esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Routing Protocols
### Proposed Two-Level Phantom with a Backbone Route Protocol (PhaT)
#### 4.2.1 Phase 0: Network Configuration
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Meaning** \\ \hline \(epN\) & Existing first level phantom node adopted from TreeR \\ \(npN\) & New second level phantom node \\ \(d_{NB}\) & Distance between the inner and outer boundaries of the near network border region \\ \(d_{SB}\) & Distance from a sensor node to the network border \\ \(N_{NB}\) & Nodes which are located in the near network border regions \\ \(RR\) & Restricted region around \(epN\) \\ \(d_{P}\) & Hop distance between \(epN\) and \(npN\) \\ \(T\) & A bias threshold value \\ \(gN\) & Initial node of the backbone route, neighbor of \(npN\) \\ \(SF\) & A random selection factor distributed between [0, 1] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Notations used in the proposed two-level phantom with a backbone route (PhaT) protocol. The protocol employs the phantom nodes in the near network border regions to ensure effectively long and diversified routing paths. To control the communication overhead of the protocol, the near network border regions must be configured according to the network topology to ensure the routing paths are not excessively long. If the network size is very large, the distance between phantom nodes and the sink node may be excessively long and the protocol may incur high communication overhead. To route packets, the protocol operates in three phases as shown in Algorithm 2.
## 2 Materials and Methods
### Site Effect
#### 2.3.1 Theoretical HVSR  
The site effects can also be estimated using predictive theoretical formulas. The H/V ground motion spectrum was calculated for the one-dimensional model of horizontal layers, considering vertically propagating shear waves. Such an assumption results from analyzing seismograms from the research area, where S-type waves transmit most seismic energy. Each layer is homogeneous and isotropic and is characterized by thickness, density, shear modulus, and damping coefficient. In our cases, the shear velocity to calculate shear modulus was taken from seismic surveys and the presented geological profiles (Figure 1d,f). In this study, we applied the formula from the paper of [PERSON] [4]. The theoretical calculation uses dependencies between acoustic impedance, absorption/attenuation coefficient, soft layer thickness, shear velocity in soft and rigid layers, and the shock-wave frequency. The maximum value of the calculated H/V ratio corresponds to the amplification coefficient and the expected resonance frequency. This theoretical relation assumes that the layers are infinite in their lateral extent and, hence, should be considered a steady-state approximation of the site effect parameters [41; 42]. The HVSR method tends to underestimate the value of  
Figure 3: Motion directions in: (**a**) translation and (**b**) rotation, and (**c**) an ideal sketch of amplification effect in a soft layer.  
the amplification coefficient of the ground motion in comparison to the classic standard spectral ratio [43; 44]. Moreover, the HVSR spectrum can present more than one significant peak, leading the subjective analysis to accept the first (fundamental) or highest peak as the determinable resonance frequency [44]. Therefore, the theoretical models were estimated to avoid potential inaccuracies in analyzing the HVSR result.
## 5 Conclusions  
Energy efficiency and network lifetime are two major attributes in the design of a network routing algorithm. Constructing an energy-efficient protocol that distributes the network load is a challenge. Z-SEP is a useful algorithm for this, however it still has some weak points. The extended zonal stable election protocol (EZ-SEP) is a two level heterogeneous, hierarchical cluster-based routing protocol that provides a modified cluster head selection technique as well as nodes communication with the sink. Thus, the nodes in EZ-SEP transfer data to their BS in a hybrid manner, meaning normal regular nodes deliver records to the base station directly, whereas extended advanced nodes make use of a clustering mechanism. This is aimed at extending the lifetime of the network by taking into account the network energy dissipation. An improved routing process can be achieved by selecting the cluster head among the nodes based on the remaining residual energy in comparison to the parent Z-SEP protocol. This method increases the period of stability of the network before the death of the first node, as well as improving the number of live nodes in the network. Thus, the packet transmission ratio is enhanced, meaning that a bigger amount of environmental events are monitored, sensed and delivered to the base station. Simulation results show the improvement of network performance in parameters like lifetime, packets sent to BS and energy consumption.  
In conclusion, three different protocols like SEP, LEACH and Z-SEP have been observed and compared to the proposed EZ-SEP algorithm, where EZ-SEP demonstrates better performance reducing the dead nodes by 48%, increasing the packet delivery ratio by 16% and decreasing the average power consumption by 34%.
## 4 Conclusions  
The results of the work above show that all the unconventional protein sources tested have high nutritional value and can be considered for designing new high-protein food products, especially fermented food products. In particular, it is worth considering the addition of edible insects, algae, SCP, potato, or rice preparations to fermented products, as fermentation can further enhance the nutritional value by increasing protein digestibility. Each should be treated individually for the best and optimal use of protein preparations. All are characterized by a high protein content per 100 g of product but vary in the content of other components such as fatty acids, carbohydrates, or the presence of particular elements. For this reason, each of the studied preparations may find different applications in the food industry and play a different role as a high-protein superfood. Potato protein, rice protein, and SCP performed best in terms of protein content compared to WPC80, while edible insect and algae protein had lower overall protein content. Nonetheless, insects and algae can also be comparable sources of protein if subjected to technological processes that reduce fat and carbohydrate content and increase protein digestibility through fermentation. For a complete view of the quality of the proteins discussed in the paper, further studies should be conducted to test the suitability of these preparations after implementation into food products, including fermented ones.  
Conceptualization, A.S.G. and B.G.S.; methodology, A.S.G. and B.G.S.; software, A.S.G. and B.G.S.; validation, B.G.S.; formal analysis, A.S.G.; investigation, A.S.G.; resources, B.G.S.; data curation, A.S.G.; writing--original draft preparation, A.S.G.; writing--review and editing, B.G.S.; visualization, A.S.G.; supervision, B.G.S.; project administration, A.S.G. and B.G.S.; funding acquisition, A.S.G. and B.G.S. All authors have read and agreed to the published version of the manuscript.  
This research was financed from the science grant (Individual Research Plan--IPB) granted by the Sector of the University of Life Sciences in Lublin via Doctoral School--research theme number SzD/13/TZ/2020.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Toxin**} & \multicolumn{2}{c}{**Edible**} & \multirow{2}{*}{**Algae**} & \multirow{2}{*}{**SCP**} & \multirow{2}{*}{**Potato**} & \multirow{2}{*}{**Rice**} & \multicolumn{2}{c}{**WPC80**} \\  & & & & & & & **(Control)** \\ \cline{2-7}  & & & & & & & \\ \hline Ochratoxin A & 1.60 & \textless{1.50 }\textasci{1.50 }\text
# A Simulated Annealing Algorithm for the Generalized Quadratic Assignment Problem
## 2 Mathematical Models
### Combinatorial Optimization Problem Model  
The GQAP is a type of COP because it's solution can be represented as a finite set of locations, where locations may be repeated, since multiple facilities may be assigned to each location. More specifically, the solution, \(\mathbf{S}\), to the GQAP can be defined as follows where _s(i) = k_, which indicates that facility \(i\) is assigned to location \(k\).  
\[S=\{s(1),\ s(2),\ldots,\ s(M)\} \tag{8}\]  
Since each facility \(i\) = 1, 2,\(\ldots\), \(M\) can be assigned to either location 1, 2,\(\ldots\), or \(N\) (i.e., _s(i) = 1, 2,\(\ldots\), or \(N\)_), there are \(N^{M}\) possible solutions. However, many of the solutions may be infeasible because the capacity of one or more locations may be exceeded. For example, consider a solution \(\mathbf{S}\) = {2, 1, 3, 3, 2} for a GQAP instance where five facilities (\(M\) = 5) are assigned to three locations (\(N\) = 3). In this solution, facilities 1 and 5 are assigned to location 2 (i.e., _s(1) = _s(5) = 2)_, facility 2 to location 1, and facilities 3 and 4 are assigned to location 3. Notice multiple facilities are assigned to locations 2 and 3. For the solution to be feasible, the capacity of each location must not be exceeded. This can be checked by using the capacity constraints below. For this GQAP instance, there are a total of 243 (35) solutions in which many may be infeasible. Nevertheless, using the same notation defined previously for the input data, the COP model for the GQAP consist of solution representation (8) and the following objective function and capacity constraints.  
\[\text{Minimize}\ TC(\mathbf{S})=\sum_{i=1}^{M}a_{is(i)}+\sum_{i=1}^{M}\sum_{ j=1}^{M}c_{ijs(i)s(j)}f_{ij}d_{s(i)s(j)} \tag{9}\]  
Subject to  
\[\sum_{\forall i,\ s,\ s(i)=k}r_{i}\leq C_{k}\ \ \forall k \tag{10}\]Equation (8) gives the assignment of facilities to locations. Objective function (9) is used to determine the minimum total cost (i.e., the sum of the assignment and transportation costs) of the solution, and Constraint (10) is used to check the feasibility of the solution. That is, it ensures that the capacity of each location is not exceeded.  
The COP model above can be solved optimally using complete enumeration. In other words, the feasibility of all \(N^{M}\) solutions can be checked using Constraint (10). Then, each feasible solution can be evaluated using Objective function (9), and the solution \(S^{\star}\) that gives the minimum total cost is the optimal solution. As with the MILP model presented in Section 2.1, only small-sized problems can be solved in reasonable computational time using the COP model and complete enumeration. Therefore, heuristic algorithms are developed for the GQAP using the COP model, but next an illustrative example is presented and is solved using the proposed MILP model.
## 4 Taillight Detection and Light Signal Identification Based on Image Processing
### Taillight Detection
#### 4.2.3 Correlation Detection Based on Color  
To further reduce possible false detections in the color correlation restriction conditions, a correlation detection on the pixel cluster pairs satisfying the conditions is carried out. Pixel cluster pairs are extracted from the original image. The linear correlation coefficient formula is used, as follows:  
\[r(X,Y)=\frac{Cov(X,Y)}{\sqrt{Var[X]Var[Y]}} \tag{5}\]  
The sum of the correlation coefficient vectors of the mean value of the three monochrome channels (red, green, and blue channels) in the candidate taillight pair region is calculated to determine whether taillight pairs belong to the same vehicle \(Var[X]\) is the variance of \(X\). \(Var[Y]\) is the variance of \(Y\). \(Cov(X,Y)\) is the covariance of \(X\) and \(Y\). The calculation method is expressed as:  
\[Cor(X,Y)=E[[X-E(X)][Y-E(Y)]] \tag{6}\]  
\(\mathrm{E}(X)\) represents the expectations of component \(X\). \(\mathrm{E}(Y)\) represents the expectations of component \(Y\).  
Figure 11: Symmetry condition in the vertical direction.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Taillight** & **Gradient\_x** & **Gradient\_y** \\ \hline Picture (a)\_left\(\backslash\)Picture (a)\_right & 0.9646 & 0.9884 \\ Picture (a)\_left\(\backslash\)Picture (a)\_other & 0.8917 & 0.9380 \\ Picture (a)\_right\(\backslash\)Picture (a)\_other & 0.9178 & 0.9638 \\ Picture (b)\_left\(\backslash\)Picture (b)\_right & 0.9184 & 0.9642 \\ Picture (b)\_left\(\backslash\)Picture (b)\_other & 0.8432 & 0.7691 \\ Picture (b)\_right\(\backslash\)Picture (b)\_other & 0.8461 & 0.7943 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Correlation coefficient for the taillight candidate regions.  
Figure 10: Centroid of the taillight areas.  
Using the original image based on the RGB color space to calculate the sum of the correlation of R, G, and B channels, and setting the dynamic threshold to \(a_{colour}\), as follows:  
\[r(R_{1},R_{2})+r(G_{1},G_{2})+r(B_{1},B_{2})<\alpha_{colour} \tag{7}\]  
\(a_{colour}\) represents the dynamic threshold.  
If the sum of the correlation is less than the threshold, the two regions are recognized as the correct taillight matching items.  
The taillight detection method using the positional relationship constraint alone and the taillight detection method used in this article are compared as shown in Figure 12. The taillight detection method using the positional relationship constraint alone cannot effectively eliminate the interference points that are close in positional relationship. The method used in this paper can effectively eliminate the interference with similar position and accurately detect the taillight.
## 2 Materials and Methods
### Setup for Steam Gasification of OS  
Figure 2 shows the configuration of the once-through setup for steam pyrolysis of OS with a capacity of 300 kg/h from the city of Angarsk (Russia) installed at the production facilities of the Eco-Friendly Fuel and Energy Company (EFEC, Angarsk, Russia).  
Before pyrolysis, OS was loaded into loading unit (1). This component contains a surface-type heat exchanger for OS heating to reduce viscosity and remove large inclusions. After that, the heated OS was transported to the transfer chamber of steam pyrolysis reactor (2) using an injection package. Water steam was generated and superheated using steam boiler (3) with a combined (by fuel type) burner. Diesel (4) and non-condensable gas derived from steam pyrolysis were used as fuel. The economic efficiency of the setup can also be increased with solid fuel (coal, biomass, and semi-coke obtained as a result of oil sludge processing) used as primary organic fuel.  
The resulting steam (with a temperature of 650 \({}^{\circ}\)C and a flow rate of 150 kg/h) was supplied to reactor (2) as an agent, which initiated the OS steam pyrolysis. The OS heating rate was about 50 \({}^{\circ}\)C/min, which corresponds to the slow pyrolysis process. The maximum temperature of the OS sample was about 620 \({}^{\circ}\)C. The pyrolysis process can be generally divided into two stages: evaporation of external moisture and hydrocarbons with a low boiling point and thermal decomposition of high-molecular compounds with the formation of gaseous products. Based on the OS heating rate and the maximum attainable temperature inside the sample, the time of the active pyrolysis process (the second stage) proceeded on average for 45 minutes.  
Figure 1: Methods structure of the study.  
OS transportation in the reactor was performed using a belt conveyor made of heat-resistant steel with the rotation frequency controlled by the electric drive.  
The steam-gas mixture formed as a result of the OS pyrolysis was continuously removed from the reactor and supplied to condensing devices (6) and (7). During primary condensation of the steam-gas mixture (6), heavier fractions--bitumen--were separated. At the second condensation stage (7), the liquid hydrocarbons formed and the water (agent used) were separated. Next, liquid hydrocarbons as the target product of OS processing were discharged into a separate container. After that, water from storage tank (8) and non-condensable gas-phase pyrolysis products were supplied to the burner of steam boiler (3). Combustion products from the steam boiler entered the heating jacket of steam pyrolysis reactor (2) and then were removed to a scrubber through a smoke exhauster to absorb dust and various gas-phase oxides.  
The resulting solid-phase products of steam pyrolysis of OS (semi-coke) were removed through the reactor transfer chamber and cooled discharging loader (10) into the storage tank.  
Quantitative composition of the non-condensable gas-phase products of steam pyrolysis was assessed using a once-through gas analyzer Test-1 (BONER, Novosibirsk, Russia).
# Required Evidence for Clinical Applications of Liquid Biopsy Using Especially CTCs in Lung Cancer
## 4 Required Steps before Clinical Implementation Can Be Considered  
While some may already be convinced that CTCs and ctDNA are ready for clinical application, many physicians are hesitant. Studies that compare CTCs and ctDNA with conventional methods  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author (Year)** & **Measurement Method** & **Population** & **Outcome** \\ \hline [PERSON] (2011) [99] & Cellsearch and ISET § & 210 NSCLC patients undergoing surgery, stage I-IV &
\begin{tabular}{c} Cellsearch (\(\geq\)1 CTC), 82/10 positive (97\%) \\ BST (\(\geq\)1 CTC), 10/42/10 positive (97\%) \\ Both methods independently associated with \\ diminished DPS \\ \(\geq\)2 CTCs 21 patients (21\%) \\ CTCs 25 CTCs baseline and treatment CTCs correlated with \\ MSE \(\leqare few [12; 13; 14]. Most studies are relatively small and often from a single center. Large, prospective multicenter comparative trials are lacking. Secondly, standard operating procedures for collecting and analyzing samples differ between centers. Thirdly, none have ever tested whether CTCs and ctDNA can actually be used to change clinical decisions. If we consider plasma ctDNA, however, there are many studies that prove the value of targeted next-generation sequencing for genes that are targetable with specific tyrosine kinase inhibitors [32; 52]. These are already implemented in clinical practice. ctDNA can also be used to assess the presence of minimal residual disease (MRD), as in hematology. For hematology it has been shown that early interventions increase survival when based on MRD, indicating a monitoring role for cfDNA.  
ctDNA tests are predictive, while CTCs are mainly prognostic. Therefore, physicians will refuse to implement liquid biopsies for CTCs. Only in a few malignancies have studies shown that CTCs can predict whether patients will not benefit from therapies [36; 53; 54; 55; 56; 57]. Unfortunately, it is still unknown whether a switch to therapies should be implemented and would lead to a better outcome.  
Another option for CTCs could be as a replacement for conventional biopsies (diagnosis) but in NSCLC CTCs are simply too rare, unless larger volumes of blood can be screened. Single-cell next generation sequencing may determine the specific DNA or even RNA aberrations that may have a clinical impact. These technologies are evolving but are not ready for the clinic.  
All in all, liquid biopsies are used for ctDNA that finds its application in the clinic for targeted therapies, and for CTCs mainly as a prognostic biomarker.  
Writing/review/editing: [PERSON]. [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Deceptive Jamming Detection for SAR Based on Cross-Track Interferometry
## 5 Experimental Analysis  
In this section, to validate the robustness of our method for deceptive jamming detection, simulation results are provided based on the TanDEM-X system [35].
## 3 Case Study  
### A Case of FREEDM System  
The new method is tested on the Future Renewable Electric Energy Delivery and Management (FREEDM) System, which is a practical form of the envisioned concept of smart grid. The FREEDM system is characterized by its ability of integrating highly distributed and scalable alternative generating sources and storage with existing power systems. Therefore, it has been seen as an efficient electric power grid to facilitate a green and sustainable energy-based society and mitigate the growing energy crisis [29]. According to the architecture of FREEDM system, this paper provides an example of grid-connected local framework of FREEDM system as in Figure 6.  
Figure 5: Flow chart of procedure for a single time of simulation.  
Figure 6: An local framework example of the FREEDM system.  
The plug-and-play interface includes a 400 V direct current (DC) bus and a conventional 120 V alternating current (AC) bus, combined with an open-standard-based communication interface. Loads and modular DGs can be accessed and controlled through the standardized plug-and-play interface. Energy router, which is also regarded as an intelligent energy management (IEM) device, connects to the 12 kV AC distribution bus and supports the regulated 120 V AC and 400 V DC buses at the same time. IEMs are regarded as the intelligent substations in the FREEDM system. It recognizes and manages all devices connected to the low-voltage AC and DC buses, including distributed energy storage devices, distributed renewable energy resources and the loads. Intelligent fault management (IFM) devices are used to isolate potential faults in the 12 kV primary circuit, providing the reconfiguration capability and uninterrupted power quality to the user. Through a reliable and secure communication network, IEMs and IEMs can exchange information with each other.  
The settings of priorities and capacity of the loads in the local framework of FREEDM system is given in Table 1. The DGs includes three storage batteries with the power of 5 kW, four diesel generators with the power of 10 kW, a set of PV array with maximum output power of 5 kW, a set of wind turbine also with a maximum 5 kW output power.
## 2 Materials and Methods
### Statistical Analysis  
#### 2.3.1 Mann-Kendall Trend Test  
The Mann-Kendall (MK) test is a rank-based non-parametric test for detecting a monotonic trend in a time series. The method was originally used by [PERSON] (1945) [48], and the test-statistic distribution was subsequently derived by [PERSON] (1975) [49]. It is frequently used for trend detection in hydrologic research [50]. In this study, we use the MK test to detect monotonic trends in the GPP data series as the test is more powerful than ordinary parametric trend tests for non-normally distributed series [51].  
The MK test statistics (\(S\)) for a series \(x_{1},x_{2},\ldots,x_{n}\) can be given by  
\[sign(x_{j}-x_{i})=\left\{\begin{array}{c}1;if\ x_{j}>x_{i}\\ 0;if\ x_{j}=x_{i}\\ -1;\ if\ x_{j}<x_{i}\end{array}\right., \tag{2}\]  
\[S=\sum_{i=1}^{N-1}\sum_{j=i+1_{-}}^{N}sign(x_{j}-x_{i}) \tag{3}\]  
where \(N\) is the length of the dataset and \(x_{i}\) and \(x_{j}\) denote the data values at times \(i\) and \(j\). A negative value of \(S\) indicates a decreasing trend, while a positive value indicates an increasing trend [52]. Statistical significance of the trend is checked using the variance of the MK statistics, while \(N>10\), which is calculated as  
\[Var(s)=\frac{n(n-1)(2n+5)-\sum_{j=1}^{p}t_{i}(t_{i}-1)(2t_{i}+5)}{18}, \tag{4}\]  
where \(P\) is the number of tied groups, the summary sign (\(\Sigma\)) denotes the summation over all tied groups, and \(t_{i}\) is the number of data in the \(i\)th (tied) group. If the tied groups are not available, this summation process is excluded from the equation.  
The standard \(Z\) value of the test statistics can be estimated by the following formula.  
\[Z=\left\{\begin{array}{c}\frac{S-1}{\sqrt{Var(S)}};\ \ \ if\ S>0\\ 0;\ \ \ if\ S=0\\ \frac{S+1}{\sqrt{Var(S)}};\ \ if\ S<0\end{array}\right.. \tag{5}\]  
If the computed standard \(Z\) value is greater than the critical value of the standard normal distribution at the specified significance level (\(\alpha\)), it is considered to be significant trend, and the null hypothesis is rejected.