esa-sceva/satcom-chunk-collection huggingface dataset:
# Mechanism and Equivalence of Single Event Effects Induced by 14 MeV Neutrons in High-Speed QDR SRAM
###### Abstract  
Single-bit upset (SBU) and multiple-cell upset (MCU) features of high-speed QDR-SRAM are revealed under the 14 MeV neutron irradiation. By comparing with the high-altitude real atmosphere test results directly, the equivalence of 14 MeV neutrons for atmospheric neutron-induced single event effect (SEE) evaluation is investigated. It is found that, compared with the 65 nm planar device, the SBU cross-section of 14 nm FinFET SRAM decreases to 1/58 and the proportion of MCU shows little difference, which results from the narrow channel between fin and substrate caused by shallow channel isolation in 14 nm FinFET process, and the charge sharing effect between fins is weakened. The SBU and MCU cross-sections under the 14 MeV neutron irradiation are underestimated by 22.8% and 85.7%, respectively. Besides, the probability and maximum size of MCU are both smaller than those in the real atmosphere. The MCU shape tends to be vertical, resulting from the smaller vertical spacing of sensitive volumes (about 100 nm). Further Monte-Carlo simulation shows that the total yield of secondary ions produced by atmospheric neutrons is higher than that produced by 14 MeV neutrons. Major Components of the \"useful\" products are p, Si, \(\alpha\), etc., which are the main cause of SBU events. Besides, compared with 14 MeV neutrons, atmospheric neutrons generate more kinds of secondary ions in the SV within the scope from p to W, and the diverse high-Z elements, such as W, Ta, Hf, etc., are the main cause of MCU events. Moreover, the maximum LET of secondary ions can reach 31.5 MeV\(\cdot\)cm\({}^{2}\)/mg. The equivalence of using 14 MeV neutrons for atmospheric neutron-induced SEE evaluation is closely related to the critical charge of the device under test.  
SEE; SBU; MCU; QDR-SRAM Article  
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# Enhanced Enzymatic Degradability of Livestock Blood Pretreated with Ultrasonic Technique
## 2 Materials and Methods
### Livestock Blood Samples Used in the Study
#### 2.1.2 Physical Properties of Livestock Blood Samples  
The basic characteristics of the Livestock blood samples analyzed in this study are outlined in Table 1. The analysis revealed that the blood samples consisted of 79.18% moisture, 1.29% inorganic matter, and 19.53% organic matter, which was comprised mostly of proteins (accounting for 93.5% of organic matter). The protein content was 18.26%; hemoglobin accounted for 14.08% of the total proteins, constituting the Largest proportion of the protein content. This result is consistent with the report of [PERSON] et al. (2016).  
Since this study is preliminary research exploring the resource utilization of waste Livestock blood to produce amino acid Liquid fertilizer applicable to soil, the salt content in Livestock blood is also an important assessment item. Salt cannot be easily eliminated once introduced into soil and may Lead to the Loss of soil nutrients by interfering with the nutrient and moisture absorption of plants; at high concentrations, it causes plasmolysis and destruction of plant tissue. Therefore, it is crucial to determine the salt concentration of Livestock blood when utilizing it as a fertilizer. The average salt content in Livestock blood was 0.95%, which is below the salt content Limit (1.0%) specified in the fertilizer manufacturing standards in Korea. Therefore, additional preprocessing is not required to remove salts from Livestock blood.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Concentration (wt.\%)** \\ \hline Moisture content & 79.18 \(\pm\) 0.14 \\ Inorganic content & 1.29 \(\pm\) 0.14 \\ Organic content & 19.53 \(\pm\) 0.15 \\ Protein & 18.26 \(\pm\) 0.03 \\ Albumin & 2.03 \(\pm\) 0.08 \\ Globulin & 2.05 \(\pm\) 0.04 \\ Fibrinogen & 0.44 \(\pm\) 0.46 \\ Hemoglobin & 14.08 \(\pm\) 0.07 \\ Lipid & 0.17 \(\pm\) 0.01 \\ Salt & 0.95 \(\pm\) 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of Livestock blood used in this study.
## 5 Results
### The Rare Earth Elements  
The total contents of rare earth elements in pegmatite range from 4.0 \(\times\) 10\({}^{-6}\) to 3.77 \(\times\) 10\({}^{-6}\) with an average of 5.78 \(\times\) 10\({}^{-6}\), the light rare earth elements (LREEs) contents account for 1.98 \(\times\) 10\({}^{-6}\)-12.65 \(\times\) 10\({}^{-6}\) with an average of 5.12 \(\times\) 10\({}^{-6}\), while the heavy rare earth elements (HREEs) contents account for 0.39 \(\times\) 10\({}^{-6}\)-1.12 \(\times\) 10\({}^{-6}\), with an average of 0.66 \(\times\) 10\({}^{-6}\); the ratios of LREEs/ HREEs range from 3.14 to 11.5, suggestive of an obvious differentiation between LREEs and HREEs.  
The chondrite-normalized rare earth element (REE) diagram exhibits a conspicuous right-inclining tendency with strong Eu negative anomalies (Figure 8); however, a slight \"M-type\" tetrad effect still can be seen. This means the pegmatites in the Lijapuzi deposit should have resulted from the high fractional crystallization of magma, and probably experienced strong hydrothermal fluid interaction [31].  
Figure 8: (**Left**) Chondrite normalized REE pattern; (**Right**) Primitive mantle normalized trace elements spidergram; Symbols are the same as Figure 6.  
Figure 7: Harker diagram for the pegmatites in Lijapuzi deposit. Symbols are the same as Figure 6. A-The contents of Al\({}_{2}\)O\({}_{3}\) decrease with the increasing of SiO\({}_{2}\) contents; B- The contents of P\({}_{2}\)O\({}_{5}\) decrease with the increasing of SiO\({}_{2}\) contents; C- The contents of Fe\({}_{2}\)O\({}_{3}\) slightly increase with the increasing of SiO\({}_{2}\) contents; D/E-The contents of K\({}_{2}\)O or Na\({}_{2}\)O show no obvious relationship to those of SiO\({}_{2}\); F-The sums of K\({}_{2}\)O and Na\({}_{2}\)O decrease with the increasing of SiO\({}_{2}\) contents.
# A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques
## 10 Advancing Medical Diagnostic Videos: Limitations and Future Work
### Live Feedback from Experts  
The collection process of ultrasound videos is different from other imaging technologies. The technicians need to not only be experts in ultrasound imaging and related diagnosis but also be masters in capturing videos from standardized views and planes. Therefore, a diagnosis from ultrasound videos is very challenging for physicians at secondary care hospitals. Telemedicine is an approach that combines electronic technology, computer network, modern communication, and medical diagnostic data [89]. If experts can integrate telemedicine with deep learning models for remote consultation of patients, then real-time feedback from experts in the field can help not only the diagnosis [90] but also improve the efficiency of the deep learning models. Training data can be refined based on the medical experts' analysis of the model's performance.
## 3 Results and Analysis
### Microhardness  
The hardness level of aluminum alloys is mainly determined by the strength of precipitate. Figure 9 shows the Vickers micro-hardness values of AA6061-T6 aluminum alloy joints that are welded at 3, 4, and 5 Hz. It is found from Figure 9 that the highest hardness is got in the base metal, while the lowest hardest value occurs in the heat affected zone. The softening in the heat affected zone of AA6061-T6 aluminum alloy is mainly due to the dissolution of strengthened Mg\({}_{2}\)Si precipitates [27; 28]. As shown in Figure 9, the Vickers microhardness of welded joint becomes higher when the thermal frequency is higher. With the thermal frequency increasing from 3 Hz to 5 Hz, the stirring effect of thermal frequency on weld pool is improved, the grain size is refined, and porosity and crack sensitivity decreases. Therefore, the microhardness property of welded joint is improved. According to Hall-Petch relation [21], smaller grain size leads to larger yield strength. As studied in Figure 7, the grain size for 5 Hz is smaller than that of other situations, which leads to higher yield strength, as shown in Figure 10.
###### Abstract  
The slag's chromium leaching is one of the most pressing concerns in Electric Arc Furnace (EAF) carbon and stainless steels production. In recent years, many studies have aimed at understanding which properties (basicity, cooling speed) and phases (spinels, wustite, silicates) determine the Cr leaching, defining different indices (sp-factor, cs-factor) in order to forecast the slag's behavior compared to the leaching of this toxic metal. The literature suggests that spinel formation is usually a good way to fix Cr and prevent its leaching. However, in some conditions (high basicity, low amount of spinel-forming species) soluble Cr-bearing phases can be formed, i.e., Ca-chromite or unstable spinel. In these conditions, Cr can be leached easily, even if it is bound in a spinel structure. In this paper, the effects of basicity and impurities (Ca, Si) on the instability of Cr-spinel was investigated, with respect to slag mesh. The influence of basicity was also studied on Mg-wustite stability, which might contribute to the leaching of Cr. Different samples of carbon steel slag, suspected of forming unstable spinels, belonging to different steel grade production, were also investigated. Both granulated (4 mm) and milled (<100 \(\upmu\)m) slag were analyzed. Scanning Electron Microscopy coupled with Energy Dispersive Spectroscopy (SEM-EDS) analyses were carried out to measure the local chemical composition of Cr-bearing phases. This data was correlated with slag basicity (by X-Ray Fluorescence: XRF), spinel fraction (by X-Ray Diffraction: XRD), and Cr leaching (by Inductively Coupled Plasma Mass Spectrometry: ICP-MS). The main results indicate that the increase of the slag basicity implies an increase of the impurity content (Ca, Si) in the spinel, also over-saturated by Cr. This aspect, coupled with spinel geometrical features, seems to justify the unexpected Cr leaching of some slag samples. Basicity does not influence the chemistry of wustite, thus excluding it as an additional Cr leaching source.  
Electric Arc Furnace slag; Cr leaching; basicity; Cr-spinel; Ca-chromite; Mg-wustite; geometrical features
# Impact of the Elevation Angle on CYGNSS GNSS-R
###### Abstract  
The Earth's surface bistatic reflectivity \(\Gamma_{\text{LHCP},\text{CyGNSS}}\) is experimentally characterized using the novel Global Navigation Satellite Systems Reflectometry (GNSS-R) L-band passive multistatic radar technique from the Cyclone Global Navigation Satellite Systems (CyGNSS) eight-microsatellite constellation. The focus of this study is to evaluate the influence of the GNSS satellites' elevation angle \(\theta_{\text{e}}\) on \(\Gamma_{\text{LHCP},\text{CyGNSS}}\), as a function of soil moisture content (SMC) and effective surface roughness parameter \(\hbar\). As the average response, the change of the scattering regime at a global scale and considering also vegetated surfaces appears at \(\theta_{\text{e}}\approx 55^{\circ}\). This empirical observation is understood as a change on the dominant scattering term, from incoherent to coherent. Then, the correlation of \(\Gamma_{\text{LHCP},\text{CyGNSS}}\) and SMC is evaluated as a function of \(\theta_{\text{e}}\) over specific sparsely vegetated target areas. The smoother the surface, the higher the angular variability of the Pearson correlation coefficients. Over croplands (e.g., Argentinian Pampas), an improved correlation coefficient is achieved over angular ranges where the coherent scattering regime becomes the dominant one. As such, this function depends on the surface roughness. The maximum correlation coefficients are found at different \(\theta_{\text{e}}\) for increasing mean roughness levels: \(\text{r}_{\text{Pampas}}\approx 0.78\) at \(\theta_{\text{e}}\approx[60,70]^{\circ}\), \(\text{r}_{\text{India}}\approx 0.72\) at \(\theta_{\text{e}}\approx[50,60]^{\circ}\), and \(\text{r}_{\text{Sudan}}\approx 0.74\) at \(\theta_{\text{e}}\approx[30,40]^{\circ}\). SMC retrieval algorithms based on GNSS-R multi-angular information could benefit from these findings, so as to improve the accuracy using single-polarized signals.  
GNSS-R; CyGNSS; bistatic reflectivity; elevation angle; coherent and incoherent scattering; Soil Moisture Content (SMC); surface roughness +
Footnote †: journal:  
Article
# The Forecast of the Wind Turbine Generated Power Using Hybrid (LTC + XGBoost) Model
## 3 Dataset  
The dataset was collected from an Enercon E82/2000 wind turbine (maximum generation capacity is 2000 kW) located in the Taurage region, in western Lithuania. Figure 2 shows that the Taurage region is formed by flat plains, with a few hills in the northern part. The average altitude is around 38 m above sea level.  
The dataset represents a detailed time-series record of wind power generation and associated weather parameters collected in Lithuania. The dataset consists of 8760 records corresponding to hourly data collected throughout the year and is organized into 23 columns. Columns include both predicted values, such as weather forecasts, and factual measured values (data obtained from the Lithuanian Hydrometeorological Service), allowing a comparison between expected and real conditions. The factual values of measured meteorological data were calculated as the average of data from nearby automatic meteorological stations (Taurage, Pagegiai, Silute, Silale, Raseiniai, Jurbarkas). LHMT air temperature and wind element data are provided at 2 and 10 m above ground level, respectively.  
Each dataset row corresponds to an hourly timestamp, capturing:  
* _Power generation_: The factual power output (_power_kw_) is recorded, reflecting the efficiency and performance of the wind energy system.
* _Predicted weather parameters_: These include forecasted air temperature, cloud cover, feels-like temperature, relative humidity, sea-level pressure, total precipitation, wind direction, wind gust, and wind speed. Additionally, a condition code (e.g., \"rain,\" \"heavy rain\") summarizes predicted weather.
* _Factual weather measurements_: These are factual weather conditions that were observed and recorded. They match the types of information found in the predicted data, making it possible to check how accurate the forecasts are. This includes real measurements of air temperature, cloud cover, wind speed, wind gusts, wind direction, total precipitation, and more.  
Figure 2: Taurage region elevation map.  
The dataset is composed of both quantitative features, such as temperature, wind speed, and precipitation, and categorical features, such as weather condition codes, providing a diverse range of information for analysis.
## Design of the Secondary Optical Elements for Concentrated Photovoltaic Units with Fresnel Lenses  
**[PERSON] * and [PERSON]**  
Department of Mechanical Engineering, National Central University, Taoyuan 320, Taiwan;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +886-3-4267313.  
Academic Editor: [PERSON]  
_Received: 31 July 2015 / Accepted: 30 September 2015 / Published: 12 October 2015_  
**Abstract:** The goal of this presented study was to determine the optimum parameters of secondary optical elements (SOEs) for concentrated photovoltaic (CPV) units with flat Fresnel lenses. Three types of SOEs are under consideration in the design process, including kaleidoscope with equal optical path design (KOD), kaleidoscope with flat top surface (KFTS), and open-truncated tetrahedral pyramid with specular walls (SP). The function of using a SOE with a Fresnel lens in a CPV unit is to achieve high optical efficiency, low sensitivity to the sun tracking error, and improved uniformity of irradiance distribution on the solar cell. Ray tracing technique was developed to simulate the optical characteristics of the CPV unit with various design parameters of each type of SOE. Finally, an optimum KOD-type SOE was determined by parametric design process. The resulting optical performance of the CPV unit with the optimum SOE was evaluated in both single-wavelength and broadband simulation of solar spectrum.  
**Keywords:** concentrated photovoltaic; secondary optical element; Fresnel Lens; optical efficiency
## 3 Cable Models and Methodology
### Material Properties and Air Boundary  
Each cable model (whether 2D or 3D) is surrounded by a 30 cm thick layer of air in all radial directions, allowing the electromagnetic fields to decay naturally before reaching the simulation boundary. To approximate far-field conditions, the _magnetic vector potential_ (\(A\)) is set to zero on the outer surface of this air region. This boundary condition prevents artificial reflection or confinement of the electromagnetic fields, thereby enhancing the realism of the results.  
In harmonic (AC) analyses of power cables, only the relative permeability (\(\mu_{r}\)) and electrical resistivity (\(\rho\)) of the conductor material (and air) are strictly required to capture frequency-dependent skin and proximity effects. Table 1 provides an overview of the material properties used in all simulations. Copper's high conductivity and air's near-zero conductivity ensure a clear distinction between conductor and non-conductive regions, facilitating accurate finite-element modeling of eddy currents and magnetic field distributions.  
These properties remain fixed across all models for the frequencies tested (0-250 Hz).
# The Application of Microplasma in the Terahertz Field: A Review
## 3 Challenges
### THz Detection by Microplasma  
Previous works have demonstrated the detection methods of THz radiation from the aspects of second-harmonic generation, emission of fluorescence, and acoustic signals from laser-induced microplasma. The qualitative interpretations of the detection principles have been described. On the other hand, [PERSON] et al. proposed a detection method of THz wave by microplasma or microplasma array, where the microplasma is achieved by glow discharge at low pressure. Although the effects of gas pressure, gas composition, and discharge current on the responsivity and stability of the THz detector have been studied, the measurement mechanism of this THz detector remains unclear yet.
## 4 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Surface**} & \multirow{2}{*}{**Layer**} & \multirow{2}{*}{**Position of P**} & \multirow{2}{*}{**Position of V**} & \multirow{2}{*}{**Position of H**} & \multicolumn{6}{c}{**Bond Population of**} & \multicolumn{6}{c}{**Charge (e) of**} \\ \cline{6-13}  & & & & & **P-H** & & & & **P-C** & & & **P** & **H** \\ \hline \multirow{4}{*}{(001)} & 2 & A & B & BC1 & \(-\)0.07 & 0.90 & 0.76 & 0.62 & 0.51 & 0.55 & 0.57 & 0.27 & 0.03 \\  & 3 & B & B & BC1 & \(-\)0.12 & 0.98 & 0.71 & 0.59 & 0.65 & 0.66 & 0.53 & 0.28 & 0.01 \\  & 4 & B & A & BC3 & \(-\)0.12 & 0.98 & 0.71 & 0.59 & 0.65 & 0.65 & 0.53 & 0.28 & 0.01 \\  & 5 & A & A & AB2 & \(-\)0.09 & 1.06 & 0.70 & 0.65 & 0.65 & 0.60 & 0.65 & 0.27 & \(-\)0.01 \\  & 6 & A & C & V & \(-\)0.08 & 1.05 & 0.69 & 0.65 & 0.64 & 0.65 & 0.61 & 0.27 & \(-\)0.00 \\ \hline \multirow{4}{*}{(111)} & 2 & A & A & BC1 & \(-\)0.13 & 1.06 & 0.75 & 0.61 & 0.61 & 0.65 & 0.64 & 0.28 & 0.01 \\  & 3 & A & A & BC3 & \(-\)0.13 & 1.06 & 0.75 & 0.61 & 0.61 & 0.65 & 0.64 & 0.28 & 0.01 \\ \cline{1-1}  & 4 & A & B & AB3 & \(-\)0.05 & 0.97 & 0.71 & 0.64 & 0.62 & 0.61 & 0.59 & 0.27 & \(-\)0.00 \\ \cline{1-1}  & 5 & A & B & BC3 & \(-\)0.08 & 1.04 & 0.69 & 0.64 & 0.63 & 0.62 & 0.59 & 0.27 & \(-\)0.00 \\ \cline{1-1}  & 6 & A & B & AB2 & \(-\)0.07 & 1.04 & 0.68 & 0.64 & 0.65 & 0.63 & 0.60 & 0.27 & \(-\)0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Atomic charges and electron bond population of the PVH complex with the lowest formation energy in each layer. **Author Contributions:** S.N. and W.S. contributed equally to this work. Conceptualization, S.S. and H.L. ; methodology, S.N., W.S. and Y.P. ; validation, S.N. and W.S. ; formal analysis, S.N., W.S. and Y.S. ; investigation, S.N. and W.S. ; resources, S.S. and H.L. ; data curation, S.N. ; writing--original draft preparation, S.N. ; writing--review and editing, Y.C. and H.Q. ; visualization, S.N. and W.S. ; su-pervision, S.S. and H.L. ; project administration, H.L. ; funding acquisition, W.S., S.S. and H.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (No. 51727901), the China Scholarship Council (No. 201906270142), the International Cooperation Research Project of Shenzhen (No. GJHZ20180413182004161), the Hubei Provincial Natural Science Foundation of China under Grant (No. 2020 CFA032) and the Guangdong Basic and Applied Basic Research Fund: Guangdong-Shenzhen Joint Fund (No. 2020B1515120005). The data used to support the findings of this study are available from the corresponding authors upon request. The authors declare no conflict of interest.
## 2 Theoretical Framework
### Uncertainty in Component Reliability Data  
#### 2.2.1 Component Failure Rate  
A component's failure rate varies over its operational lifespan, following three distinct periods: initial development, exploitation, and aging (Figure 1). In the early stage, latent defects from design, transportation, assembly, or installation may lead to failures. Once these issues are resolved, the failure rate stabilizes and remains approximately constant for a significant period. However, as the component ages, failures become more frequent, marking the onset of the final phase, where the failure rate progressively increases. The technical service life ends when the component is irreparable or when its maintenance and repair costs approach the expense of acquiring a replacement [42].  
This study takes into account that certain components within the network have reached the aging period. Their age will be reflected in an increased failure rate, and potential network reliability improvements will be explored by assessing the feasibility of replacing these old components with new ones.  
The Weibull distribution is widely used in reliability engineering and failure rate analysis due to its flexibility in modeling the failure characteristics of electrical power system components. The failure rate is generated as a Weibull-distributed random variate [4; 42]. Considering the failure rate characteristic, typical shapes of the Weibull distribution can be observed in each lifetime period by adjusting the shape parameter (\(\beta\)): \(\beta<1\) models the development period, \(\beta=1\) models the exploitation period, and \(\beta>1\) models the aging period. In this study, \(\beta=1\) is used for components in the exploitation period, and \(\beta=4\) is used for components in the aging period [42]. The failure rate value will be varied within predefined ranges, depending on the life phase of each specific component, and this uncertainty will be incorporated into the MCS.  
Figure 1: Variation in failure rate over the component’s lifespan.