexico) ([[http://clima.inifap.gob.mx/redinifap/](http://clima.inifap.gob.mx/redinifap/)]([http://clima.inifap.gob.mx/redinifap/](http://clima.inifap.gob.mx/redinifap/))) and the National Commission of Water (CONAGUA, Mexico City, Mexico) network. The INIFAP meteorological network collects an additional number of variables to those that are the standard meteorological network variables available. This data recording process started operations in 2007, and it is currently active. The climatic databases from INIFAP provided inputs for the NPP modelling process.  
From the INIFAP meteorological network, five different meteorological stations were used in this study, distributed over the study area (Table 1). For each meteorological station, solar radiation (W/m\({}^{2}\)), daily mean, maximum and minimum temperature (\({}^{\circ}\)C), total precipitation (mm) and potential evapotranspiration (mm) were acquired from 1 January 2007 to 31 December 2013.  
Digital elevation models (DEMs) and land use data, from the study area, were acquired from the National Institute of Statistics, Geography, and Informatics (INEGI, Aguascalientes, Mexico). The ArcGIS(tm) 10.3 software package was used to visualise, explore and process all spatial data of the study area extent. The spatial resolution of the data and the projection system were kept the same as the surface reflectance images.