esa-sceva/satcom-chunk-collection huggingface dataset:
# Identification of Volatile Sulfur Compounds Produced by _Schizophyllum commune_
## 1 Introduction  
A basidiomycetous fungus _Schizophyllum commune_ is a common mushroom that is found frequently in the environment, especially on withered trees. The fungus is also known as a causative agent of allergic bronchopulmonary mycosis (ABPM), allergic fungal rhinosinusitis, and basidiomycosis [1, 2, 3]. In contrast to fungal allergy, those diseases involve the colonization and growth of the causative agent in the host. A nationwide survey revealed that sputum culture among 6% of the patients with possible ABPA-central bronchiectasis was _S. commune_ positive [4], although _Aspergillus_ spp. are the major causative agents of ABPA. [PERSON] reviewed 218 reported cases of basidiomycosis including allergic and invasive mycoses, which showed that the most common causative agent among basidiomycetes was _S. commune_ (52.3%) [2].  
_S. commune_ is usually identified by culture methods. However, morphological identification of _S. commune_ is difficult, and colonies may be considered simply contaminated or misidentified as other causative agents because the colony shows non-specific, white, andfluffy characteristics. Further, it seems to be rare to successfully culture _S. commune_ from clinical specimens. We previously identified a _S. commune_ antigen _Sch_ c1 [5]. Although the increased titer against _Sch_ c1 was auxiliary used for the diagnosis of _S. commune_ infection, serum samples are required for the method [6]. Further options, especially non-invasive and rapid options, are being awaited for for the diagnosis of diseases by _S. commune._  
The culture emits an unpleasant odor derived from volatile organic compounds (VOCs) [2], suggesting that unique VOCs are promising biomarkers for use in the diagnosis of _S. commune_ infection. VOCs produced by _Aspergillus_ spp., the major causative agent of ABPM, have been investigated [7; 8; 9]. [PERSON] et al. reported 2-pentylfuran as a VOC, suggesting it as a biomarker candidate of _Aspergillus_ infection [7; 10; 11]. Other compounds such as \(\alpha\)/\(\beta\)-trans-bergamotene, trans-geranylacetone [8], 2,3-butanedione, and 3-hydroxy-2-butanone [9] were also reported as VOCs produced by _A. fumigatus_. Although several VOCs produced from _S. commune_ were identified [12; 13], few reports have been published. [PERSON] showed ethanol and \(\beta\)-bisabolol as the major VOCs from _S. commune_[13]. [PERSON] et al. reported that methyl 2-methylbutanoate, ethyl 2-methylbutanoate, and methyl 2-methylpropanoate were detected from monokaryotic mycel of _S. commune_ and some VOCs, such as _S_-methyl thioacetate and 3-methyl-1-butanol, were detected specifically from dikaryon [12].  
As explained in earlier reports [7], VOCs might be potential biomarkers of the infection. For this study, we explored _S. commune_ VOCs that might be candidates as biomarkers of infectious diseases by _S. commune._
# Satellite Measured Ionospheric Magnetic Field Variations over Natural Hazards Sites
## 5 Results
### Rocket Launches  
Acoustic shock waves, which are basically similar to processes in explosions, have the peculiarity to initiate ionospheric disturbances. Striking examples are rocket launches with wave generation in the exhaust wake during supersonic ascents, measurements and results are _inter alia_ from ionospheric sounders and Global Positioning System (GPS) receiver  
Figure 8: Distribution of CSES CDSM (**top**) and FGM1 (**bottom**) magnetic field fluctuations grouped in individual frequency bands for the Tlahuellpan, Mexico, event, 2019-01-18T13:10 UTC. The time shift to the event is \(\Delta t=\{-1.21,\,-0.66,\,-0.22,\,0.33,\,0.83\}\) days. The colour coding is the same as in Figure 2, the geomagnetic activity is \(|\mathrm{Dst}|\leq 15\) nT for the selected time span.  
networks [35]. In two case studies we investigate if the disturbances are detectable with magnetic field measurements at LEO altitudes.
# Structural Information Reconstruction of Distorted Underwater Images Using Image Registration
## 4 Discussion
### Analysis of Restoration Results
Furthermore, the underwater image quality measure (UIQM) [33] is employed as a no-reference quality metric, considering that the true images of the \"Large fonts\" and \"checkboard\" are unavailable. The metric is attributed by colorfulness measure (UICM), sharpness measure (UISM), and contrast measure (UIConM). In our experiment, the data sets are grayscale images so that the UICM is identically equal to zero. The comparison results of image quality are presented in Table 2. It shows that the proposed method outperforms our previous method and is close to the Oreifej method in full-reference metrics. For most of the no-reference metrics, the proposed method performs the best numerical value, which indicates the sharpness and contrast of the whole image are significantly improved. The higher value of UISM shows the image contains richer structural edge information and details, and the greater value of UIConM reflects the figure is more suitable for the human visual system. For all metrics, the UIQM, which reflects the underwater image quality from different aspects, is presented based on human visual system and is a special image evaluation for the underwater environment. However, GMSD and FSIM are not as relevant to people's intuitive feelings as UIQM. Therefore, we prefer UIQM as the main indicator. Although the residual distortions of the proposed method are indicated by GMSD and FSIM, compared with the removal of all distortions, the sharp letter distinction and details, indicated by higher contrast and sharpness, contribute more to the subsequent observation. Furthermore, from Table 2, we found that the proposed method performs better in UISM, UIConM, and UIQM, which means that it is suitable for the applications that emphasize the human visual effects of restored images and emphasizes structural boundary information, such as underwater text image.
# Measurement of the Resonant Magneto-Optical Kerr Effect Using a Free Electron Laser
###### Abstract  
We present a new experimental magneto-optical system that uses soft X-rays and describe its extension to time-resolved measurements using a free electron laser (FEL). In measurements of the magneto-optical Kerr effect (MOKE), we tune the photon energy to the material absorption edge and thus induce the resonance effect required for the resonant MOKE (RMOKE). The method has the characteristics of element specificity, large Kerr rotation angle values when compared with the conventional MOKE using visible light, feasibility for \(M\)-edge, as well as \(L\)-edge measurements for \(3d\) transition metals, the use of the linearly-polarized light and the capability for tracing magnetization dynamics in the subpicosecond timescale by the use of the FEL. The time-resolved (TR)-RMOKE with polarization analysis using FEL is compared with various experimental techniques for tracing magnetization dynamics. The method described here is promising for use in femtomagnetism research and for the development of ultrafast spintronics.  
magneto-optical Kerr effect (MOKE); free electron laser; ultrafast spin dynamics +
Footnote †: journal: Applied Physics Letters  
1
Footnote 1: _Appl. Sci._**2017**, \(7\), 662; doi:10.3390/app7070662
## 2 Methodology  
### Fundamental of ECPT  
A basic schematic diagram of ECPT is shown in Figure 1a. A coil with high frequency alternating current (AC) produces a short burst of electromagnetic excitation and thereby induces eddy currents (ECs) in the conductive material. The induced ECs concentrate in a skin depth of homogeneous material, and a local temperature rise can then be caused by the effect of Joule heating from the eddy currents. The heat will be conducted from the heated area to other places until a thermal balance is finally reached. In case there is a surface crack in a conductive material, as depicted in Figure 1a, the presence of a crack can cause a diversion in the EC flow and heat conduction, which leads to a different temperature distribution pattern on the surface [19]. Hence, the crack can be detected by the variation of temperature distribution between the defect and the surrounding sound areas in the IR images.  
As mentioned above, due to its large volume and weight, it is difficult to thermally excite and detect the bit once using a small induction coil. In order to solve such a problem, ECPT with low excitation power and a small-size coil is proposed here to detect surface cracks on the matrix of a PDC bit in scanning mode.  
From Figure 1b, we can see the temperature distribution of a specimen with a surface crack on the matrix excited by an annular coil with a small size, which is calculated with COMSOL. For a given position of the coil, only the annular region below the coil can be directly heated because the size of the coil is much smaller than the length of the crack. It means that only the crack segment within and around the annular coil labeled in Figure 1b can be directly detected each time. In order to completely detect all segments of the crack, it is necessary to move the excitation coil to perform a scanning heat excitation. However, it is difficult to use continuous scanning approach presented in previous work [18] because PDC bits have an irregular (rather than flat) surface. Therefore, a successive scanning mode is proposed for heat excitation on PDC bits. After finishing one heating excitation at the present position (Position I), as shown in Figure 1b, the coil will be moved to the following position (Position II). The rest can be done in the same manner until all positions are detected. Then, the crack will be reconstructed by all segments in varied IR images captured in different positions. Obviously, such scanning heating mode makes it possible to ensure that all positions of a bit matrix are effectively heated because the coil can be placed in a flexible and suitable manner for each position.
# LIDAR Point Cloud Registration for Sensing and Reconstruction of Unstructured Terrain
## 1 Introduction
Compared with the traditional one-step registration method, our method has high accuracy and can reconstruct the point cloud of unstructured terrains quickly. It can effectively solve the accuracy problem of registration for density-uneven and noisy point clouds of unstructured terrains. The rest of this paper is organized as follows. Section 2 introduces the traditional initial registration method. Section 3 describes our accurate two-step registration method. Four groups of experiments are conducted in Section 4 to validate the feasibility and performance of our two-step registration method. Finally, a brief conclusion and future research directions are given in Section 5.
## 3 The Proposed Method
### Decryption Process  
The decryption process is the inverse of the encryption process. Firstly, the key value **K** us generated from the user's key by the SHA-512 hashing algorithm. The key value K is then used to compute a set of random Gaussian numbers as described by Equations (6) and (7) in Section 3.3. The encrypted objects are then extracted from the encrypted vector map and vertices are re-randomized by the random Gaussian numbers before decryption using the key value **K**. After the vertex re-randomization process, if the encrypted object cannot be simplified, it is decrypted by the key value **K** to generate the backbone object. If the encrypted object can be simplified, it is simplified to compute the encrypted feature vertices. These encrypted feature vertices are then decrypted by the key value **K** to generate the backbone object. From the backbone object, we can calculate the decrypted objects based on Equation (1). The decrypted GIS vector map is a set of the decrypted objects.
# Effects of Nanoparticles on Algae: Adsorption, Distribution, Ecotoxicity and Fate
## 3 Distribution of NPs in Algal Cells  
When NPs enter the algal cells, they can destroy the cell wall and cell membrane, and will be deposited in the space between the cell wall and the plasma membrane (i.e., the periplasmic space) [19; 46]. NPs can enter the cytoplasm and make contact with some organelles of cellular structures, including chloroplasts, vacuoles, endoplasmic reticulum, Golgi apparatus and mitochondria, and will significantly damage or alter their function and structure [20; 47]. It has been observed that NPs entering cells will damage the structure of chloroplasts. In the chloroplasts, the chloroplast membrane was damaged, and the grana lamellae of the thylakoids was also be destroyed, creating a messy state [46; 48]. CuO NPs were observed to be clearly deposited in the vacuoles of algal cells [47]. Heavy metals can accumulate in the vacuoles of _Chlamydomonas acidophila_ cells, likely as a detoxification mechanism [49]. One study has shown that endoplasmic reticulum swelling was clearly observed in algal cells exposed to high doses of single-walled nanotubes [50]. NPs can also cause mitochondrial dysfunction, which may affect the metabolic process of algal cells [47]. In addition, NPs can enter the nucleus, leading to abnormal nuclear effects [20; 51]. It was found that nuclear chromatin clumped and became condensed against the nuclear membrane [46]. This can cause DNA damage and thereby inhibit the process of cell division. Internal degradation of the nucleus and chloroplasts occurred in algal cells treated by ZnO NPs [20]. There was also NPs aggregation in other cytoplasmic spaces [19]. In short, after entering the algal cell, NPs will be spread to various parts of the cell. It will not only destroy the cellular structure, but also influence the metabolism and reproductive function of the algal cell. However, very few studies have described the transport mechanism of NPs in algal cells, which remains to be studied. The possible distribution and potential toxic outcomes of NPs in algal cells are shown in Figure 1.
# Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols
## 5 Clustering Process Optimization
### Data Aggregation Phase
#### 5.3.2 Cluster-Based Data Aggregation  
This scheme depends on cluster formation; in every cluster, a CH serves as the point for data aggregation. An example of this method is the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol [62; 63], where CHs serve as the data aggregation points. The Hybrid Energy-Efficient Distributed (HEED) protocol is another cluster-based data aggregation protocol where the CH selection is based on the availability of multiple power levels at the SNs [64]. A combined metric takes into account both residual node energy and the nearness of the node to its neighbors.
## 4 Performance Evaluation
### Experimental Results  
#### 4.3.1 Performance Comparison for Varying Numbers of Threads  
The benchmark programs were run with varying numbers of threads, ranging from 1, 2, 4, 8, 16, and 32. The average elapsed time, the standard deviation, and the ratio of these times to an unmodified Linux kernel are calculated for different task selection policies and the specific number of threads. Figure 11 shows the results.  
\begin{table}
\begin{tabular}{c c c c} \hline
\begin{tabular}{c} Based on the \(\text{Excl}_{\text{base}}\) policy. \\ Enhanced task selection procedure. \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 6: The experimental system running different task selection policies.  
Figure 11: Experimental results for different numbers of threads. (**a**) Average elapsed time of each test case; (**b**) Standard deviation of each test case; (**c**) The performance increase over the unmodified Linux kernel.  
Figure 11a shows that all test cases behave similarly in terms of average elapsed time for different numbers of threads. As the number of threads increases, multithreaded benchmarks run faster since a multi-core system's parallel computing capability increases performance as well. Besides, since more threads wait in the runqueue, an effective task selection policy can select a more suitable one among them for migration. The proposed task selection policies allow more efficient inter-node task migration for the load balancing mechanism on the experimental NUMA system. These perform better than the First-Fit policy that is used in the unmodified Linux kernel.  
However, the parallel computing capability of a multi-core system is limited, the elapsed time measured depends on the number of cores in the target system. Because the experimental system has 32 cores, if there are too many threads for a multithreaded benchmark, contention for cores and memory slows down the performance. On the contrary, if the number of threads in a multithreaded benchmark is much smaller, multi-core is not fully utilized. Besides, few or no threads wait in runqueue, then task selection policies are not triggered or used effectively. Therefore, the increase in performance is not so great when the numbers of threads are 1, 2, and 32, as shown in Figure 11c.
# An Integrated Thermal-Electrical Model for Simulations of Battery Behavior in CubeSats
## 2 Methodology
### Irradiance Model
The angle \(\phi\) between the satellite's position and the Earth's border is:  
\[\phi=\sin(1/H) \tag{24}\]  
The angle \(\gamma_{w}\) between the orientation of surface \(w\) and the center of the Earth is given by Equation (25), and Equation (26) is the expression for the view factor [28]. \[\gamma_{w}=\cos^{-1}\left(\frac{\vec{\mu}_{w}.\vec{R}_{\text{sat}}}{|\vec{\mu}_ {w}||\vec{R}_{\text{sat}}|}\right) \tag{25}\]  
\[F_{w\to e}= \begin{cases}\frac{\cos(\gamma_{w})}{H^{2}},&\gamma_{w}\leq\frac{ \pi}{2}-\phi\\ \frac{2}{\pi}\big{(}\frac{\pi}{4}-W_{1}+W_{2}\big{)},&\frac{2}{\pi}-\phi\leq \gamma_{w}\leq\frac{2}{\pi}+\phi\\ 0,&\text{otherwise}\end{cases}  \tag{26}\]  
The terms in Equation (26) are:  
\[W_{1}=\frac{1}{2}\left[\sin^{-1}\left(\frac{\sqrt{H^{2}-1}}{H\sin(\gamma_{w} )}\right)\right] \tag{27}\]  
\[W_{2}=\frac{1}{2H^{2}}\bigg{[}\cos(\gamma_{w})\cos^{-1}\left[-\sqrt{H^{2}-1} \cot(\gamma_{w})\right]-\sqrt{H^{2}-1}\sqrt{1-H^{2}[\cos(\gamma_{w})]^{2}} \bigg{]} \tag{28}\]  
**Infrared radiation:** this source of thermal energy has its origin on Earth, which is always above absolute zero, and for this reason, emits radiation as:  
\[Q_{\text{IR}_{w}}(t)=\alpha_{w}A_{r_{w}}F_{w\to e}(t)Q_{e}^{\prime\prime}, \tag{29}\]  
where \(Q_{e}^{\prime\prime}\) is the radiative heat flux emitted by the Earth, at the infrared wave-length. **Satellite emission:** the satellite's emission of radiation to outer space, in the infrared spectrum, is governed by:  
\[Q_{\text{sat}_{w}\rightarrow\text{spa}}(t)=\varepsilon_{w}A_{r_{w}}\sigma \Big{(}T_{w}^{4}(t)-T_{\infty}^{4}\Big{)}, \tag{30}\]  
where \(\varepsilon_{w}\) is the emissivity of the surface in the infrared spectrum, \(\sigma\) is the Stefan-Boltzmann constant (\(5.67\times 10^{-8}\) W/m\({}^{2}\)K\({}^{4}\)), \(T_{w}\) is the temperature of PV \(w\) and \(T_{\infty}\) is the temperature of space. The parameters used in this work for the irradiance model are summarized in Table 1.  
Figure 4: Geometry to estimate \(F_{w\to e}\). The solar irradiance is constant and equal to 1360 W/m\({}^{2}\) in this model, as shown in Table 1. In reality, this parameter changes primarily with the Earth/Sun distance and the 11-year solar cycle. However, the impact of these variations are within the model's uncertainties. As observed in Equation (30), the boundary condition is a function of the temperature at photovoltaic panels (\(T_{w}(t)\)), a variable to be solved in the thermal model. More information on irradiance models can be found in [29].
# CL-MMAD: A Contrastive Learning Based Multimodal Software Runtime Anomaly Detection Method
## 6 Results Analysis
### Performance Evaluations of CL-MMAD, MSE Loss vs. InfoNCE Loss  
We evaluated the performance of CL-MMAD using MSE-loss and InfoNCE-loss as anomaly scores separately.  
Anomaly scores produced by CL-MMAD using MSE measures are shown in Figure 13.  
The findings presented in Figure 13 demonstrate that the CL-MMAD model trained under the mean squared error (MSE) loss can effectively distinguish between normal and abnormal samples, yielding a satisfactory performance.  
Figures 14 and 15 present the anomaly scores of normal and abnormal samples utilizing the InfoNCE loss. The abnormal scores of both normal and abnormal samples are intertwined, making it challenging to differentiate them distinctly. Moreover, neither the lowest nor the most stable group of hyperparameters can achieve a desirable anomaly detection performance. The results highlight the importance of having sufficient negative samples for contrastive learning to capture pairwise information between modalities, thereby necessitating a relatively large batch size and more advanced GPU devices. The latent space learns hierarchical features of the software runtime statuses, most of which are irrelevant for anomaly detection. Consequently, difficulties in converging at global optimal points and the presence of excessive unrelated information make the InfoNCE loss unsuitable for software anomaly detection.  
Figure 13: Anomaly scores of normal samples and abnormal samples produced by CL-MMAD using MSE loss.  
The results shown in Table 5 have revealed that InfoNCE is not a good choice for anomaly detection tasks. Not only is it difficult to train with InfoNCE loss, but it is also difficult to obtain better results when performing an anomaly detection task.  
MSE loss as the anomaly score can achieve a rather high accuracy after applying the label-adjusting strategies.
## 3 Overviews of the Articles Published in the Special Issue
### Performance  
[PERSON] et al. [Contribution 4] analyzed the main cause behind the lateral vibration problem in longitudinal timing sections, and proposed a specification of longitudinal texture that can shed light on lateral vibration. The longitudinal timing of concrete pavements is very important to reduce noise and improve skid resistance. However, lateral vibration in longitudinal timing sections threatens driver safety. In this paper, several methods were conducted to determine the optimal longitudinal texturing specification, including panel rating, FEM analysis, field tests, indoor driving simulation tests, and field applications. The results show that the texture denoted as 2 mm \(\times\) 3 mm \(\times\) 19 mm is the optimal longitudinal texture on aged concrete pavement. However, since this criterion is difficult to apply to concrete pavement at an early age, 3 mm \(\times\) 3 mm \(\times\) 16 mm is recommended as the optimal longitudinal texture, achieving the best performance from the panel ratings as well as in the numerical analysis. Based on these findings, lateral vibration in longitudinal timing sections on the studied expressway will be reduced.  
[PERSON] et al. [Contribution 7] studied the application of mineral and glass wool waste for fiber-reinforced concrete. By adding mineral and glass wool waste in a range from 0.5 to 2% by cement weight, the study aimed to determine the material properties in the areas of unhardened concrete, as well as its durability and mechanical strength properties. The air content was not affected to a significant extent by adding waste fibers. However, by adding more waste fibers, the slump loss is ascribed to the high absorption characteristics of the fibers. Furthermore, the test results demonstrated that by adding fibers, the compressive strength was decreased compared to the control specimen. In contrast, the increase in tensile strength is ascribed to the load-bearing characteristics of the waste fibers. The improvement in freeze-thaw resistance was confirmed in all the glass wool waste specimens. The test results for chloride penetration resistance were found to be equivalent to those of the control specimen. However, it was found that a 2% or higher fiber addition rate was needed to secure freeze-thaw resistance in the case of mineral wool waste. With the increase in the addition rate, a significant increase in permeability was observed.