esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Adipose Tissue
* PDD distribution: Figure 3 shows the Geant4-simulated percentage depth dose distributions for the five studied tissues for a \(10\times 10\) cm\({}^{2}\) field size for a 6 MV photon beam. The PDD distribution is a function of depth, field size and SSD and can be characterized by the surface dose (d0(%)), depth of maximum dose (dmax(cm)) 80% dose (d80%(cm)), and dose at depth of 10 cm (d10 cm(%)) and 20 cm (d20 cm(%)). We can see a fast increase followed by a slower decrease after reaching the maximum photon absorption. The first region is caused by the backscattering dominance, whereas the second region simply describes the exponential attenuation of photons through matter;
* Photon energy effect: The incident photon energy dependence of adipose tissue BUF for fixed penetrations of 0.5, 5, 15, 25 and 40 mfp is shown in Figure 4 (left side). For each penetration depth, BUF continuously decreased as a function of energy. In order to understand such phenomena, the studied energy interval can be divided into two subintervals: from 0.15 to 1 MeV corresponding to the Compton process domination and from 1 to 15 MeV, where the pair production effect is dominant. The large value of the BUF at the beginning of each curve can be explained by the multiple Coulomb scattering, which only attenuates and does not totally absorb the photon energy, existing for a longer time in the medium. Similarly to the photoelectric effect, resulting in lower BUF values due to the total energy absorption, the second energy interval is mainly dominated by the pair production process;
* Penetration depth effect: Figure 4 (right side) shows the dependence of BUF on penetration depth dependence for fixed photon energies of 0.2, 0.5, 1, 1.5, 5, 10 and 15 MeV for adipose tissue. Generally, increased BUF values for deeper penetrations were observed. However, the increasing rate of BUF (comparing 0.5 to 40 mfp range edges) is inversely proportional to photon energies. Additionally, we can see the contribution effect of secondary photons to BUF due to the multiple scattering effect, which reaches a maximum at 1 MeV;
* BUF(Material)/BUF(Adipose)) in Figure 5, we have: BUF(AD2) > BUF(AD1) > BUF(Adipose) > BUF(AD3), and from Table 1, we have \(Z_{eq}(\text{AD2})<Z_{eq}(\text{AD1})<Z_{eq}(\text{Adipose})<Z_{eq}(\text{AD3})\), which confirms the theoretical hypothesis that BUF and \(Z_{eq}\) are inversely proportional;
* TEM study: Based on the relative difference between the radiological properties of tissues and mimicking material candidates in % for BUF, \(Z_{eq}\), \(\mu/\rho\), \(\mu_{en}/\rho\), \(Z_{eff}\), \(N_{eff}\), d0 and dmax and in cm for d80, d10 cm and d20 cm listed in Table 3 and plotted in Figure 5, we can see the close similarity of the AD1 candidate to adipose tissue. Moreover, the substitute AD2 seems to be closer to adipose tissue when looking for other parameters than BUF and \(Z_{eq}\). However, the discrepancy of AD3 compared to adipose tissue was seen for many parameters. Therefore, AD1 can be an acceptable TMM for adipose for the actual photon energy range.
# Development of a Spatial Heterodyne Terahertz Raman Spectrometer with Echelle Gratings
## 3 Experiment
### Medicines Measurement Results  
In addition to food additives, we also tested medicines. Figure 5a shows the terahertz Raman spectrum of furantoin, in which the characteristic peaks of the sample at 61.77 cm\({}^{-1}\) (1.85 THz) and 79.59 cm\({}^{-1}\) (2.3877 THz) can be identified clearly. The sample is a class II biological agent and is mainly used to treat urinary system infections. Tylenol is used to treat colds, fevers, and postoperative analgesia, and Figure 5b shows the terahertz Raman spectrum of Tylenol, where the measured results are almost entirely consistent with the theoretical values [21]. Indomethacin is used for the treatment of arthritis and soft tissue injuries. Detection of the terahertz Raman band of indomethacin can help to determine the crystallinity of the sample [22], and its terahertz Raman spectrum is shown in Figure 5c. Ibuprofen is an antipyretic and analgesic nonsteroidal anti-inflammatory drug, and its terahertz Raman spectrum is shown in Figure 5d. We measured the characteristic peaks of this spectrum at 21 cm\({}^{-1}\) (0.63 THz), 52 cm\({}^{-1}\) (1.56 THz), 74 cm\({}^{-1}\) (2.22 THz), and 138 cm\({}^{-1}\) (4.14 THz). Detection of the terahertz Raman band of ibuprofen samples can enable characterization of the internal vibration mode of these molecules [23].  
We also acquired terahertz Raman spectra for some other tablets; for example, Figure 5e shows the terahertz Raman spectrum of a cold medicine tablet, and Figure 5f shows the terahertz Raman spectrum of a painkiller tablet.  
After that, we mixed samples with flour with a 1:1 ratio. Figure 5g,h show the results for theophylline and carbamazepine when mixed with flour, respectively. The figures show that the intensities of the characteristic peaks in the spectral curves for the mixed samples (pink curves) have been weakened to a certain extent, but the locations of these characteristic peaks can still be identified clearly. In addition, the sample composition can be determined based on these characteristic peaks. This indicates that when observed under the same objective conditions, the sample purity is positively correlated with the spectral intensity, in line with the Lambert-Beer law [24].  
Figure 4: (**a**) Terahertz Raman spectrum of DL-leucine; (**b**) terahertz Raman spectrum of DL-tartaric acid; **(c)** terahertz Raman spectrum of DL-threonine; (**d**) terahertz Raman spectrum of L-malic acid; (**e**) terahertz Raman spectrum of fumaric acid; and (**f**) terahertz Raman spectrum of glycine.  
Figure 5: (**a**) Terahertz Raman spectrum of furantion; (**b**) terahertz Raman spectrum of Tylenol; (**c**) terahertz Raman spectrum of indomethacin; (**d**) terahertz Raman spectrum of ibuprofen; (**e**) terahertz Raman spectrum of cold medicine tablet; (**f**) terahertz Raman spectrum of painkiller tablet; (**g**) comparison of terahertz Raman spectra of theophylline; (**h**) comparison of Terahertz Raman spectra of carbamazepine.
## 4 Case Study
### Results and Discussion  
#### 4.2.1 Number of Neurons in the Hidden Layer  
The optimum number of neurons in the hidden layer \(n_{1}\) was roughly estimated using Equation (14), where the value of \(m\) is 21 and the value of \(n\) is 1. Accordingly, the value of \(n_{1}\) may range from 5 to 15. Furthermore, 30 cases, where the number of neurons in the hidden layer of a BPNN was set from 1 to 30, were run to explore the exact value of \(n_{1}\) of an optimum BPNN. In each case, 500 BPNNs were trained, and the average value of \(RMSE\), as well as the average computational time for training a BPNN, were illustrated in Figure 6. The results show that the value of \(RMSE\) generally decreased as the number of neurons in the hidden layer increased from 1 to 15, while the value of \(RMSE\) fluctuated when such a number increased from 15 to 30. Therefore, the number of neurons in the hidden layer of the BPNN with the highest prediction accuracy in the proposed case study is 15, and the \(RMSE\) of the prediction result is 1.43 mm. Additionally, the average computational time for training a BPNN shows a trend of wavy increase with an increase in the number of neurons in the hidden layer of a BPNN.
## 3 Results
### Cost vs. Area vs. Energy Trade-Off Analysis  
To further demonstrate the optimization potential, a parametric evaluation was conducted linking the total system cost to the number of PV panels (area coverage) and energy produced. Table 3 shows the variation in the system metrics based on panel count.  
As can be seen, increasing the panel count linearly scaled the area and energy but non-linearly affected the cost-efficiency trade-off. The optimal solution would balance these factors based on irrigation demand and land availability.
# Application of Machine Learning in the Identification and Prediction of Maritime Accident Factors
###### Abstract  
Artificial intelligence seems to be a new point of view to classical problems that, in the past, could not be understood in depth, leaving certain gaps in each knowledge area. As an example of this, maritime accidents are one of the most recognised international problems, with clear environmental and human life consequences. From the beginning, statistical studies have shown that not only the typical sampled variables must be considered but the accidents are related to human factors that, at the same time, are related to some variables like fatigue that cannot be easily sampled. In this research work, the use of machine learning algorithms on over 300 maritime accidents is proposed to identify the relationship between human factors and the main variables. The results showed that compliance with the minimum crew members and ship length are the two most relevant variables related to each accident for the Spanish Search and Rescue (SAR) region, as well as the characteristics of the ships. These accidents could be understood as three main groups of accidents related to the general tendency to not meet the minimum number of crew members and its difference in the year of construction of the ship. Finally, it was possible to use neural networks to model accidents with sufficient accuracy (determination factor higher than 0.60), which is particularly interesting in the context of a control system for maritime transport.  
maritime accidents; neural networks; clustering; random forest; human factor Article Application of Machine Learning in the Identification and Prediction of Maritime Accident Factors.
# Adaptive Impedance-Based Fault Location Algorithm for Active Distribution Networks
## 6 Discussion  
The fault location method proposed in this paper showed to be robust for the validation scenarios. The effect of the fault resistance, random variation of the load, level of penetration of dispersed generation sources, and the location of the fault, were analyzed.  
The performance of the method is adequate, with errors lower than 1.1%. The factor that produces the highest error is the fault resistance. This effect is significantly intensified with the type of fault; the single-phase faults the ones that affect the location accuracy the most. This is expected, since being shown previously [35], with the increase of the fault resistance, the impedance seen by the impedance-based fault location methods tends to be greater than the real impedance of the power system.  
However, the factor that produces a greater location error is the DER penetration level: the greater the DER penetration level, the greater the error. However, the error decreases if the IED information is available. Nevertheless, when IED information is not available, the DER electrical model is used, and an iterative process is made to determine the DER contribution to the fault current. This process is less accurate than using the current measured by the IEDs in the DER and the accuracy is affected by the location of the fault.  
On the other hand, the load variation also showed an effect on the performance of the proposed method. This is expected for two reasons: first, the proposed method is as impedance-based method; therefore, any variation in the load will affect the impedance of the power system. The second reason is that the power system impedance variation due to the load variation must be compensated in the fault  
Figure 17: Performance curve for the proposed method and comparison with [PERSON] et al.’s [16; 17] and [PERSON] et al.’s [21] methods. Single-phase faults with resistance of 50 \(\Omega\).  
location formulation. If a compensation strategy is not used, a significant error on the estimation of the fault distance will be introduced. For the proposed method, a compensation strategy was not used, which produces an increase in the error with a behavior depending on the load variation features.
## 6 Discussion and Future Challenges  
The electromagnetic environment is becoming increasingly complex, and the vital role of its effect evaluation research has become prominent. The study of objective electromagnetic environmental effects takes the characteristics of the four-domain electromagnetic environmental signals as the research object. There are also a small number of relevant studies that reflect on the interference threat posed by the electromagnetic environment's complexity on equipment. It inspires us to evaluate the electromagnetic environmental complexity from the perspective of individual equipment, allowing for the unification of objective and subjective assessments.  
The evaluation of complex electromagnetic environmental effects needs to be analyzed from the perspective of multi-source interference. Although the complex electromagnetic environment is mentioned in current research, it mainly analyzes it from the perspective of the multiple characteristics of interference signals in four domains. It does not really involve the electromagnetic environmental effects under the action of multi-source signals.  
For the effects evaluation of the electromagnetic environment combining the characteristics of interference signals and the work efficiency of the test equipment, the indexes of the environmental effects signal are mainly determined manually; however, the selection of indexes is different and the model adaptability is poor. The subjective evaluation in terms of the effects mechanism analysis is mainly based on interference levels and equipment sensitivity. The evaluation indexes are consistent with each other, but the experimental environment has a significant impact on those results.  
Although the BP neural network is introduced to train model weightings and the model's parameters are processed based on AHP and other methods, the input parameters of the neural network are still determined manually, leading to greater subjectivity of the evaluation indicators. These indicators, which are closely related to the evaluation of the electromagnetic environment and equipment, may be missed.  
The tremendous amount of research is replete with the efforts of many researchers for the electromagnetic environmental effects evaluation, indicating that it has a solid theoretical foundation and engineering application. Based on large amounts of analysis of the literature on the effects evaluation of the electromagnetic environment and our contributions to research, some research ideas are worthy of further exploration and inquiry as follows.
## 3 Results and Discussion
### Electrochemical Behavior
The parameters obtained from the equivalent circuit were \(R_{s}\), \(R_{ct}\), \(CPE\), \(L\), and \(R_{L}\), which symbolize the solution resistance, charge transfer resistance, constant phase element, inductance element, and electrical inductance, respectively. A capacitive loop was formed between the high-frequency and intermediate-frequency regions (HF/IF), while an inductive loop was formed between the intermediate- and low-frequency (IF/LF) regions, as shown in the Nyquist plot for both the as-received and ECAP conditions. The charge transfer resistance (\(R_{ct}\)) and the formation of a double-layer capacitance (\(C_{dl}\)) at the Al alloy surface, as well as the interface of the solution, relied upon the capacitive region in HF/IF. The corrosion product formation, adsorption, and desorption on the metal surface depended on the inductive loop [52; 53; 54; 55; 56]. The initial stage of the pitting corrosion is indicated by \(R_{L}\) and \(L\)[57; 58]. Figure 5: EIS Bode angle plot of the Al5083 alloy in the 3.5% NaCl solution. Figure 6: Nyquist plot curve of the Al5083 alloy in the 3.5% NaCl solution. The polarization resistance (\(R_{P}\)\({}^{-1}\) = \(R_{ct}\)\({}^{-1}\) + \(R_{L}\)\({}^{-1}\)) of the Al alloy was inversely proportional to the corrosion resistance. The corrosion resistance of the as-received sample was higher than that of the ECAP one, which was supported by the Nyquist plots, where the radius of the largest loop curve was observed for the as-received sample. Table 2 presents the obtained \(R_{p}\) values from these curves, and from the fitted circuit model (Figure 7). The Nyquist plot pattern was almost similar for the as-received and ECAP samples (Figure 6). Therefore, the ECAP may be expected to have very little effect on the character of the surface properties during corrosion. However, the microstructural state affected polarization resistance. After the ECAP processing, the polarization resistance value became lower than that in the as-received sample. A higher value of \(R_{P}\) was found in the as-received sample compared to the ECAP sample under different passes. As referenced, a few papers have portrayed corrosion resistance variations as a function of the grain size caused by the expanded forced strain due to the ECAP. The \(R_{p}\) value also supports the same herein.
# Hybrid Shielding for Hypervelocity Impact of Orbital Debris on Unmanned Spacecraft
###### Abstract  
The passive shielding of space craft structures is critical due to the increase in demand for lightweight protection, which is required to counter the damaging effects of micro-meteoroid orbital debris (MMOD) on unmanned spacecraft, which have steeply increased in recent years. Research on hypervelocity impact (HVI) led to the development of shield configurations such as the conventional Whipple shield, which consists of two plates separated by a stand-off distance to allow for the fragmentation and dispersion of the debris from the impact. Variations in the Whipple shield have been proposed, where additional layers are included for increased energy dissipation efficiency. In this work, the authors develop, validate and test a numerical model of an orthogonally loaded hybrid Whipple shield, incorporating an aluminium honeycomb core, orientated with hexagonal tubes perpendicular to the direction of proposed debris travel, to mitigate the well-known channelling effect. The debris threat is an A2024-T3 projectile, impacting the structure at a velocity of 6.5 km/s. The proposed model is validated with experimental observations of the debris spread at half-angle and the efficiency of the proposed topology is assessed against a conventional two-plate A2024-T3 shield. The honeycomb core cell density, its position relative to the point of impact, the thickness of the honeycomb shell, and the material of the honeycomb are thoroughly analysed. A hybrid honeycomb structure concept is proposed, which provides a highly efficient alternative to a standard Whipple shield design, without significantly compromising the weight of the structure. The obtained results clearly show that the hybrid Whipple shield exhibits significantly increased the kinetic energy dissipation of the debris from the impactor and shield front plate, with an increase in the dissipated kinetic energy that can reach 86.8% relative to the conventional shield.  
hypervelocity impact; whipple shield; honeycomb passive shielding; micro-meteoroid and orbital debris (MMOD); Smoothed Particle Hydrodynamics (SPH); finite-element analysis; LSDyna +
Footnote †: journal: Applied Physics Letters B  
12 May 2022  
Accepted: 12 July 2022  
Published: 13 July 2022
## 3 Results
### XRD Analysis  
The XRD patterns registered on the pigmented layers were all very similar, with several mineral phases being observed on all investigated fragments: calcite, quartz, and hematite. As shown in Figure 7, calcite is the main phase identified. The high intensity of the calcite peaks within the recorded diffractograms indicates that the pictorial layer is not thick enough to mask the strong signal coming from the substrate [45].  
Braunite (Mn\({}_{2}\)O\({}_{3}\)), a naturally occurring manganese oxide frequently found in metamorphic manganese-rich systems and in hydrothermal deposits, could be present in the  
Figure 7: XRD pattern registered on the black pigmented area (sample S7) (**a**), on the yellow pigmented area (sample S6) (**b**), and on the coarse plaster layer (sample S9) (**c**). Identified mineral phases are marked as follows: C—calcite; Q—quartz; H—hematic; K—kaolinite; M—muscovite; I—illite; B—braunite.  
black finishing layer (Figure 7a) [46]. The presence of a manganese oxide is sustained by both FTIR and XRF data. As indicated in previous studies [37; 38], wads ( manganese ochres) are typically composed of one or more manganese oxide minerals along with other associated ore and gangue minerals such as quartz, feldspar, calcite, or iron oxides. Quartz and iron oxides under the form of hematite were observed in all pigmented areas, including the black finishing layer, indicating that both quartz and hematite grains are associated with the manganese oxide pigment in this latter case [45].  
The presence of goethite, typically found within yellow-based ochres [37], could not be highlighted within the X-ray diffraction pattern (Figure 7b). Limonite, jarosite ochres, or less common yellow iron oxides such as hydroxide lepidocrocite (Y-FeOOH) were also ruled out. This situation could be due to the low concentration of the iron oxide chromophore present within the investigated yellow pigmented layers. However, small amounts of hematite could be observed, which may indicate that the yellow color of the used pigment could be due to a mix of hematite (and most probably goethite as well) with clay minerals and other metal oxides [28].  
The results of the X-ray powder analysis carried out on the coarse plaster layer indicate that calcite and quartz are the main mineralogical components (Figure 7c). Small amounts of kaolinite, muscovite, illite, and hematite were also confirmed. The presence of muscovite can be correlated with the rich potassium content, a indicated by XRF analysis. All other components were also identified via FTIR analysis.