esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Machine-Learning Based Security Design for Cyber-Physical Systems
## 6 Conclusions  
Since networks combine the physical system and the computing system, a CPS becomes vulnerable to cyber-physical attacks, which may disrupt and cause malfunctions in a physical system in the real world. Moreover, due to the enhanced connectivity of the networks, the CPS will become large and complex; thus, modeling a complex CPS becomes difficult and inaccurate compared to a real CPS, reducing the security level of conventional model-based cyber-physical security strategies. To guarantee the safety and reliability of large and complex CPSs, it is necessary to adopt not only machine learning techniques but also conventional detection approaches in order to enhance the security level of the CPS.  
This paper presented a comprehensive survey of the threats that damage the CPS and attack-detection strategies based on ML techniques. First, we presented a hierarchical CPS model that abstracts the complex CPS structure into three layers of CPS functions: the physical system layer, the network layer, and the application layer. Then, we presented cyber-physical attacks for each CPS layer in terms of attack implementations and examples. In particular, we analyzed various cyber-physical attacks from the perspective of physical system dynamics by introducing linear dynamics. In addition, we presented various ML-based cyber-physical attack-detection strategies and security evaluation metrics of the ML-based strategies, where the hierarchical CPS model was considered in order to detect and handle cyber-physical attacks targeting each layer. Finally, we discussed future research directions from the perspectives of real-time attack detection, resilient CPS design, and dataset generation to defend against cyber-physical attacks. These research directions enhance the security level of the CPS, despite the shortcomings of ML techniques, due to their tremendous computation power and data-driven nature.  
Conceptualization, S.K. and K.-J.P.; investigation, S.K.; writing--original draft preparation, S.K.; writing--review and editing, K.-J.P.; visualization, S.K.; supervision, K.-J.P.; project administration, K.-J.P.; funding acquisition, K.-J.P. All authors have read and agreed to the published version of the manuscript.  
This work has been supported by the Unmanned Swarm CPS Research Laboratory program of the Defense Acquisition Program Administration and Agency for Defense Development (UD190029 ED).  
Not applicable.  
The authors declare no conflict of interest.
## 5 Technical Tips for Performing Effective Superselective cTACE  
### Achievement of Marked Portal Vein Visualization with Iodized Oil During cTACE  
There is a relationship between the grades of portal vein visualization with iodized oil and local tumor recurrence. We divided the grades of portal vein visualization with iodized oil into three grades on spot radiographs obtained during the procedure: grade 0, no visualization; grade 1, visualization adjacent to the tumor; and grade 2, visualization in the whole or extended embolized area, and the local recurrence rates in the grade 2 group were significantly lower than those in the grades 1 and 0 groups (\(p<0.0485\) and \(p<0.0001\), respectively) (Figure 4) [40].  
In superselective cTACE, iodized oil should be slowly injected to avoid \"oil cast formation\" in the arteries. The endpoint of iodized oil injection is grade 1 portal vein visualization, not grade 2, because iodized oil in the tumor and surrounding liver is pushed into the portal vein by GS slurry injection and widely distributes into the whole embolized area, and frequently beyond the embolized area. As a result, grade 2 visualization can be obtained [74]. When the flow of the tumor-feeding branch unexpectedly stops before adequate portal vein visualization, we can adopt two measures: injection of 0.5 \(\upmu\)g of prostaglandin E1 or 0.5 mL of 2% lidocaine through the catheter to increase  
Figure 4: Classification of grades of portal vein visualization with iodized oil and relationship between local tumor recurrence. (**a**) Schematic representation of grades of portal vein visualization with iodized oil; (**b**) Graph shows the local recurrence rates calculated with the Kaplan-Meier method for each portal vein visualization group. The local recurrence rates for tumors in the grade 2 group were significantly lower than those for tumors in the grades 1 and 0 groups (\(p=0.0485\) and \(p<0.0001\), respectively). The local recurrence rates for tumors in the grade 1 group were also significantly lower than those in the grade 0 group (\(p<0.0001\)). Reprinted with permission from Ref. [40].  
arterial flow; and more distal advancement of a microcatheter to achieve a semi-wedged condition [74]. The latter technique should be attempted first, if possible, because it is usually more effective. When the microcatheter is advanced more distally, iodized oil can be re-injected. In such a condition, iodized oil can flow forward due to the injection force, because stretching the catheterized artery and the mass effect of the microcatheter itself can prevent backflow of iodized oil. As a result, the volume of iodized oil passing through the arterioportal and arterial communications can increase and embolic effects can be enhanced.
# A Hybrid Tolerance Design Method for the Active Phased-Array Antenna
## 3 Statistical Analysis of the Performance of the Array Antenna
### Variance Derivation  
The variance of the maximum power density deviation \(\Delta P\) is defined as follows:  
\[\begin{array}{ll}\sigma^{2}&=D(\Delta P_{1}+\Delta P_{2})\\ &=D(\Delta P_{1})+D(\Delta P_{2})+2\text{cov}(\Delta P_{1},\Delta P_{2})\end{array} \tag{18}\]  
Based on the independence of the discrete error \(z_{ij}^{d}\) and continuous error \(z_{ij}^{c}\), the variance of \(D  
The variance of \(D(\Delta P_{2})\) can be derived as follows:  
\[\begin{array}{ll}D(\Delta P_{2})&=\frac{1}{4}D\left(\sum\limits_{i=1}^{m}\sum \limits_{j=1}^{n}\left\{\frac{\partial^{2}P}{\partial z_{ij}^{2}}\Delta z_{ij}^ {2}\right\}\right)\\ &=\frac{1}{4}\left\{\sum\limits_{i=1}^{m}\sum\limits_{j=1}^{n}\left[\left( \frac{\partial^{2}P}{\partial z_{ij}^{2}}\right)^{2}D\left(\Delta z_{ij}^{2} \right)\right]+2\sum\limits_{i=1}^{m}\sum\limits_{j=1}^{n}\sum\limits_{\begin{array} []{c}\sum\limits_{q=1}^{n}\frac{\partial^{2}P}{\partial z_{ij}^{2}}\cdot \frac{\partial^{2}P}{\partial z_{pq}^{2}}\cdot\text{cov}\left(\Delta z_{ij}^{2 },\Delta z_{pq}^{2}\right)\right\}\\ ij\
eq pq\end{array}\right.\end{array} \tag{20}\]  
Based on the conclusion in Appendix C, \(\Delta z_{ij}^{2}\) obeys Gamma distribution and the variance is:  
\[D\left(\Delta z_{ij}^{2}\right)=2\left(\left(\sigma_{ij}^{d}\right)^{2}+\left( \sigma_{ij}^{c}\right)^{2}\right)^{2} \tag{21}\]  
According to the mathematical definition, the covariance is expanded as follows:  
\[cov(\Delta P_{1},\Delta P_{2})=E(\Delta P_{1}\cdot\Delta P_{2})-E(\Delta P_{1} )E(\Delta P_{2}) \tag{22}\]  
For the convenience of description, part of \(\Delta P_{1}\) and \(\Delta P_{2}\) are abbreviated as \(A_{ij}\) and \(B_{ij}\).  
\[A_{ij}=\frac{\partial P}{\partial z_{ij}}\Delta z_{ij},\;B_{ij}=\frac{1}{2} \frac{\partial^{2}P}{\partial z_{ij}^{2}}\Delta z_{ij}^{2} \tag{23}\]  
Expanding the \(\Delta P_{1}\cdot\Delta P_{2}\) as the sum of polynomials:  
\[\begin{array}{llllll}\Delta P_{1}\cdot\Delta P_{2}=&\left\{\begin{array}{ ccccc}A_{11}B_{11}&+&A_{11}B_{12}&+&\cdots&+&A_{11}B_{mn}\\ +A_{12}B_{11}&+&A_{12}B_{12}&+&\cdots&+&A_{12}B_{mn}\\  
The maximum power density locates on the angle \((\theta,\varphi)=(0,0)\). Equation (31) can be solved by the feasible direction method.
## 2 The MCVSK and MVSK Portfolio Optimization Model
### The Skewness and Kurtosis Constraints  
In order to get a more practical result by portfolio optimization model, many scholars try to achieve this goal by adding several objectives and constraints into the model. For example, [PERSON] et al. [18] introduce transaction cost, cardinality constraints and market capitalization into Markowitz MV model as constraints, which is optimized by genetic algorithm. Besides, [PERSON] et al. [3] introduce some real-world constraints, such as cardinality, quantity, pre-assignment, round-lot and class constraints, into Markowitz MV model. In this paper, we mainly consider the skewness and kurtosis constraints.  
About transaction costs, [PERSON] [36] points out that ignore the transaction costs will lead to very ineffective portfolio implementation. Briefly, transaction costs can be used to model a number of costs, such as brokerage fees, bid-ask spreads, taxes, or even fund load. Supposing the transaction cost \(c_{i}\) to be a V-shaped function of the difference between a given portfolio \(x^{o}=\left(x_{1}^{o},\ldots,x_{n}^{o}\right)\) and a new portfolio \(x=\left(x_{1},\ldots,x_{n}\right)\), which is incorporated into the portfolio return. Therefore, the transaction cost of \(i^{th}\) asset and total transaction cost can be expressed as follows [37]:\[c_{i}=k_{i}\mid x_{i}-x_{i}^{o}\mid,i=1,2,\ldots,n \tag{12}\] \[\sum_{i=1}^{n}c_{i}=\sum_{i=1}^{n}k_{i}\mid x_{i}-x_{i}^{o}\mid \tag{13}\]  
Generally speaking, the transaction cost can be modelled as the sum of fixed and variable cost [38]. In this paper, we only consider the situation under a single period, so we assume that the transaction cost is as a relatively exogenous variable.  
[PERSON] et al. [4] point out that higher moments, including skewness and kurtosis, can't be neglected in the portfolio selection, which has great importances of financial economics. As shown by [PERSON] [14], the investor's preference for more skewness to less is consistent with the notion of decreasing absolute risk aversion, because a positive-skewness asset return refers to a right-hand elongated tail of density function of asset return. Let \(\zeta\) be an uncertain variable with finite expected value \(e\), the skewness and kurtosis of \(\zeta\) are respectively given by Equations (14) and (15):  
\[skewness=S\left[\zeta\right]=E\left\{\left(\zeta-e\right)^{3}\right\} \tag{14}\] \[kurtosis=K\left[\zeta\right]=E\left\{\left(\zeta-e\right)^{4}\right\} \tag{15}\]
# Bluetooth-Based Healthcare Information and Medical Resource Management System
## 3 Materials and Methods
### Service Module
#### 3.2.3 Work Coordination Information for Medical Staff  
In addition to the transmission of patient physiological information and positioning information, the presented system will also provide work coordination information among healthcare workers to facilitate real-time communication and dissemination of important information among healthcare workers. Therefore, the Publish/Subscribe of the MQTT protocol is used to divide the information exchange/transmission of medical personnel into two modes: one-to-one message transmission and group message dissemination, and to establish two different names of Group Topic and User Topic according to the different modes. The Topic is differentiated according to the different modes. In the group message dissemination mode, according to the different medical departments in the medical institution, each department is divided into different groups, and each department is given a Group Topic so that medical staff can register (Subscribe) to the Group Topic of their respective medical departments, and then they will be able to publish messages for the specific Group Topic and send the information to the designated medical departments to achieve the purpose of group message publishing. The transmission process is shown in Figure 19.  
On the other hand, one-to-one messaging is mainly used to send messages from a user to a specified recipient. Although it gives a unique User Topic to the user, the same as Group Topic, and users are allowed to register for their own User Topics, each User Topic has only one user registration. Therefore, when a user needs to send a message to a specific object, he/she only needs to publish the message to the User Topic of that specific person. Since the User Topic has only one registrant, that is, the specific person, only the specific person can receive the message, thus achieving the purpose of one-to-one message transmission.
## 3 Results
### Data Acquisition System  
In order to validate the performance of proposed method for a PV system, original and unseen datasets have been recorded as a combination of normal and faulty cases by simulating a PV array in the environment of MATLAB/Simulink according to Figure 1. LL faults and normal conditions have been simulated under numerous scenarios, including different environmental conditions, fault impedances, and various mismatch levels. Environmental conditions cover a wide range of irradiance and temperature changes, in which the irradiance range varies from 200 W/m\({}^{2}\) to 1000 W/m\({}^{2}\), and the temperature range is from 0 \({}^{\circ}\)C to 40 \({}^{\circ}\)C. Moreover, the range of fault impedance is set to [0 \(\Omega\), 25 \(\Omega\)] with a step of 5 \(\Omega\), and the mismatch level varies from 10% to 50% with an increment of 10%. In this research, 433 normal conditions and 570 cases of LL faults have been recorded under the aforementioned scenario's operation.  
After completing the learning process, each layer has been tested by an unseen dataset. This dataset was recorded under different conditions and scenarios from the original dataset. These conditions are the combinations of irradiance levels (350, 550, and 850 W/m\({}^{2}\)), operation temperatures (4, 7, 12, 17, 22, and 27 \({}^{\circ}\)C), fault impedances (3, 7, 12, and 17 \(\Omega\)), and the mismatch level varies from 10% to 50% with an increment of 10%. Therefore, 60 normal conditions and 120 cases of LL faults are obtained by simulation setup in order to make an accurate and reliable assessment of the trained model.
## 3 Results and Discussion
### Fatty Acid Composition in lentil Wholenael Samples  
The results of fatty acid profiles in lentil samples are presented in Table 5. Linoleic acid (C18:2) was the most abundant fatty acid in all of the lentil samples, ranging, on average, from 41.3 to 50.3%. The least abundant fatty acid detected in all samples was saturated stearic acid (C18:0), ranging, on average, from 1.63 to 3.29%. Palmitic acid's (C16:0) relative concentration in the SMF and SSF LR groups (SMFLR24, SMFLR48, SSFLR24, and SSFLR48) was not significantly different from the respective control groups, while SMFLR24, SSFLR24, and SSFLR48 samples contained significantly higher percentages of C16:0, in comparison with respective control. Relative concentrations of C18:0 in SMF and SSF LR samples were not significantly different from the respective control groups (except in SMFLR48 samples). Moreover, C18:0 relative concentrations in SMF and SSF L samples were significantly higher only in the SMFLR48, SMFLR24, and SSFLR48 samples, in comparison to respective control groups. Only the SMFLR24 samples contained significantly lower relative percentages of 9-octadecenoic acid (C18:1), in comparison with the respective control group, while other fermented samples did not show significant differences. Similarly, the C18:1 concentration in fermented L samples did not show significant differences when compared to the respective control groups. Furthermore, only SMFLR24 samples contained a significantly different relative concentration of C18:2, in comparison to the respective controls. \(\alpha\)-linolenic acid's (C18:3 \(\alpha\)) relative concentration in SMF and SSF LR samples was not different from the respective controls. On the other hand, its relative concentrations in SMFLR24, SMFLR24, SSFLR24, and SSFLR48 sample groups were lower than those in the respective controls. Tetradecanoic acid (C14:0), eicosanoic acid (C20:0), and _cis_-11-eicosenoic acid (C20:1) were only detected in fermented sample groups. The factor of growing conditions was statistically significant in almost all the content of fatty acids in lentils but C16:0 and C18:0 (see supplementary file: Table S8). The type of fermentation was a statistically significant factor for almost all fatty acids content except C18:0 and C18:2. Fermentation duration was a statistically significant factor for most fatty acids content except C18:0, C18:1, and C18:2. The factor interactions (growing conditions * type of fermentation and type of fermentation * fermentation duration) were shown to be statistically significant for the most of the fatty acids, but not for C18:1 and C18:2, while the growing conditions * fermentation duration factor's interaction was only not statistically significant for C18:1 content in lentils. Furthermore, the interaction of the factors growing conditions * type of fermentation * fermentation duration was statistically significant for all fatty acid content in lentils. A weak positive correlation was found between C18:0 concentration and LAB viable counts (r = 0.363, \(p\) = 0.030) in lentils (see supplementary file: Table S7).
## 1 Introduction
The standard deviation of the usage of the memory is 4.6 MiB. Equation (3) below (see also Figure 15 in Section 3) shows the dependence of the memory required by our algorithm on the total number of cities \(n\). As we can observe, this dependence is linear:  
\[RAM=0.0000685n+0.563\;\;MiB. \tag{3}\]  
Our algorithm consists of the constructive, the insertion and the improvement phases, we call it the Constructive, Insertion, and Improvement algorithm, the _CII_-algorithm, for short. The constructive heuristics of Phase 1 deliver a partial tour that includes solely the points of the girding polygon. The insertion heuristic of Phase 2 completes the partial tour of Phase 1 to a complete feasible tour using the cheapest insertion strategy: iteratively, the current partial tour is augmented with a new point, one yielding the minimal increase in the cost in an auxiliary, specially formed tour. We use simple geometry in the decision-making process at Phases 2 and 3. The tour improvement heuristic of Phase 3 improves iteratively the tour of Phase 2 based on the local optimality conditions: it uses two heuristic algorithms which carry out some local rearrangement of the current tour. At Phase 1, the girding polygon for the points of set \(V\) and an initial, yet infeasible (partial) tour including the vertices of that polygon is constructed in time \(O(n^{2})\). The initial tour of Phase 1 is iteratively extended with the new points from the internal area of the polygon at Phase 2. Phase 2 also runs in time \(O(n^{2})\) and basically uses the triangle inequality for the selection of each newly added point. Phase 3 uses two heuristic algorithms. The first one, called _2-Opt_, is a local search algorithm proposed by [PERSON] [13]. The second one is based on the procedure of Phase 2. The two heuristics are repeatedly applied in the iterative improvement cycle until a special approximation condition is satisfied. The number of repetitions in the improvement cycle, in practice, is bounded by a small constant. In particular, the average number of the repetitions for all the tested instances was about 9 (the maximum of 49 repetitions was attained for one of the moderate sized instances _1 ra498378_, and for the largest instance _1 rb744710_ with 744,710 points, Phase 3 was repeated 18 times). The rest of the paper is organized as follows. In Section 2, we describe the _CII_-algorithm and show its time complexity. In Section 3, we give the implementation details and the results of our computational experiments, and, in Section 4, we give some concluding remarks and possible directions for the future work. The tables presented in Appendix A contain the complete data of our computational results.
## 3 LEO Satellite Network Model
### System Architecture
Regarding the routing problem of the LEO satellite network transmission network, the topology changes in the feeder link part are much more frequent than those in the inter-satellite link, and a ground station has many feeder links with a small bandwidth, so the feeder link and the inter-satellite link routing problems are not considered as a whole, and instead, the feeder link selection issue is considered based on the inter-satellite link routing. Figure 1: Schematic diagram of polar-orbiting satellite constellation.
## 3 Results
### Subacute Antidiabetic Study  
Figure 2 shows the effect of prolonged administration of DHEAM (100 and 200 mg/kg per day) on the FBG level of alloxan-induced diabetic mice.  
From the first week of the study, the two doses of DHEAM successfully controlled the diabetic status, prevented hyperglycemia, and sustained a rapid and substantial reduction (\(p<0.001\)) (day 7: 36.36%, 74.40%, 72.67%; day 14: 41.46%, 79.79%, 80.20%; day 21: 61.68%, 82.17%, 78.48%; day 28: 74.52%, 82.00%, 81.22%, for glibenclamide, doses 100 and 200, respectively) calculated in comparison to the negative control group.  
Table 2 outlines the evolution of bodyweight and the biochemical parameters of the different study groups. Throughout the experimental period, a significant decrease in bodyweight was observed in the untreated diabetic group as opposed to the normal group, and at the end of the fourth week, this drop was very significant (\(p<0.001\)). The diabetic groups treated with DHEAM and glibenclamide (2 mg/kg) for four weeks significantly improved in their bodyweight compared to the diabetic control group (\(p<0.001\)).  
The ASAT (aspartate aminotransferase) and ALAT (alanine aminotransferase) levels were highly elevated in diabetic control mice compared with the normal control group. The effect of the administration of DHEAM (100 and 200 mg/kg bodyweight) to alloxan-induced diabetic mice for 28 days decreases their activities significantly compared to the diabetic control group. An increased level of urea and creatinine were also noted for the alloxan-induced non treated diabetic mice after four weeks as compared to the normal control group. However, the treatment of diabetic mice with DHEAM significantly reduced their creatinine and urea levels compared to the diabetic group.  
Figure 3 demonstrates the DHEAM's antihyperglycemic activity. Compared to the positive control (Glibenclamide), the first hour didn't show a significant reduction in BGL (Blood glucose level). Starting from the hour and a half, the extract BGL reached the positive control values until the sixth hour (\(281\pm 17.8\) mg/dL (Positive control) \(300\pm 23.0\) mg/dL for DHEAM at 100 mg/kg and \(254\pm 26.0\) mg/dL for DHEAM at 200 mg/kg). After 12 h, the positive control blood glucose values increased against a significant decrease (\(p<0.001\)) in those with DHEAM doses, which reflect the long-term effect and the power of the extract.  
Figure 2: The effect of DHEAM on mice FBG (Fasting blood glucose) during the four weeks of the study. Values are expressed as mean \(\pm\) SD (n = 5 mice). \(***\)\(p<0.001\) compared to diabetic control.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Treatment**} & \multicolumn{6}{c}{**Bodyweight Development (g)**} & \multicolumn{6}{c}{**Biochemical Parameter**} \\ \cline{2-9}  & **1 st Day** & **1 st Week** & **2 nd Week** & **3 rd Week** & **4 th Week** & **ASATU/L** & **ALAT(U/L)** & **Urea