esa-sceva/satcom-chunk-collection huggingface dataset:
# Centralized MPPT Control Architecture for Photovoltaic Systems Using LoRa Technology
## 2 Centralized MPPT Architecture Based on LoRa
### LoRa/LoRaWAN Technology  
LoRa and LoRaWAN are emerging technologies for Internet of Things (IoT) systems that operate in the sub-GHz band. The exact frequency depends on the geographical region. For instance, Europe uses 868 MHz, North America uses 915 MHz, and Asia uses 433 MHz. LoRa technology shares common objectives with other wireless technologies such as low-power communication and applications geared toward the IoT, but it distinguishes itself through differences in modulation technique and frequency bands [43]. LoRa achieves an extended range, immunity to interference, and lower power consumption, albeit at the expense of lower data rates.  
LoRa, as a proprietary RF modulation technology designed for low-power, wide-area networks (LPWANs), claims a range of 5 km in urban areas and 15 km or more in rural areas. It caters to applications requiring long-range or deep in-building communication among numerous low-power devices collecting small amounts of data [44]. LoRa consists of the physical layer of communications while LoRaWAN extends to Layers 2 and 3. Only using the LoRa physical layer reduces latency and jitter.  
LoRa employs a proprietary modulation technique based on Chirp Spread Spectrum (CSS) technology; it offers a compromise between sensitivity and data rate depending on the configuration parameters. In contrast to traditional Direct-Sequence-Spread Spectrum (DSSSS) systems, LoRa CSS technology achieves spectrum spreading by producing a so-called chirp signal with a variable frequency. This approach ensures that the timing and the frequency offset between the transmitter and the receiver are equivalent, significantly reducing the complexity of the receiver's design [45]. The spreading factor (SF) determines the amount of code used for the spectrum spreading of the original signal. SF is a critical parameter in LoRa modulation, with values from SF7 to SF12. The selection of the spreading factor influences the signal's travel distance and the reception reliability: a larger spreading factor enables longer distances at the expense of a decrease in data rate [46].  
In the context of this project, LoRa modules were chosen for several compelling reasons: (1) Reliability, durability, and affordability; (2) Operation at a sub-GHz band, particularly Europe's 868 MHz; (3) Exceptional flexibility for configuration in various topologies and scenarios (point-to-point, star, and star of stars); (4) The capability, when coupled with high-gain antennas, to extend coverage over substantial distances that span several kilometers.
## 3 Materials and Methods
These LUS measurements were carried out in a similar manner as described in [20; 21; 22; 23], whereby the third-order frequency-dependent attenuation is correlated with the austenite grain size to achieve a temperature-dependent calibration curve used to calculate the average grain size from the LUS measurements in austenitic samples. The typical GLUS measurement procedure is displayed in Figure 3a-c, where in Figure 3a, the ultrasound was generated in the hot sample via ablation on the surface with a \(\sim\)150 mJ generation laser with 5.5 ns long pulses at a 532 nm wavelength, which was operated at 1-20 Hz depending on the duration of the GLUS measurement. The ultrasonic signal response from the sample was measured with a TECNAR detection laser system and interferometer, as illustrated in Figure 3b, wherein the first longitudinal wave looking like a parabola reaches the surface after being reflected at the bottom. The signal from the TECNAR interferometer was sampled with a 1.6 GS data acquisition card resulting in signals similar to those displayed in Figure 3c, which displays the generation event and the consecutive backwall echoes. After the annealing program and the data collection, the grain size was calculated for the measurements of interest, serving as input for the following analysis. The data for the calibration curve used herein came from measurements conducted on stainless-steel samples, annealed to various grain sizes, and determined ex situ from orien  
Figure 3: **(a)** Image of a 10 cm long sample during annealing and in situ laser ultrasonic measurement with the Gleeble. The ablation that generates the ultrasound is visible as the white and green plume on the top surface. The sample is in vacuum inside the Gleeble, which is why the ablation plume is quite extensive. The two wires on the bottom side of the sample are the thermoelement. **(b)** Illustration of the ultrasonic wave as the first backwall echo reaches the surface where the detection laser is measuring the surface vibration. **(c)** Typical laser ultrasonic A-scan showing the generation event and the consecutive backwall echoes for the longitudinal wave; the signal is bandpass-filtered with a 3–80 MHz Butterworth filter. tation maps measured via electron backscatter diffraction. Some stainless-steel specimens were also measured with the GLUS at room temperature for the calibration curve. The HSLA materials were annealed and quenched in the Gleeble similarly to the stainless-steel samples, after which the orientation maps of the austenite microstructure were measured and the grain size was determined with the parent austenite reconstruction method described above, both with and without annealing twin boundaries. Three commercial high-strength low-alloy steels, one high-vanadium tool steel, one high-silicon quenching and partitioning material, and two experimental compositions were selected for the studies based on their commercial importance, compositional differences, and in the case of the expected difference in the precipitate density of the tool steel.
## 5 Results
### Vision-Based Versus Prompt-Based Approach: Results
#### 5.2.3 Efficiency  
As shown in Figure 6c, questions 5 through 6 asked about assessed efficiency. For Q5, participants showed no statistically significant difference between the two approaches (\(Z=-1.08\), \(p=0.28\)), with vision-based (\(M=4.75\), \(SD=1.54\)) and prompt-based (\(M=5.25\), \(SD=0.87\)) approaches being relatively similar. For Q6, participants showed no statistically significant difference between the two approaches (\(Z=-0.72\), \(p=0.47\)), with vision-based (\(M=5.17\), \(SD=1.64\)) and prompt-based (\(M=5.41\), \(SD=1.24\)) approaches being relatively similar.
# On-Board Real-Time Ship Detection in HISEA-1 SAR Images Based on CFAR and Lightweight Deep Learning
###### Abstract  
Synthetic aperture radar (SAR) satellites produce large quantities of remote sensing images that are unaffected by weather conditions and, therefore, widely used in marine surveillance. However, because of the hysteresis of satellite-ground communication and the massive quantity of remote sensing images, rapid analysis is not possible and real-time information for emergency situations is restricted. To solve this problem, this paper proposes an on-board ship detection scheme that is based on the traditional constant false alarm rate (CFAR) method and lightweight deep learning. This scheme can be used by the SAR satellite on-board computing platform to achieve near real-time image processing and data transmission. First, we use CFAR to conduct the initial ship detection and then apply the You Only Look Once version 4 (YOLOv4) method to obtain more accurate final results. We built a ground verification system to assess the feasibility of our scheme. With the help of the embedded Graphic Processing Unit (GPU) with high integration, our method achieved 85.9% precision for the experimental data, and the experimental results showed that the processing time was nearly half that required by traditional methods.  
ship detection; lightweight deep learning; CFAR algorithm; on-board processing Article  
On-Board Real-Time Ship Detection in HISEA-1 SAR Images Based on CFAR and Lightweight Deep Learning  
1,2,3,*  
* 2021
# Internal Backpressure for Onboard Crospoint-Buffered Switches with Port Multiplexing
## 3 Design of Internal BP Architecture  
### System Model  
The system model is composed of the Shared Memory Input Module (SIM), Crosspoint Buffer Module (CBM), and Output Arbitration Module (OAM). The input end employs VOQs to prevent queuing blocking, with each SIM being uniquely associated with a respective OAM. Figure 3 illustrates the internal BP for onboard crosspoint-buffered switches with a port multiplexing module of an \(N\times N\) switching structure, where \(N\) denotes the dimension of the switch. The system architecture is stratified into three hierarchical levels: the input level, intermediate level and output level.  
The input level employs traffic multiplexing and shared memory technologies to enhance the efficacy of buffer utilization. Each SIM module is capable of multiplexing \(k\) input ports onto a single internal input bus (IB), where \(k\) represents the number of multiplexing units defined by the system configuration. At this point, the data transmission rate of this bus is increased by \(k\) times compared to the rate of a single input link. For the clarity of identification, we label the \(n\)th SIM module as SIM(\(n\)), and the \(x\)th input port within is denoted as IP(\(n\),\(x\)), where \(x\) ranges from 0 to \(k\)-1. At the intermediate level, the CBM serves as a interconnection between SIM and OAM, facilitating efficient data transmission through the Crosspoint Queue, CQ(\(n\),\(m\)). Within the CBM, there are a total of \((N/k)^{2}\) queues of this type. Data can only be scheduled from SIM(\(n\)) to OAM(\(m\)) when  
Figure 3: Internal BP for onboard crosspoint-buffered switches with port multiplexing module.  
there is free space available in the target CQ(\(n\),\(m\)). This process essentially simplifies into a precise matching between input and output ports [5]. At the output level, the OAM is tasked with the demultiplexing of packets from the internal output bus (OB), routing them to various output ports based on the addresses of destination. In an \(N\times N\) architecture, the system is equipped with \(N/k\) OAM modules, and the \(x\)th output port on the \(m\)th OAM is labeled as OP(\(m\), \(x\)). Each OP has an output queue used for buffering packets that are about to be output.The related symbols are shown in Table 1.
# Infrared Multi-Scale Small-Target Detection Algorithm Based on Feature Pyramid Network
## 2 Materials and Methods
### Pyramid Segmentation-Connection Module  
The pyramid segmentation-connection module takes the feature map \(X\) obtained from feature extraction as its input and processes it to produce the output feature map \(Z\). The detailed structure of PSC is shown in Figure 2. PSC feeds the feature map \(X\) into multiple scales of CSE, and the scale is denoted as \(b\in\{b_{1},b_{2},\ldots,b_{n}\}\). For each scale, CSE preserves the essential details of small targets by incorporating contextual information, as illustrated in Figure 3, depicting the structural diagram of the CSE model. It can be seen in the figure that CSE is divided into two parallel processing flows, namely, LSS and GSR. The feature maps processed by LSS and GSR are convolved, and then the convolved feature maps are added to \(X\) to obtain the final output \(M\). Subsequently, \(M\) and \(X\) obtained at different scales are concatenated together. Finally, the information from multiple scales is integrated, and the feature map \(Z\) is obtained through \(1\times 1\) convolution. As can be seen from the model architecture of infrared multi-scale small-target detection in Figure 1, the PSC model has been processed three times throughout the entire process. Although the size of the input feature map \(X\) varies across different stages, the selected scale \(b\) remains consistent.
## Mercury Removal Methods
### Adsorption
In addition, acidic gases in flue gas, such as HCl, H\({}_{2}\)SO\({}_{4}\), NO\({}_{2}\), have a promoting effect on the mercury removal using fly ash [94]. To further improve the mercury removal efficiency, researchers have conducted studies on the modification of fly ash. [PERSON] al. [95] achieved a high mercury removal efficiency by treating fly ash with CuBr\({}_{2}\), CuCl\({}_{2}\), and FeCl\({}_{3}\). [PERSON] et al. [96] modified fly ash with calcium oxide and found that when the mass ratio of calcium oxide was 33%, the modified fly ash achieved the highest mercury removal efficiency. Noble metals such as palladium (Pd), rhodium (Rh), iridium (Ir), gold (Au), and silver (Ag) have the potential to remove mercury. [PERSON] et al. [97] prepared Pb-Al\({}_{2}\)O\({}_{3}\) adsorbents via the impregnation method and tested their Hg\({}^{0}\) removal efficiency at high temperatures. This mercury removal ability should be mostly attributed to the formation of a Pb-Hg amalgam on its surface. Gold is highly stable and an Au-Hg amalgam is also difficult to decompose at high temperatures. Researchers added Au and Cl to activated carbon to prepare high-performance mercury adsorbents [98]. [PERSON] et al. [99] loaded nano-Ag particles on small glass beads for the removal of elemental mercury (Figure 8). The results showed that this material had the highest mercury removal efficiency at 60 \({}^{\circ}\)C, while mercury desorbed from the adsorbent at 245 \({}^{\circ}\)C.  
In fact, the key to mercury removal via adsorption is to convert gaseous mercury into particulate mercury. The development of high-efficiency and low-cost mercury adsorbents is essential to ensure the widespread application of this method. The ultimate goal of all the related research on mercury adsorbents is to reduce the operational costs of the adsorbents. However, the high cost of modified activated carbon and modified noble metal materials limits their effectiveness in reducing the operational costs. Additionally, the efficiency of ore-based adsorbents and fly ash for mercury removal is relatively low and susceptible to various factors, especially the chlorine content, which directly determines the actual mercury removal efficiency of these adsorbents. Therefore, the search for high-efficiency, stable, and relatively low-cost mercury adsorbents remains the main bottleneck restricting the application of adsorption-based mercury removal technology.
# WCET Analysis Based on Micro-Architecture Modeling for Embedded System Security
## 5 Results and Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Module** & **Parameter** & **Description** \\ \hline \multirow{6}{*}{Cache} & \multirow{6}{*}{-cache:ill i1:768:64:3:l} & L1 instruction cache that is a \\  & & 48 KB 3-way set-associative \\  & & & cache with a 64-byte cache \\  & & & line; LRU replacement policies. \\ \cline{2-3}  & & & L1 instruction cache that is a \\  & & & 1 MB 16-way set-associative \\  & & & cache with a 64-byte cache \\  & & & line; LRU replacement policies. \\ \cline{2-3}  & & & \\ \cline{2-3}  & & & \\ \cline{2-3}  & & & \\ \hline \multirow{6}{*}{Branch Prediction} & \multirow{6}{*}{-bpred:2 lev 1 1024 4 1} & Size of first level entry is 1; \\  & & & size of second level entry is 1024; Width of BHR is 4; \\  & & & Support branch history. \\ \cline{2-3}  & & & Branch mis-prediction latency \\  & & & is 15 cycles \\ \hline \multirow{6}{*}{Instruction Pipeline} & \multirow{6}{*}{-fetch:ifqsize 4} & Instruction prefetch queue \\  & & & size is 4. \\ \cline{2-3}  & & & Instruction decode width is \\  & & & 4 insets/cycle. \\ \cline{2-3}  & & & Instruction transmission \\ \cline{2-3}  & & & width is 4 insets/cycle. \\ \cline{2-3}  & & & Instruction commission \\ \cline{2-3}  & & & width is 4 insets/cycle. \\ \cline{2-3}  & & & Run pipeline with in-order \\ \cline{2-3}  & & & issue. \\ \cline{2-3}  & & & issue instructions down \\ \cline{2-3}  & & & wrong execution paths \\ \hline \multirow{6}{*}{Others} & \multirow{6}{*}{-ruux:size 128} & Size of register update unit \\  & & & is 128. \\ \cline{1-1} \cline{2-3}  & & & \\ \cline{1-1} \cline{2-3}  & & & \\ \cline{1-1} \cline{2-3}  & & & \\ \cline{1-1} \cline{2-3}  & & & \\ \cline{1-1} \cline{2-3}  & & & \\ \cline{1-1} \cline{2-3}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Processor configuration. However, some estimated WCET values for benchmarks are up to 2-3 times larger than the observed WCET. There may be two reasons for these differences. First, our micro-architecture modeling may not be detailed enough. For instance, features like data caches and Translation Lookaside Buffers (TLBs) supported by modern processors are not included in our model, leading to a lack of constraints. Second, the differences may be attributed to the pessimistic nature of the static analysis method.
## 4 Results and Discussion
### Irregular Waves  
For irregular wave conditions, the P-M spectrum was used and the analysis was performed in the frequency domain. The curves in Figures 12-14 show the magnitudes of each motion component. Representative response amplitudes are summarized in Table 6.  
From the results in the frequency domain, it can be found that the surge has an obvious low-frequency response peak value, and at this frequency the magnitudes of the three different platforms are significantly different, as shown in Figure 12. By comparing the three load conditions LC4-LC6, it can be further found that as the irregular wave height and the period increase, the amplitude of the responses also decreases. Importantly, the subsequent high-frequency response shows a large range. As the number of offset columns increases, the structure will peak at almost the same frequency with gradually decreased amplitudes. When the number of offset columns is 6, the decrease in the surge amplitudes is the most obvious, reaching nearly 70% that of Model0.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Load Conditions** & **Resposes** & **3 Columns** & **4 Columns** & **6 Columns** \\ \hline \multirow{3}{*}{LC4} & Surge (m) & 1.391 & 1.159 & 0.917 \\  & Heave (m) & 0.055 & 0.043 & 0.043 \\  & Pitch (deg) & 0.065 & 0.047 & 0.035 \\ \hline \multirow{3}{*}{LC5} & Surge (m) & 1.324 & 1.096 & 0.847 \\  & Heave (m) & 0.179 & 0.168 & 0.158 \\  & Pitch (deg) & 0.057 & 0.039 & 0.031 \\ \hline \multirow{3}{*}{LC6} & Surge (m) & 1.266 & 1.047 & 0.796 \\  & Heave (m) & 0.367 & 0.367 & 0.367 \\ \cline{1-1}  & Pitch (deg) & 0.033 & 0.029 & 0.028 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Statistics of the motions in the frequency domain for LC4–LC6.  
Figure 12: _Cont._  
As plotted in Figure 13, there is no obvious low-frequency peak similar to the surge response in the spectra of the heave responses. Comparing the three loads conditions LC4-LC6, the significant influence on the heave response is from the height and period of the wave, although the amplitude is small. In addition, different from the surge motion, there are two peaks in the whole frequency range, while the representational area is continuous, and it is difficult to accurately distinguish them.  
The trend of the response amplitude of the pitch motion shown in Figure 14 is similar with the other two responses described above. As can be further found from the spectra, the response amplitude shows no significant peak. Comparing the results of the heave, surge, and pitch responses, it can be found that the peak of the low frequency response appears at different frequencies, the former is about 100 times higher than the other two.  
Figure 12: Surge response spectrum for the three platforms under wave loadings (**a**) LC4, (**b**) LC5, and (**c**) LC6.  
Figure 13: Heave response spectrum for the three platforms under wave loadings (**a**) LC4, (**b**) LC5, and (**c**) LC6.  
0.058 Hz, the latter two are nearly 0.01 Hz, indicating that the coupling between the heave response and the surge or pitch responses is weak.
# Design and Optimization of Non-Coplanar Orbits for Orbital Photovoltaic Panel Cleaning Robots
## 2 Construction of Models
### Effectiveness Evaluation and Analysis
#### 3.4.2 Curvature  
The curvature of the pre-optimized and post-optimized Bezier curves is presented in Table 4 below. The results indicate that the maximum curvature of the optimized Bezier curve exceeds that of the pre-optimized Bezier curve while still satisfying the curvature threshold. This suggests that the optimized Bezier curve aligns more closely with the actual trajectory data. Additionally, the minimum curvature of the optimized Bezier curve is less  
Figure 9: Modeling and simulation: (**A**) the non-coplanar track 3D model that connects the PV panels; (**B**) the spanning motion phase: (**a**) incompletely driving into the orbit; (**b**) completely driving into the orbit; (**c**) incompletely driving out of the orbit.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Bezier Curve** & **Standard Deviation** & **Optimization Percentage** \\ \hline Unoptimized curve 1 & 0.8197 & \\ Optimized curve 1 & 0.5276 & 35.63\% \\ \hline Unoptimized curve 2 & 0.7484 & \\ Optimized curve 2 & 0.4448 & 40.57\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: The standard deviation of the distance for The Bezier curves.  
than that of the pre-optimized Bezier curve, indicating that the optimized Bezier curve is smoother.
# A Geostationary Instrument Simulator for Aerosol Observing System Simulation Experiments
###### Abstract  
In the near future, there will be several new instruments measuring atmospheric composition from geostationary orbit over North America, East Asia, and Europe. This constellation of satellites will provide high resolution, time resolved measurements of trace gases and aerosols for monitoring air quality and tracking pollution sources. This paper describes a detailed, fast, and accurate (less than 1.0% uncertainty) method for calculating synthetic top of the atmosphere (TOA) radiances from a global simulation with a mesoscale free running model, the GEOS-5 Nature Run, for remote sensing instruments in geostationary orbit that measure in the ultraviolet-visible spectral range (UV-Vis). Generating these synthetic observations is the first step of an Observing System Simulation Experiment (OSSE), a framework for evaluating the impact of a new observation or algorithm. This paper provides details of the model sampling, aerosol and cloud optical properties, surface reflectance modeling, Rayleigh scattering calculations, and a discussion of the uncertainties of the simulated TOA radiance. An application for the simulated TOA radiance observations is demonstrated in the manuscript. Simulated TEMPO (Tropospheric Emissions: Monitoring of Pollution) and GOES-R (Geostationary Operational Environmental Satellites) observations were used to show how observations from the two instruments could be combined to facilitate aerosol type discrimination. The results demonstrate the viability of a detailed instrument simulator for radiance measurements in the UV-Vis that is capable of accurately simulating high resolution, time-resolved measurements with reasonable computational efficiency.  
observing system simulation experiment; instrument simulator; radiative transfer Article  
A Geostationary Instrument Simulator for Aerosol Observing System Simulation Experiments