esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
According to the characteristics of the LEO space particles radiation environment of China's Fengyun No. 3 (FY-3) polar-orbiting meteorological satellites, in order to monitor the characteristics, and space-time distribution of charged particle radiation in the orbit space, it is proposed to install a three-directional high-energy particle detector (HEPD) in the three vertical orthogonal directions of FY-3E, so as to carry out the energy spectrum and flux observation of high-energy protons and electrons in the three directions of the satellite, namely, \(-\)X, +Y, and \(-\)Z. The on-orbit detection data acquired by these payloads can be used for space environment modeling and solar-terrestrial physics research, and provide data sources for operational space environment weather warning and forecasting. Through the ground accelerator calibration experiment and simulation analysis of the three-directional HEPDs developed in the flight model phase, the experimental results show that all the HEPDs' measured values meet the requirements for technical indexes, such as the detection energy range (high-energy protons: 3-300 MeV; high-energy electrons: 0.15-5.7 MeV), energy span accuracy (<15%), flux accuracy (<15%), and sensitivity (<5% (AN/N)).  
FY-3; space environment; high-energy proton; high-energy electron; energy spectrum; flux
## 2 Material and Methods
### Mathematical Formulation
#### 2.3.2 Equations for the Absorption Heat Pump  
The operating working fluid in the single-effect absorption heat pump is the LiBr/water and Figure 1 clearly shows this device, while Figure 2 depicts the different operating modes. The present mathematical modeling is based on some usual assumptions that are made in similar simulations. More specifically [28]:  
* Every device is assumed to be in steady-state conditions in order to apply the energy balance.
* There are no piping pressure losses, and the pressure level changes only in the pumps and in the valves.
* The process in the throttling valves is ideal and so the enthalpy is preserved.
* There is no LiBr in the condenser and in the evaporator devices.  
There are no thermal losses from the system to the ambient.  
The exits of the devices (evaporator, condenser, absorber and generator) are assumed to be saturated state points.  
Below, the basic mathematical formulation, based on the aforementioned assumptions, is given:  
The energy balance in the generator is given as:  
\[\mathrm{Q_{gen}}+\mathrm{m_{w}}\cdot\mathrm{h_{2}}=\mathrm{m_{s}}\cdot\mathrm{ h_{4}}+\mathrm{m_{r}}\cdot\mathrm{h_{3}} \tag{9}\]  
The energy balance in the absorber is given as:  
\[\mathrm{Q_{abs}}+\mathrm{m_{w}}\cdot\mathrm{h_{1}}=\mathrm{m_{s}}\cdot\mathrm{ h_{5}}+\mathrm{m_{r}}\cdot\mathrm{h_{8}} \tag{10}\]  
The energy balance in the evaporator is given as:  
\[\mathrm{Q_{evap}}=\mathrm{m_{r}}\cdot(\mathrm{h_{8}}-\mathrm{h_{7}}) \tag{11}\]  
The energy balance in the condenser is given as:  
\[\mathrm{Q_{con}}=\mathrm{m_{r}}\cdot(\mathrm{h_{3}}-\mathrm{h_{6}}) \tag{12}\]  
The expansions in the throttling valves are assumed to be isenthalpic processes due to the lack of thermal losses. Thus, it can be written as:  
\[\mathrm{h_{5}}=\mathrm{h_{45}} \tag{13}\]  
\[\mathrm{h_{7}}=\mathrm{h_{6}} \tag{14}\]  
The work consumption in the solution pump is very small and thus it can be written as:  
\[\mathrm{h_{12}}\approx\mathrm{h_{1}} \tag{15}\]  
The energy balance in the heat exchanger can be written as follows:  
\[\mathrm{m_{w}}\cdot(\mathrm{h_{2}}-\mathrm{h_{12}})=\mathrm{m_{s}}\cdot( \mathrm{h_{4}}-\mathrm{h_{45}}) \tag{16}\]  
The heat exchanger effectiveness of the solution heat exchanger is defined as follows:  
\[\mathrm{\eta_{HEX}}=\frac{\mathrm{h_{4}}-\mathrm{h_{45}}}{\mathrm{h_{4}}- \mathrm{h_{12}}} \tag{17}\]  
The total mass flow rate balance in the generator is calculated as follows:  
\[\mathrm{m_{s}}=\mathrm{m_{w}}+\mathrm{m_{r}} \tag{18}\]  
The LiBr mass flow rate in the generator is calculated as follows:  
\[\mathrm{m_{s}}\cdot\mathrm{X_{s}}=\mathrm{m_{w}}\cdot\mathrm{X_{w}} \tag{19}\]
## 3 Experimental Results
### Evolution of the Crack Network under an SSR of 0.15  
Figure 5 shows the initiation and propagation process of crack network during crater blasting under an SSR of 0.15. Under the coupling of dynamic and static loads, four initial radial cracks were observed first at the centre of the blast hole at 70 us (see Figure 5b). However, when compared with the radial cracks without static stress, the lengths of the four initial radial cracks under SSR of 0.15 were different. The radial cracks parallel to the static stress were longer than the radial cracks that were perpendicular to the static stress. At 110 us, the initial circumferential crack started to appear on the right of the blast hole, as shown in Figure 5c. At 140 us, the initial circumferential crack was completely formed (see Figure 5d). It was obvious that the initial circumferential crack was elliptical with its long axis parallel to the static stress. At 150 us, the second radial cracks appeared outside the initial circumferential crack (see Figure 5e). At 170 us, the second circumferential cracks started to appear on the left and right sides of the blast hole (see Figure 5f). At 250 us, the second circumferential crack was fully formed, which stopped the propagation of the second radial cracks, and a small amount of blasting dust was emitted (see Figure 5g). Clearly, the second circumferential crack was also elliptical with a long axis that was parallel to the static stress. At 500 us, the main radial and circumferential cracks were fully formed, and the blasting crater morphology was determined (see Figure 5h). After 500 us, the blast fragments flew outward.  
Figure 5: The crack network at different times during crater blasting under an SSR of 0.15. **(a-i)** The crack network after 0, 70, 110, 140, 150, 170, 250, 500, and 890 μs of explosion, respectively.
# Flickering-Free Distance-Independent Modulation Scheme for OCC
## 5 Methodology
### Simulation Setup  
In this case, the frame acquisition process was simulated for a camera with diagonal Field of View FOV\(=67^{\circ}\). The camera's frame rate was set to 30 fps. To emphasize the light bands, a high contrast (255), low brightness (zero), and short exposure time (\(1/1000\ s\)) were assumed. For speeding up the simulations, a basic image resolution of \(640\times 480\) pixels was selected. The emitter was a 10.00 cm \(\times 10.00\) cm white LED-based lamp with square shape. The distance between the camera and the emitter was set to 0.25 m for assuring that the whole frame represents the light source while the environmental conditions can be neglected, therefore the greatest range of \(\alpha\) values can be evaluated.  
From Equation (9), the maximum value of the frequency multiplier \(\alpha\) for the selected resolution (\(N_{y}=640\)) is 64 (\(\alpha<N_{y}/10\)). On the other hand, since the whole frame represents the light source and the minimum value of \(\alpha\) is one (\(f_{\mathrm{SW}}=f_{\mathrm{CAM}}\)), the frame has at least two bands for all the cases in the simulation. When \(f_{\mathrm{SW}}=f_{\mathrm{CAM}}\) the signal period corresponds to the frame's acquisition time (\(T_{\mathrm{SW}}=T_{\mathrm{CAM}}\)), therefore the frame has a complete light band and a complete dark strip. Taking this fact into account, for the simulation, the frequency multiplier was set from 10 to 70. For all the considered cases, the number of extracted light strips should be greater than two. However, the number of projected pixels for each band at \(\alpha=70\) is less than five, the amount of pixels that assures the data extraction for long distances.  
Using this range of \(\alpha\), the coupled switching signal's frequency varies from 300 Hz to 2100 Hz. For all the cases, it is expected that the PCC of consecutive frames for the different calculated switching frequencies are distinguishable. The PCC values should not overlap, allowing the correct demodulation of the four symbols. A total of 7500 frames (corresponding to the transmission of 5000 bits represented by 2500 symbols) for each value of \(\alpha\) were generated.
## 5 Results and Discussion
### Parametric Setup  
Performance is evaluated on a four-bypass highway environment with bidirectional hops movement. The speed of the node varies between 10 and 40 m/s. In a multi-hop ad-hoc region, every node in V2V communication uses similar IEEE 802.11p standard MAC specifications. The node transmission area is up to 0.3 km, and the time taken for simulation is 2.5 min, with a data rates of up to 6000 kbps for the safety application and size of the messages at 4096 bits [33]. The ad hoc coverage range and the data transfer rate are considered according to the IEEE 802.11p standard.  
Vehicular density is considered constant, and total hops (i.e., nodes) competing for the channel vary between 5-40, within five steps. In the case of heterogeneous illustration, our proposed MAMC-MAC protocol is correlated with standard protocol scenario, which displays appreciable output. The parameters used in the simulation are shown in Table 1.
# Process Optimization and Wear Performance of Plasma-Cladding Fe5 Coatings on Rotary Tillage Blades
## 2 Materials and Methods
### Performance Testing Methods  
The microstructure and micro-morphology of the cladding layer were observed using a Tescan Mira 4 scanning electron microscope (SEM). Elemental composition analysis was performed using the energy-dispersive X-ray spectroscopy (EDS) system integrated with the SEM. The phase composition of the cladding layer was analyzed using a D8 ADVANCE X-ray diffractometer, with a scanning range of 20 = 10\({}^{\circ}\) to 90\({}^{\circ}\) and a scanning speed of 5\({}^{\circ}\)/min.  
The micro-Vickers hardness of the cladding layer was measured using an HV-1000Z micro-Vickers hardness tester, with a loading force of 0.5 kg and a loading time of 5 s. Testing began at a distance of 50 nm from the surface, and one point was tested every 200 nm. To reduce measurement error, the average value of three points at the same depth was taken.  
The wear performance tests were conducted using an MLS-225 wet sand rubber wheel abrasion tester [24; 25]. The quartz sand particle size was set to 40-70 mesh, with a test load of 70 N, a sand-to-water ratio of 3:1, and a rotational speed of 200 r/min. Each sample was tested three times, and the average value was taken.  
The wet sand rubber wheel wear test was conducted using a Xiangyi electronic analytical balance, with a device accuracy of 0.0001 g and an error range of \(\pm\)0.0002 g. Every sample was tested three times, and the average value was taken as the result. In the field tests, soil compaction was measured using the SC900 soil compaction meter, and an S-shaped sampling method was employed to avoid depressions, protrusions, and stones, with a total of nine points being tested; the average value was taken for the test results. The soil moisture content was measured using a halogen moisture analyzer by adopting a five-point sampling method, and the average value was taken for the results. For the field trial, the weight of the rotary tiller blades was measured using a DM6 household electronic scale, with a precision of 0.1 g and an error range of \(\pm\)0.05 g. Every sample was also tested three times, and the average value was used as the final result.
# Event-Based Emergency Detection for Safe Drone
## 4 Case Study
### Normal Case  
The first case is a normal condition where no emergency event occurs to the drone. This case is a control group for future comparison with other cases. Figure 5a demonstrates the drone's movement data during a mission in the simulation environment. It is in a form of a graph, plotted with sequential 2D locations (x, y) of the drone. As shown below, the drone followed the path well and did not detect any type of emergency. In Figure 5b, we can observe that the drone made it within the boundaries of normal arrival time on every waypoint arrival. Further in Table 1, the actual numerical time data including T\({}_{\mathrm{start}}\), T\({}_{\mathrm{min}}\), T\({}_{\mathrm{max}}\), and the actual arrival time T\({}_{\mathrm{arrival}}\) is displayed.  
By analyzing the normal case, we were able to partially validate the concept we are introducing and get an idea of the trend of the result data we can expect from the simulator. Especially from the time-window check results, the \(\text{T}_{\text{arrival}}\) value showed a decreasing pattern, which was due to the drone approaching closer to the desired speed over time. In addition, we gained confidence on the length of the time-window scope we implemented, which was one second. This allowed a foundation to be established for further experiments with different cases.  
Figure 5: (**a**). Drone movement 2D log on normal case. The results show the drone managed the mission well with no failures. Both the drones with and without an event-based controller showed similar routes. (**b**). Time-window check results. The black dots illustrate the actual arrival time for each waypoint interval relative to each time-window. In this case, the drone made it in all normal arrival time-windows. \(\text{T}_{\text{arrival}}\) showed a decreasing pattern over time as the drone approached the desired speed.
# Towards Flexible Distribution Systems: Future Adaptive Management Schemes
## 2 Evolution of Flexible Distribution System
### Role of Sector Coupling and P2X2P Technologies  
Resiliency can be improved with flexibilities such as storages, but also increased integration of different sectors (electricity, heat, transport, communication) will have an important role. From the resiliency and future distribution network operation and planning viewpoint, for example, locating battery energy storages (BESSs), bi-directional EV fast-charging stations, 5G base stations (for low-latency wireless communication) (a) on the same MV/LV substations at chosen network key locations or b) on EV parking lots with potential photovoltaic (PV) generation, could maximize the flexibilities use potential and resiliency of both distribution network and communication infrastructure (backup power from BESSs and bi-directional EV chargers). In addition, future HV/MV substations could be developed toward sector integration stations with a number of flexible energy resources such as (i) large-scale BESS, EV parking lots with very fast and bi-directional chargers, and hydrogen electrolyzer also producing heat to local heating network or community heat storage. However, regulation development is needed in order to increase coupling between different sectors and energy networks (electricity, heat, hydrogen, natural gas,transportation, data/ICT, etc.) [2], e.g., related to taxation energy storages (including also electricity-to-heat-storages and P2X2P -solutions).  
Moreover, P2X2P technology can play a key role in improving the flexibility of the distribution network. This technology has already been employed in energy storage devices such as thermal and electrochemical storages. In this regard, the electricity is converted to different types of end-products or other types of energy, and it is converted back to electricity when required. For example, electricity is converted to methanol and methane than can be further used as fuels to produce electricity or other products. Thus, not only can this technology be used as flexible resources, but the obtained alternative carbon-free fuels can also reduce greenhouse gases emission and their negative impacts on the environment. In terms of flexibility enhancement, some projects such as FLEXnCONFU [18] assess the flexibility potential of P2X2P technology. The project aims to design an integrated power plant that improves the operational flexibility of the electrical network. In the first step, electricity is converted into hydrogen (H\({}_{2}\)) or ammonia (NH\({}_{3}\)) when the network has an electricity surplus. These carbon-free fuels are then converted back to electricity when the network needs more electricity injection. Reference [19] also analyses opportunities of the innovative P2X technology regarding Danish wind development and flexibility improvement.
## 4 Robustness Testing
### Environmental Conditions Test
The results suggest the presence of trees and nearby vehicle traffic did not have any negative effects on the signals. The high air temperature led to significant increase of packet loss rate and this slightly affected the signals in terms of RSSI and standard deviation of RSSI. The strong wind reduced the signal strength but did not vary the stability of signals. Pedestrian traffic without blocking the line of sight, led to mild increases of packet loss rate, and did not impact the stability of the iBeacon signals. Pedestrian traffic blocking the line of sight, caused the largest adverse impact on both packet loss rate, RSSI and the standard deviation of RSSI. The effects of the line of sight were far more significant than other environmental factors. When accounting for ways to provide stable and accurate positioning, the results recommend that the deployment of signal-based positioning systems should consider the line of sight as the first priority, as the signal quality and stability are most easily impacted by this factor.
## 3 Materials and Methods
### Software and Hardware Requirements  
Python 3.10 programming language was used to implement all algorithms. The code was developed and executed using Anaconda 2023.09 distribution for environment management, Google Colab Pro (subscription-based), and Vertex Workbench 1.11 cloud platforms. We utilized various libraries including accelerate 0.16.0, PyTorch 2.0.1, torchvision 0.15.2, transformers 4.25.1, huggingface hub 0.15.1, diffusers 0.14.0, xformers 0.0.20, and bitsandbytes 0.37.0. Computations were performed on high-performance hardware: g2-standard-48 machines (4 NVIDIA L4 GPUs, 48 vCPUs, 192 GB RAM) and a 40 GB NVIDIA A100 GPU. The system utilized 83.5 GB of total RAM (with 2.2 GB utilized during the experiments) and 40.0 GB of GPU RAM, with a storage capacity of 235.7 GB (100 GB data disk + 150 GB boot disk). With pretrained Stable Diffusion 1.5 and SAM models for image synthesis and segmentation, this configuration facilitated a complete colonoscopy analysis pipeline that was evaluated using a variety of classifier architectures (ViT, BiT, FixResNeXt, DeiT, EfficientNet) and segmentation models (U-Net, PSPNet, FPN, LinkNet, MANet).
## 3 Denoising Diffusion Probabilistic Model (DDPM)
### Applications of DDPM in Image Processing
#### 3.3.5 Video Processing  
DDPM's sequential denoising process makes it suitable for video processing. By extending its application to video frames, DDPM can effectively denoise and generate realistic sequences, contributing to tasks such as video enhancement or synthetic video generation [55].
# IoT System for Detecting the Condition of Rotating Machines Based on Acoustic Signals
## 1 Introduction
Many IoT solutions are available in the literature for monitoring parameters of specific machines [21; 22], but, to the best of the authors' knowledge, there are no solutions that detect the condition of machines with different rotary frequencies and characteristic spectral parameters based on acoustic signals and using intelligent PdM methods. In this paper, we propose an IoT device that (i) due to inexpensive hardware is accessible to both large industrial plants and small businesses; (ii) contains an AI state detection algorithm that can easily be adapted to a wide range of rotating machines; (iii) uses acoustic signals and can therefore acquire measurements in a contactless manner; (iv) can be connected to available cloud platforms; and (v) the signals are processed immediately after acquisition on the device, so no external computer is required for decision-making process. In addition, we have improved the existing state detection algorithm and designed the appropriate software for end-users. Compared to the [14; 19], the state detection algorithm is upgraded using GA for optimization of neural network architecture, which makes it easily applicable to a wide range of rotary machines with different rotating frequencies. Compared to available PdM methods that are mainly focused on a specific type of rotary machine, the approach in this paper is of particular interest to all types of rotary machines with a fixed rotating frequency that experience structural changes over time. One type of such machine is a grinding mill, in which the grinding plates are gradually degraded during the grinding process. The proposed solution was tested on several coal grinding mills with fixed rotational frequencies and different dominant spectral components, located in the thermal plant. It showed very good performance in such a real industrial setting with a strong stationary acoustic noise. This paper is structured as follows. In Section 2 the condition detection algorithm implemented on the proposed portable device is described, firstly by describing the two main approaches used for this purpose, namely discrete wavelet transform (DWT) and neural networks (NN). Then the entire algorithm is presented in detail, as well as the software developed for parameter training and hyperparameter tuning using genetic algorithms. The proposed architecture of the system is described in Section 3 with special attention given to the hardware configuration and communication paradigm. Experimental results are presented in Section 4, while Section 5 concludes the paper.
## Materials and Methods
### Characterization  
Scanning electron microscopy (SEM) and SEM-EDX (SEM coupled with energy dispersive X-ray diffraction analysis) were performed using an environmental scanning electron microscope Quanta FEG 200 (FEI France, Thermo Fisher Scientific, Merignac, France), equipped with an Oxford Inca 350 energy dispersive X-ray micro-analyzer (Oxford Instruments France, Saclay, France). FT-IR spectrometry analysis was performed in the range 4000-400 cm\({}^{-1}\) using an FTIR-ATR (attenuated Total Reflectance tool) Bruker VERTEX70 spectrometer (Bruker, Bremen, Germany). The Pd(II)-loaded beads for characterization of the metal-sorbent interaction were prepared by the contact of a Pd(II) solution (pH = 1; V = 20 mL; C\({}_{0}\) = 1.5 mmol Pd L\({}^{-1}\)) with 10 mg of beads for 72 h.