esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Antioxidant Activity Measurement  
2.5.1. 2,2-diphenyl-1-picryl-hydrazyl-hydrate (DPPH) Activity Measurement  
The DPPH activity was assessed by modifying the Blois [28] method to measure the reducing power of the GV samples. DPPH was dissolved in methanol, 50 uM of the dissolved DPPH (190 uL) was added to GV extracts (10 uL) at various concentrations, and the reaction was maintained at 24 \({}^{\circ}\)C for 15 min. The synthetic antioxidant butylhydroxytoluene (BHT) was prepared as a positive control group, and the DPPH assay was performed by measuring the absorbance of each sample at 490 nm, and the DPPH activity was calculated using the following formula [28]. The concentration of the GV extract that scavenged 50% of the DPPH radicals is represented by its half-maximal inhibitory concentration (IC\({}_{50}\)) value.  
\[\text{DPPH activity }(\%)=\frac{\text{A}_{\text{sample}}-\text{A}_{\text{control}} }{\text{A}_{\text{sample}}}\times 100 \tag{2}\]  
where A\({}_{\text{sample}}\) is the absorbance of the reactant with the GV extract, and A\({}_{\text{control}}\) is the absorbance of the reactant without the GV extract.  
2.5.2. 2,2\({}^{\prime}\)-Azino-bis(3-ethylbenzothazoline-6-sulfonic acid) (ABTS) Activity Measurement  
To measure ABTS radical scavenging of GV extracts, 250 uL of 2.45 mM potassium peroxodisulfate mixed with 7 mM 2,2\({}^{\prime}\)-Azino-bis(3-ethylbenzothazoline-6-sulfonic acid) diammonium salt was reacted under dark conditions at 24 \({}^{\circ}\)C for 15 h. Next, the ABTS solution was adjusted with distilled water until the optical density reached 0.6 at 734 nm [29]. ABTS solution (180 uL) was added to the GV extract (20 uL) and kept at 24 \({}^{\circ}\)C for 4 min. The ABTS assay of all samples was performed by measuring the absorbance at 734 nm and calculating the absorbance using the susequesnt formula. The concentration of the GV extract that scavenged 50% of the ABTS radicals was represented as the IC\({}_{50}\) value.  
\[\text{ABTS activity }(\%)=\frac{\text{A}_{\text{sample}}-\text{A}_{\text{control}} }{\text{A}_{\text{sample}}}\times 100 \tag{3}\]
## 3 Numerical Analysis Results
### Relief Cone Effect on SCF  
In this section, the effect of the relief cone angle (10 degree) on SCFs was investigated at the bolt-insert joint model, and the combined effect of the cone angle and reduced shank diameter was also examined, as shown in Figure 13. The various analysis cases with the combination of shank diameter and cone angle are shown in Figures 14 and 15 with two-dimensional and three-dimensional models, respectively.  
As listed in Tables 10 and 11, the SCF was further reduced by 10% with the application of the relief cone angle. In particular, applying the relief cone angle to the bolt was more beneficial in reducing SCF than insert cone application by 10% at most. Therefore, the bolted joint model consisting of a neck-down bolt (reduced shank bolt) with a relief cone and a root insert with a large shank diameter showed the largest reduction in the SCF by 50% in total.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Nut Type**} & \multirow{2}{*}{**Shank Type**} & \multirow{2}{*}{\begin{tabular}{c} **Norm Diameter** \\ **(mm)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Norm Stress** \\ **(MPa)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Max. Principal** \\ **Stress (MPa)** \\ \end{tabular} } & \multirow{2}{*}{**SCF**} & \multirow{2}{*}{**Reduction**} \\ \hline Standard & Straight & 37.1 & 369 & 1995 & 5.40 & 0\% (base) \\ Standard & Reduced & 28 & 650 & 1936 & 2.98 & 44.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Bolt shank effect on SCF (connected with standard nut type).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Nut Type**} & \multirow{2}{*}{**Shank Type**} & \multirow{2}{*}{\begin{tabular}{c} **Norm Diameter** \\ **(mm)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Norm Stress** \\ **(MPa)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Max. Principal** \\ **Stress (MPa)** \\ \end{tabular} } & \multirow{2}{*}{**SCF**} & \multirow{2}{*}{**Reduction**} \\ \hline Round & Straight & 37.1 & 369 & 1683 & 4.56 & 0\% (base) \\ Round & Reduced & 28 & 650 & 1629 & 2.51 & 45\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Bolt shank effect on SCF (connected with round nut type).  
Figure 12: 2D stress analysis of straight bolt and neck-down (reduced shank) bolt.
## 2 Background on SSI  
[PERSON] [11] first proposed the ideas and principles that form the current design goals of SSI. The ten principles can be summarized as existence, control, access, transparency, persistence, portability, interoperability, consent, minimization, and protection. According to [PERSON], by implementing selective disclosure, developers can facilitate data minimization better and thereby support privacy. Due to putting the user at the center of any authentication, the users must protect their identities to avoid data breaches. Besides confidentiality and integrity, this also includes the availability of users' data and any associated claims.  
The following sections introduce the terminology used to describe SSI systems and components (see Section 2.1), provide an overview of common SSI architectures (see Section 2.2), and showcase relevant implementations of SSI systems (see Section 2.3). As the literature survey includes publications from other areas, a general outline of blockchain and DLT is provided (see Section 2.4).
# Scanning Electron Microscopy Techniques in the Analysis of Gunshot Residues: A Literature Review
## 3 Results
### Future Prospects of SEM in the Forensic Field  
The adoption of AI and automation, the miniaturization of instruments, and innovations in imaging and spectroscopy techniques could significantly enhance SEM applications, making forensic investigations faster, more accurate, and more efficient. Future developments in SEM-based forensic investigations are characterized by technological innovations and the integration of new techniques, which promise to improve analytical capabilities and investigation efficiency.  
One of the main advancements in SEM involves the integration of artificial intelligence (AI) and machine learning algorithms. AI can revolutionize the interpretation of SEM images by automating the identification and classification of particles and structures, reducing analysis time and increasing accuracy [38; 39]. Automation in sample preparation, including automated fixation and embedding, could further minimize human errors and improve the reproducibility of results. Standardized preparation processes would ensure higher consistency and quality in forensic sample analyses [40; 41].  
Another promising direction for SEM involves miniaturization and portability. The development of portable SEM instruments could allow forensic investigators to analyze samples directly at crime scenes or in emergency situations. These portable instruments could be designed to be lighter, more compact, and easy to transport, while maintaining an adequate resolution and sensitivity for forensic applications [42; 43]. The miniaturization of SEM components could also lead to the creation of smaller and more versatile microscopes, with applications in forensic and clinical situations that require high-resolution instruments in limited spaces [44; 45].  
Advancements in imaging techniques are also shaping the future of SEM. The use of electron tomography and other 3D imaging techniques is becoming increasingly common in SEM. These innovations provide a three-dimensional view of samples, improving the understanding of their structure and composition [46; 47]. Furthermore, integrating SEM with advanced spectroscopic techniques, such as X-ray photoelectron spectroscopy (XPS) and mass spectrometry, can enhance the compositional analysis of samples, providing more detailed chemical and structural information [36; 48].
## 4 Microbiota Disturbance Influences the Suppressive Properties  
### Microbiome Induces Defense Response  
The interactions among pathogen, plant and microbiota are fundamental to modulate a protective phytobiome to fungal invasion [185]. Plant pathogenic fungi and bacteria can induce differential responses to the stress that lead to variation in the microbiome composition of soil and activation of general and/or specific antagonisms restraining the infection through direct/indirect actions of the microbiome or through responses of the host plant. The rhizosphere microbiome can create a barrier against the root infection that can exclude the pathogenic invader activating competition [186]. For instance, PGPR act as a dynamic microbial network that affects the invasion, infection and severity of the disease [186]. Ribosomal RNA-based analyses revealed that the most abundant taxa in the sugar beet seedlings rhizosphere after invasion of _R. solani_ belonged to the families _Oxalobacteraceae_, _Burkholderiaceae_, _Sphingobacteriaceae_ and _Sphingomonadaceae_[185]. In this study, the authors observed that bacterial taxa identified at a family level upregulated the stress-related genes (ppGpp) in response to pathogen invasion and colonization.  
Maintaining the higher soil functionality, the microbial biodiversity increased the ecosystem resilience by making the soil less vulnerable to the biotic stress [187]. Soil biodiversity can significantly contribute to disease suppressiveness by supporting higher trophic level organisms that feed on pathogens and improving plant health and resistance [188]. In general, the mechanisms by which DSS can control disease development are different [189]: (a) parasitism against pathogens by beneficial microbes/microbial communities; (b) production of metabolites, toxins and antibiotics [190; 191]; (c) competition for nutrients/resources/substrates; (d) activation of disease-resistance genes in the host plant by beneficial microbes; and (e) improvement of plant nutrition and soil health. Disease suppressiveness can be attributed to the combination of different mechanisms that support each other by forming a true microbial consortium acting as a \"super-organism\" against specific pathogens. For example, the combined action of mutually compatible non-pathogenic _F. oxysporum_ Fo47 and several strains of fluorescent _Pseudomonas_ spp. can stimulate soil suppressiveness [192]. Relative abundance of disease-suppressive functional genes can be assessed targeting the _prnD_ gene [193] that is responsible for the production of antifungal compounds such as pyrrol
## 3 Results and Discussion  
### Selection of Marker Components and Optimization of HPLC-DAD Conditions for Quality Control of DSHT  
To determine the optimal marker components for the quality assessment of DSHT, HPLC-DAD analysis was conducted on each component herbal medicine in DSHT to establish the major ingredients. To this end, 16 compounds were investigated: saikosaponin A from _B. falcatum_; baicalin, wogonoside, baicalein, and wogonin from _S. baicalensis_; gallic acid, albiflorin, benzoic acid, benzoylpaeoniflorin, gallic acid, and paeoniflorin fromP. lactiffora; chrysophanol and physcion from P. palmatum; naringin and poncirin from P. trifoliata; and 3,4-dihydrobenzaldehyde and homogenistic acid from P. terntate. As shown in Figure S2, 10 components (gallic acid, albfilorin, paeoniflorin, naringin, benzoic acid, baicalin, poncirin, wogonoside, baicalein, and wogonin) in the DSHT sample were detected out of 16 components investigated, and these 10 compounds were selected as marker components for quality control of DSHT through HPLC analysis.  
After comparing the column temperatures (30, 35, 40, and 45 \({}^{\circ}\)C) and the reversed-phase columns (4.6 mm \(\times\) 250 mm, 5 \(\upmu\)m) such as SunFire (Waters, Milford, MA, USA), Gemini (Phenomenex, Torrance, CA, USA), Capcell Pak UG80 (Shiseido, Tokyo, Japan), and Optima Pak (RS Tech. Co., Daejeon, Korea) using water-acetonitrile (both containing formic acid) as the mobile phase, optimal conditions for analysis were found with a SunFire column maintained at 40 \({}^{\circ}\)C. Using the optimized HPLC-DAD assay, the 10 marker components (gallic acid, albfilorin, paeoniflorin, naringin, benzoic acid, baicalin, poncirin, wogonoside, baicalein, and wogonin) in the DSHT sample were eluted with the resolution \(\geq\)2.13 and the retention factor \(\geq\)1.25 at 6.49, 16.45, 17.32, 21.11, 23.22, 26.32, 26.74, 29.98, 32.34, and 38.07 min, respectively (Figure 1).
# A Trusted Lightweight Communication Strategy for Flying Named Data Networking
## 1 Introduction
The proposed architecture uses our FANET monitoring solution [15] which selects the most trusted and stable UAVs as network monitors. These monitors are responsible for interest packets dissemination, gathering, and forwarding to the next monitor. When a node requires content(s) and generates interest for it, the interest eventually reaches the node that has the content in its CS (either it is the owner of the content or has received the content during past communication). The content will be pushed back to the requester (through the reverse path) from which the interest packet has been received. To achieve the goal of trust-based communication, first inter-UAV trust is established through evaluation of the historical communications among UAVs based on actions similarities. Then, instead of authenticating the chain of trust or web of trust (which is both time consuming and inefficient), we minimize the overhead by evaluating the data producer's trust. Trust value is managed for every producer and the producers are classified based on their level of trust. For the trusted producers,we perform probabilistic authenticity verification where authenticity is checked and verified for a fraction of the content generated by that producer whereas for non-trusted producers, the authenticity of every chunk of the content is verified. It is worth noting that with the increase in trust value of the producer, the number of verifications will decrease thereby increasing performance. Finally, for the data coming from the vanilla NDN in fixed or wired networks, the authentication process is automatically embedded into the intermediate routers until the content reaches the fixed Ground Station that are considered as trusted middle-ware to the trusted authorities, therefore there is no need to check it again. As a result, it minimizes the authentication delay and supports the FANET's real-time applications. The main contributions of this paper are summarized as follows:  
* A new trust-based communication architecture is proposed for FNDN. * A lightweight data authentication strategy is used over the established inter-UAV trust. * A monitor-based interest forwarding and data delivery technique through the shortest and most trust path is proposed. The remainder of the paper is organized as follows: in Section 2, we present the existing trust establishment works for FANET, and the data authentication process used by the vanilla NDN. Section 3 describes our Trust-establishment and Monitor selection strategy, followed by the proposed trust-based lightweight authentication solution Section 4. The performance evaluation of our proposal is discussed in Section 5 followed by open security challenges in FNDN in Section 6. Section 7 concludes the paper with future directions.
## 2 Materials
### Satellite Data
#### 2.2.2 Sentinel-2 (S2)  
S2 is a multi-spectral instrument (MSI) satellite mission with spectral sensitivity to the visible-near-infrared region of the electromagnetic spectrum. In this mission, like S1, there are two identical satellites (a and b). Both satellites have a temporal resolution of 12 days, also resulting in an average of a six-day repeat cycle.  
Figure 2: Ternary plot of the soil class distribution of ISMN sites.  
In our research, we used the Level-2a surface reflectance product processed by ESA. The data has 13 bands ranging from 10- to 60-m spatial resolution. We only used red and near-infrared bands to derive the vegetation indices. As in the case of S1, pixels within the 50 m \(\times\) 50 m region of interest around the stations were extracted to calculate the mean NDVI values. However, this feature was only used to evaluate the model performance in the presence of vegetation and was not included in the feature set to train the model.
# Research on a Comprehensive Evaluation Method of Train Anti-Slip System Performance
## 2 Anti-Slip Performance Evaluation Indexes
### Wheelset Slipping Energy Index  
The slip energy of the wheelset is mainly generated by the speed difference between the wheels and rails. The slip energy value affects the degree of wear on the wheel tread and track surface, and excessive wear can cause wheel-rail scratches. Define the wear power \(P\) as [9]:  
\[P=\mu\times T\times\Delta v \tag{6}\]  
where \(\mu\) is the actual utilized adhesion coefficient, \(T\) is the positive pressure between the wheel and rail (unit: N), and \(\Delta v\) is the speed difference between the wheel and rail (unit: m/s). Therefore, the wheelset slip energy \(W\) (unit: J) is as follows [9]:  
\[W=\int\mu\times T\times\Delta vdt \tag{7}\]  
Starting from the start of the pressure maintaining valve in the anti-slip valve and ending with the stop of the pressure maintaining valve as a slipping control cycle, during which the train is in a slipping control state, the wheelset slipping energy during each slipping cycle \(W_{i}\) is calculated, and the average value of the wheelset slipping energy for a single axle during the entire braking anti-slip process \(W_{avg}\) is calculated. Then, the average  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Initial Braking Speed \(V_{0}\) (km/h)** & \(V_{\text{cr}}\) \\ \hline
40 & 15 \\
80 & 20 \\
120 & 25 \\ \(\vdots\) & \(\vdots\) \\
350 & 55 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Limit value of air consumption increase ratio under different initial braking speeds.  
value of the wheelset slipping energy is taken as the index of the wheelset slipping energy for all axles.  
\[W_{avg}=\frac{\sum_{1}^{n}W_{i}}{n}(i=1,2,3,\ldots n),n\text{ is the number of single axis slipping cycles} \tag{8}\]  
\[W=\frac{\sum_{1}^{m}W_{maxg}}{m},m\text{ is the number of slipping axles} \tag{9}\]  
For the wheelset slipping energy index, the minimum value is taken as \(w_{\min}=0\). At this time, the train wheelset is in a pure rolling state, and there is no speed difference or wear between the wheels and rails. The maximum value is taken as \(w_{\max}=26\),000 J. This value is taken from the requirement in EN15595 that the wheelset slipping energy during a single slipping cycle should not exceed 26 kJ [1]. The normalized index \(x_{3}\) is as follows:  
\[x_{3}=1-\frac{w-0}{26,000-0} \tag{10}\]
## 4 Conclusions  
In this work, the feasibility of quantifying the concentrations of oil samples based on three-dimensional concentration-emission matrix (CEM) spectra, which were constructed by the emission spectra of 10 concentrations and were supplemented by the spectral change information between adjacent concentrations, was studied. The results show that the predicted recovery rates for test samples of six oil samples are between 86.8% and 115.59%, and the relative errors are in the range of 2.09-15.2%. The predicted recovery rates for weathering samples are in the range of 89.68-116.11%, and the relative errors range from 2.47% to 13.87%. The CEM spectra, interpolated in concentration dimension via a bicubic interpolation and combined with matrix similarity matching, can predict the concentration of test samples and weathering samples with good quantitative accuracy.  
Conceptualization, Y.C. and R.Y.; methodology, Y.C., R.Y. and W.Z.; writing-original draft preparation, Y.C. and R.Y.; writing-review and editing, Y.C., R.Y. and N.Z.; supervision, W.Z., Y.H., R.Z., X.C., J.L., W.L., Z.Z.; Project Administration, N.Z.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of China (grant number 61805255), the National Key Research and Development Plan (grant number 2017 YFF0108402, 2016 YFC1400602), the Natural Science Foundation of Anhui Province (grant number 1808085 MF208), and the Key Research and Development Plan of Anhui Province (grant number 1804a0802192).  
The authors declare no conflicts of interest.  
Figure 7: The predicted results of test samples and weathering samples based on the CEM database.
## 5 Conclusions  
The paper presents the results of applying the methods for suppressing coherent noises in the reconstructed particle image related to the scattering parameters of the medium with the studied particles, as well as to the twin-image effect of the investigated particle. Addressing the research questions formulated in the introduction, the authors come to the following practical conclusions.  
On the examples of the method of complex field amplitude reconstruction based on the Gerchberg-Saxton algorithm and a spatial-frequency method, it was shown that the methods for suppressing the twin-image effect of a particle image reconstructed from an in-line hologram improve the quality of the particle image boundary detection according to a boundary intensity jump by at least 1.7 times if the condition \(z\geq\frac{2d^{2}}{\lambda}\) is satisfied, where \(z\) is the distance from the particle to the hologram registration plane, \(d\) is the particle circumscribed diameter, and \(\lambda\) is the wavelength. Thus, the expression \(z=\frac{2d^{2}}{\lambda}\) is a distance condition for the effective use of appropriate algorithms and should be taken into account in the design features of the DHC and other holographic cameras used to record the in-line holograms of particles.  
The methods based on the averaging of digital holograms or images of particles reconstructed from digital holograms are considered to suppress non-stationary coherent noises associated with the heterogeneity of the medium and the studied particles. The study of the dependence of the quality indicators of the reconstructed images of particles on the number of averaging showed that averaging over at least three digital holograms is sufficient to achieve the quality of the particle image boundary suitable for particle recognition. At the same time, the method of averaging is not that critical here: over three digital holograms or images of particles.  
The multiple scattering conditions require a limit on the length of the studied medium volume (DHC holographic depth of scene). This limit depends on turbidity and may vary within the limits of not more than 500 mm for clean water and 50 mm for turbid water.  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.