esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Variable** & **All Patients** & \begin{tabular}{l} **Cystatin C** \\ **\(<\) 0.95 mg/mL** \\ \end{tabular} & \
\begin{tabular}{l} **Cystatin C** \\ **\(\geq\) 0.95 mg/mL** \\ \end{tabular} &
\begin{tabular}{l} **\(P\) Value** \\ \end{tabular} \\ \hline
**No. of Patients** & **n = 31** & **n = 14** & **n = 17** & \\ \hline Age, years, median (25 th–75 th) & 65 (55–72) & 66 (56–72) & 65 (51–72) & 0.84 \\ Male gender-no. (\%) & 26 (84) & 11 (79) & 15 (88) & 0.63 \\
**Risk Factors** & & & & \\ Hypercholesterolemia-no. (\%) & 19 (61) & 8 (57) & 11 (64) & 0.72 \\ Hypertension-no. (\%) & 19 (61) & 9 (64) & 10 (59) & 1.00 \\ Smokers-no. (\%) & 26 (81) & 12 (86) & 13 (76) & 0.66 \\ Family-history of CAD-no. (\%) & 7 (23) & 2 (14) & 5 (29) & 0.41 \\
**Presenting Characteristics** & & & & \\ Systolic BP, mmHg, median & & & & \\ (25 th–75 th) & 155 (140–170) & 152 (140–170) & 160 (150–160) & 0.89 \\ Heart rate, pulse/min, mean \(\pm\) SD & 75 (64–88) & 84 (75–90) & 73 (64–83) & 0.06 \\ ECG changes-no. (\%) & 21 (68) & 10 (71) & 11 (65) & 1.00 \\ Hb, g/dL, median (25 th–75 th) & 14.8 (13.9–15.6) & 14.5 (13.9–15.6) & 14.9 (14.3–15.6) & 0.56 \\ WBC, \({}^{\ast}10^{3}\)/mm\({}^{3}\) median (25 th–75 th) & 8.42 (6.53–12.13) & 9.54 (7.57–12.13) & 7.62 (6.28–11:03) & 0.17 \\ Platelet count, \({}^{\ast}10^{3}\)/mm\({}^{3}\) median & 254 (209–291) & 273 (244–287) & 226 (192–291) & 0.17 \\ Mean platelet volume, fl median & & & & \\ (25 th–75 th) & 7.4 (7.2–8.3) & 7.5 (7.2–10.7) & 7.4 (7.2–8.0) & 0.35 \\ Creatinine, mg/dL, median & & & & \\ (25 th–75 th) & 0.94 (0.77–1.02) & 0.9 (0.76–0.96) & 0.94 (0.83–1.02) & 0.26 \\ MDRD eGRF, mL/min/1.73, median & & & & \\ (25 th–75 th) & 87 (81–98) & 87 (81–100) & 97(76–96) & 0.49 \\ Cystatin C, mg/dL median & & & & \\ (25 th–75 th) & 0.95 (0.88–1.08) & 0.88 (0.79–0.90) & 1.04 (0.98–1.22) & \textless{0.001} \\ Low density liporetin, mg/dL & & & & \\ (25 th–75 th) & 136 (106–160) & 148 (127–155) & 132 (102–161) & 0.52 \\ High density lipoprotein, mg/dL & & & & \\ median (25 th–75 th) & 37 (33–46) & 37 (35–39) & 38 (33–47) & 0.72 \\ CRP, ng/L median (25 th–75 th) & 0.40 (0.24–0.67) & 0.52 (0.29–0.95) & 0.34 (0.23–0.47) & 0.05 \\ LVEF, \% median (25 th–75 th) & 57 (50–61) & 59 (48–65) & 57 (61–60) & 0.82 \\
**Procedure** & & & & \\ Multivessel disease-no. (\%) & 14 (45) & 7 (50) & 7 (41) & 0.73 \\ Multivessel treatment & 12 (38) & 7 (50) & 5 (29) & 0.28 \\ DES-no. (\%) & 29 (94) & 14 (100) & 15 (88) & 0.48 \\ \hline \hline \end{tabular}  
* BP = blood pressure; CAD = coronary artery disease; CRP = c-reactive protein; DES = drug eluting stent; LVEF = left ventricle ejection fraction, MDRD eGRF = modified diet in renal disease estimated-glomerular filtration rate, WBC = white blood cell.  
\end{table} 
Table 1: Baseline characteristics.
# Performance Analysis of Ambient Backscatter NOMA Systems
###### Abstract  
This paper analyzed the performance of an ambient-backscatter-(AmBC)-assisted non-orthogonal multiple access (NOMA) system, where a backscatter device (BD) broadcasts its signal to numerous users. More specifically, the realistic assumptions of imperfect successive interference cancellation (ipSIC) and residual hardware impairments (RfIIs) for AmBC\(-\)NOMA systems were taken into consideration. We further derived the closed-form and asymptotic expressions of outage probability for the BD and the \(d\)-th user. Based on the asymptotic expressions, the diversity orders of the BD and the \(d\)-th user were obtained in the high SNR regime. Furthermore, throughput and energy efficiency are further discussed for AmBC-assisted orthogonal multiple access (OMA) systems in the delay-limited transmission model. The numerical results revealed that: (i) AmBC-NOMA systems have the ability to achieve better outage behavior than AmBC-OMA; (ii) due to the existence of the backscatter link, the error floors of outage probability for the BD and the \(d\)-th user appear at a high signal-to-noise ratio; (iii) AmBC-NOMA systems are able to achieve higher energy efficiency and throughput than AmBC-OMA systems.  
ambient backscatter communication; non-orthogonal multiple access; hardware impairment; outage probability +
Footnote †: journal: Applied
## 2 Materials and Methods
### Vegetation Index-Based GPP Model  
The VPM was developed based on the conceptual partitioning of photosynthetically active vegetation and non-photosynthetic vegetation canopy [5]. It has been demonstrated that the VPM can accurately simulate GPP in cold and arid regions [49]. The EVI, LSWI, air temperature, and photosynthetically active radiation (PAR) data were used as inputs to estimate GPP via the VPM [5]. The equation is as follows:  
\[GPP_{VPM} = \varepsilon_{g}\times\textit{IPAR}_{chl}\times\textit{PAR}\ = \ \varepsilon_{g}\times\textit{APAR} \tag{3}\]  
where \(\varepsilon_{g}\) represents LUE (g C M)\({}^{-1}\) APAR); \(PAR\) represents photosynthetically active radiation; APAR is the absorbed photosynthetically active radiation; and \(\textit{fPAR}_{chl}\) is the fraction of \(PAR\) absorbed by chlorophyll. According to a previous study [18], \(\textit{fPAR}_{chl}\) can be calculated as follows:  
\[\textit{fPAR}_{chl}\ =\ (EVI-0.1)\times 1.25 \tag{4}\]  
The VPM employs downregulating scalars (ranging between 0 and 1) to characterize the effects of temperature, water, and leaf phenology on the LUE (\(\varepsilon_{g}\)) as follows:  
\[\varepsilon_{g}\ =\ \varepsilon_{0}\times T_{scalar}\times W_{scalar} \tag{5}\]  
where \(\varepsilon_{0}\) is the maximum LUE (g C M)\({}^{-1}\) APAR). The site-specific \(\varepsilon_{0}\) was obtained from a survey of the literature [49; 50; 51]. \(T_{scalar}\) and \(W_{scalar}\) are the scalars for the effects of temperature and water stress limitations on the LUE of vegetation, respectively.  
\(T_{scalar}\) is estimated from the equation used in the terrestrial ecosystem model (TEM) [52] as follows:  
\[T_{scalar}\ =\ \frac{(T-T_{\min})(T-T_{\max})}{[(T-T_{\min})(T-T_{\max})]-(T-T_{opt} )^{2}} \tag{6}\]  
where \(T\) is the air temperature (\({}^{\circ}\)C) and \(T_{min}\), \(T_{max}\), and \(T_{opt}\) are the minimum, maximum, and optimum temperatures for photosynthesis, respectively. The values of these parameters can be found in the following Table 1[49; 51].  
The _LSWI_ is an indicator of water stress on photosynthesis, which can be calculated as follows:  
\[W_{scalar}\ =\ \frac{1+LSWI}{1+LSWI_{\max}} \tag{7}\]  
where \(LSWI_{max}\) is the maximum _LSWI_ within the growing season for individual pixels.
## 5 The Architecture Design and Combinations of New Devices  
Although the band gap of CdTe NC thin film (\(\sim\)1.45 eV) is in the ideal bandgap region for single junction solar cells according to the Shockley\(-\)Queisser constraint [64], to further improve device performance, a higher \(J_{\text{sc}}\) is preferred. One way to improve the short circuit current of NC solar cells is to introduce more active layers with a lower bandgap. For example, introducing more CdSe\({}_{\text{x}}\)Te\({}_{\text{1-x}}\) NC active layers into the CdTe NC solar cells will extend the spectrum response to the longer wavelength region, as CdSe\({}_{\text{x}}\)Te\({}_{\text{1-x}}\) layers possess a lower bandgap (less than 1.45 eV). There are several ways to attain a CdSe\({}_{\text{x}}\)Te\({}_{\text{1-x}}\) alloy NC active layer. For example, sintering thin films containing mixtures of CdTe and CdSe NC will form CdSe\({}_{\text{x}}\)Te\({}_{\text{1-x}}\) alloy easily due to the low melting point of NC [24; 45]. In this case, a mixture of different compositions of CdTe and CdSe NC is deposited on the substrate and annealed at a temperature of \(\sim\)350 \({}^{\circ}\)C to facilitate the formation of CdSe\({}_{\text{x}}\)Te\({}_{\text{1-x}}\) alloy. Improved device performance is obtained in this case, which is mainly attributed to an increase in \(J_{\text{sc}}\). It is also found that the device performance can be tuned by adjusting the composition of the NC alloy. Later on, the CdSe\({}_{\text{x}}\)Te\({}_{\text{1-x}}\) alloy NC is used directly to replace the mixtures of CdTe and CdSe NCs as the active layer [61]. Here, a homogeneous alloy active layer is obtained through the application of CdSe\({}_{\text{x}}\)Te\({}_{\text{1-x}}\) NC with an ascertained composition. Furthermore, as the physical properties of each active layer can be well controlled, the NC performance can be adjusted easily. As shown in Figure 3a, NC solar cells with a vertically-graded bandgap are formed using the layer-by-layer NC deposition process. In this new device architecture, the active layer consists of two or more alloy NC layers with different bandgaps, which will extend the absorption range. Furthermore, as shown in Figure 3b, the slope of the built-in electric field will facilitate carrier separation and collection. The device performance is found to be related to composition of alloy, as shown in Figure 3c,d. Due to the present of low band gap Cd\({}_{\text{x}}\)Te\({}_{\text{1-x}}\) alloy, enhanced EQE is found in both short and long wavelength when comparing to traditional
## 4 Conclusions  
The Zhayu products fermented with LAB had lower pH values and TVB-N contents than the control, showing better safety qualities. Moreover, LAB-inoculated Zhayu samples contained more water-soluble proteins and crude fats as the inoculation amount exceeded \(10^{8}\) cfu/100 g, and fermentation with LAB decreased the hardness, springiness, and chewiness of Zhayu products, making the products more tender and soft. Additionally, the Zhayu prepared with LAB at the inoculation amount of \(10^{9}\) cfu/100 g presented better safety, nutritional, and textural qualities. LAB could also improve the flavor characteristics of Zhayu products. Zhayu samples fermented with _L. plantarum_ and _P. acidilactici_ presented the strongest sourness, while Zhayu samples prepared with _P. pentosaceus_ showed the highest level of umami taste, consistent with the highest contents of water-soluble proteins and free amino acids with an umami taste (Asp and Glu). For odor characteristics, the addition of LAB decreased the relative contents of aliphatic aldehydes, (Z)-3-hexen-1-ol, and 1-octen-3-ol, subducting the earthy and fishy notes in the Zhayu samples, and the Zhayu samples fermented with _P. acidilactici_ contained the lowest content of 1-octen-3-ol and the weakest earthy notes. In addition, LAB-inoculated fermentation enhanced the contents of terpenoids, acids, esters, and S-containing compounds, increasing the sour, pleasant, and unique odors of the Zhayu products. Especially for samples fermented with _L. plantarum_, the relative contents of acids and esters were the highest. Overall, the inoculation fermentation with LAB increased the acceptability of the comprehensive quality of Zhayu products. In further studies, the three kinds of LAB can be mixed and used together to exploit the advantages of each starter and improve the quality of fermented fish products.  
**Author Contributions:** Conceptualization, Y.H. and X.H.; methodology, X.C. and L.C.; software, Y.A. and X.C.; validation, Y.A., X.C. and L.C.; formal analysis, Y.A. and X.C.; investigation, R.L.; resources, Y.H.; data curation, Y.A.; writing--original draft preparation, Y.A.; writing--review and editing, R.L.; visualization, Y.A.; supervision, X.H.; project administration, S.X.; funding acquisition, S.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (grant number: 2018 YED0901003).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors confirm that they have no conflict of interest with respect to the work described in this manuscript.
# Malicious File Detection Method Using Machine Learning and Interworking with MITRE ATT&CK Framework
## 1 Introduction  
The number of worldwide Internet users is increasing as a result of the development of new 5G and artificial intelligence (AI) technologies. Consequently, malicious codes and cyberattacks that leak user information and cause financial damage are becoming more sophisticated and intelligent. In 91% of the cases, the inflow path of cyberattacks using malicious code starts from spear-phishing emails, and such attacks are initiated through attachments and links containing malicious code [1]. The detection and analysis of existing intrusions and malicious codes have been performed smoothly using the signature-based security control system. However, progressively developing malicious codes cannot be detected and analyzed by the existing security control systems, and false-positive and false-negative detection can increase explosively, making it difficult for administrators to judge and respond. In addition, static malicious code analysis technology is weak in terms of its inability to detect unknown malicious code, and dynamic malicious code analysis technology can be too slow. In addition, as a result of checking the rate at which the information protection systems of some companies and institutions can actually detect cyberattacks, most systems showed a detection rate of 60 to 70%, and about 30% of them had false detection and exception handling. This is a serious problem.  
To respond to this, this paper proposes a method for visualizing malicious file detection data by applying a static-analysis-based machine learning (ML) algorithm to improve the shortcomings and mapping the dynamic analysis results to the MITRE ATT&CK (Adversarial Tactics, Techniques, and Common Knowledge) framework.  
The remainder of this paper is organized as follows. Section 2 reviews the ML methods used in the study and the visualization tool, the MITRE ATT&CK framework. Section 3 describes the experimental methods, and Section 4 contains the experimental results and discussion. The conclusion is presented in Section 5.  
The main aim of this study was to perform a dynamic analysis of malicious and suspicious files and apply the results to the MITER ATT&CK framework to visually present the attack tactics and detailed techniques used for the files. This makes it easier for agents to identify and respond to threats. In addition, it utilizes the advantages of dynamic-analysis-based malicious file detection to increase detection accuracy. Malicious and suspicious files can be detected and analyzed, increasing the reliability of analysis and securing excellent on-site connectivity.
## 1 Introduction
The result revealed that the thermal conductivity of paraffin with 2 wt % MWCNTs was enhanced 35.0% and 45.0% in solid and liquid states. [PERSON] et al. [25] prepared a PCM composite by vacuum impregnation of paraffin (Tm = 50-60 \({}^{\circ}\)C) with graphene oxide (GO) sheets. The result revealed that the thermal conductivity of the composite was improved from 0.305 to 0.985 W/mK. Although high thermal conductivity was easily achieved by incorporating paraffin with thermal conductive fillers, the composites have no mechanical strength, and leakage of paraffin during storage, transportation, and usage is a significant problem. Shape-stabilized phase change material (SSPCM) with a polymer matrix as supporting and encapsulation materials [28; 29; 30] is a good solution for strength and leakage. However, only a few works on thermal conductivity enhancement of SSPCM have been found in the literature [31; 32; 33; 34; 35]. For example, [PERSON] et al. [34] found that expanded graphite in styrene-butadiene-styrene copolymer (SBS)/paraffin composite helped to decrease the heat release time by 61%. [PERSON] et al. [33] found that EG enhanced the thermal conductivity of an HDPE/paraffin composite more than graphite powder (GP). [PERSON] et al. [32] used linear low-density polyethylene, paraffin wax, and expanded graphite (EG) to prepare SSPCM. They found that the thermal conductivity was increased to 1.329 W/mK for SSPCM containing 15 wt % of EG. Although many works have been carried out and the thermal conductivity of paraffin was improved to different extents, relative works on thermal conductivity enhancement of paraffin-based SSPCM are not sufficient. Most of the previous works focused on preparation and thermal property determination, with little discussion on thermal conductivity enhancement mechanisms and the effect of filler(s) dispersion morphology in SSPCM. In addition, most of works were based on paraffinswith a high phase change temperature (50-60 \({}^{\circ}\)C); paraffins with phase change temperatures of 20-30 \({}^{\circ}\)C, which are required for energy-saving buildings, are not concerned. Therefore, in this paper, a two-dimensional (2D) structure filler-expanded graphite plate (EGP) and a one-dimensional (1D) structure filler-multi-wall carbon nanotube (MWCNT) were used to enhance the thermal conductivity of the SSPCM, which was prepared for building applications with good comprehensive properties [29]. The effect of filler dispersion morphology on thermal conductivity was investigated. Furthermore, a synergistic effect between EGP and MWCNT fillers were observed, and the synergistic mechanism was discussed.
## 2 System Model  
Figure 1 demonstrates the UAV-assisted CCS deployed in a hydropower IoT, where the UAV serves as the transmitter of covert messages, providing covert communication services to terminal devices. According to [20; 21], NOMA can be used for multi-connectivity, high-speed, and high-reliability scenarios in 5G communication networks to improve performance and efficiency, which can support two devices synchronously accessing the IoT in a hydropower plant. Therefore, in the UAV-assisted CCS with NOMA, two cooperative users, denoted as U1 and U2, receive the covert messages transmitted from the UAV. In addition, there is a monitor denoted as [PERSON], who keeps monitoring the communication between the UAV and legitimate users to detect covert communications.  
In this paper, we adopt a multiuser-based NOMA power allocation method that can utilize different channel gains among multiplexed users. First, we use the NOMA technique to send signals from different users. On the receiver side, successive interference cancellation (SIC) is used to detect the desired signal. Second, the power allocation of the user depends on the distance between the user and the UAV, which means the user with a far distance receives more transmit power than the user with a normal distance. Then, based on the above power allocation method, the power of user one and user two is jointly optimized. In addition, we use the finite LTMB for the UAV to transmit covert messages to the terminals, which is assumed to be \(N\). Since NOMA is adopted as the access mode for terminals, \(N\) sub-channels are used by the UAV to transmit covert messages. Add to that, we assume that the UAV knows [PERSON]'s position.  
Without loss of generality, we assume that U1 is located far from the UAV and close to [PERSON], called weak NOMA user, and that U2 is close to the UAV and far from [PERSON], called strong NOMA user. Since the rate of the channel between the UAV and U2 is higher than the rate between UAV and U1, the transmit power of the UAV of the signal to the U2 is lower than that of U1. In this scenario, the UAV transmits the covert messages to strong NOMA users, i.e., U2, and U1 is selected as a jammer to jam [PERSON] to detect the covert communication between the UAV and U2. In hydro-power IoT, U1, U2, and Willie are sensors or devices, equipped with a single antenna.
## 3 Bridge Element Maintenance Decision Optimization Model
### Sequential Decision Making Model for Bridge Project-Level Maintenance Decisions
#### 3.2.5 Weibull Distribution for the Duration of States  
Compared to traditional Markov processes, in a semi-Markov process, the random variable \(T\), which represents the duration of staying in a state, follows a probability distribution. Let \(T_{1}\),\(T_{2}\),\(\ldots\),\(T_{4}\) represent the random variable for the duration of bridge elements in state \(i\)(\(i\) = 1,2,3,4). It is assumed that duration \(T_{i}\) in any state \(i\) follows a two-parameter Weibull probability distribution, which is denoted as \(T_{i}\sim Weibull\left(b_{i},\frac{1}{a_{i}}\right)\). Therefore, we have the following:  
\[F_{i}(t)=Pr[T_{i}\leq t]=1-e^{-(a_{i}t)^{b_{i}}} \tag{8}\]  
\[S_{i}(t)=\Pr[T_{i}>t]=1-F_{i}(t)=e^{-(a_{i}t)^{b_{i}}} \tag{9}\]  
\[f_{i}(t)=\frac{\delta F_{i}(t)}{\delta t}=a_{i}b_{i}(a_{i}t)^{b_{i}-1}e^{-(a_{ i}t)^{b_{i}}}, \tag{10}\]  
where \(F_{i}(t)\) represents the Cumulative Distribution Function (CDF) associated with the duration, which refers to the probability that an element remains in state \(i\) for less than time \(t\). \(S_{i}(t)\) represents the Survival Function (SF), also known as the cumulative survival rate, where the probability that an element remains in state \(i\) for longer than time t is indicated. \(f_{i}(t)\) represents the Probability Density Function (PDF) associated with the duration, which describes the probability density of the duration in state \(i\) at time \(t\).  
By utilizing the historical inspection data of bridges, it is possible to fit the parameters \(a_{i}\) and \(b_{i}\) for different states \(i\). According to the properties of the Weibull distribution, the duration \(T_{i}\) of staying in each state of the elements can be calculated by substituting the values into Equation (11):  
\[T_{i}=-\frac{(\ln(u))^{\frac{1}{b_{i}}}}{a_{i}} \tag{11}\]  
Assuming that, without any maintenance, the elements can only degrade by one condition level, it can be assumed that the duration of staying in state s for an element is equal to the time it takes for the element to transition from state \(s\) to state \(s^{\prime}\), where \(s^{\prime}=s+1\). \(T(s,s^{\prime},t^{\prime})\) represents the time distribution required to transition from state \(s\) to state \(s^{\prime}\), and \(T(s,s^{\prime},t^{\prime})=\{T_{1},T_{2},T_{3},T_{4},T_{5}\}\). \(T(s,s^{\prime},t^{\prime})\) is used to calculate the state value function, which serves as a guide for determining maintenance priorities, formulating maintenance strategies, and allocating maintenance funds.