esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Real-Time Fluorescent Quantitative PCR of P. nicotiana  
On the 5 th and 15 th day after potting, rhizosphere soil of the tobacco seedlings was sampled, and the population of _P. nicotiana_ was detected by real-time fluorescent quantitative PCR (RT qPCR). According to the 185 rDNA gene sequence of _P. nicotiana_ from the NCBI database, the following specific primers were designed for fluorescent quantitative PCR: SP: 5'-TGAAGAACGCCTGGAACTGC-3', AP: 5'-CTGACATCTCTCCACCGACTA-3'. The length of the amplified target fragment was 172 bp [4]. DNA was extracted and purified from rhizosphere soil by a DNEasy(r)PowerSoil(r)Kit ((Qiagen, Hilden, Germany).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multicolumn{3}{c}{**Orthogonal Treatment Design**} & \multirow{2}{*}{**pH (Day 0)**} & \multirow{2}{*}{**pH (Day 7)**} & \multirow{2}{*}{**Colony Density c**} \\ \cline{2-2} \cline{6-6}  & **PAS 4 (\%)** & & & & & & **(cfu/g)** \\ \hline
1 & 50 & 15 & 15 & 6.33 \(\pm\) 0.00 e & 6.82 \(\pm\) 0.01 d & 8 \(\times\) 10\({}^{8}\) d \\
2 & 50 & 10 & 30 & 6.54 \(\pm\) 0.00 b & 6.93 \(\pm\) 0.01 b & 5 \(\times\) 10\({}^{8}\) d \\
3 & 50 & 5 & 45 & 6.65 \(\pm\) 0.01 a & 6.96 \(\pm\) 0.01 b & 1.7 \(\times\) 10\({}^{10}\) a \\
4 & 60 & 15 & 45 & 6.64 \(\pm\) 0.00 a & 7.05 \(\pm\) 0.01 a & 4.1 \(\times\) 10\({}^{9}\) b \\
5 & 60 & 10 & 15 & 6.39 \(\pm\) 0.01 de & 7.02 \(\pm\) 0.02 a & 5.9 \(\times\) 10\({}^{9}\) b \\
6 & 60 & 5 & 30 & 6.55 \(\pm\) 0.00 b & 6.93 \(\pm\) 0.00 b & 2.8 \(\times\) 10\({}^{9}\) c \\
7 & 70 & 15 & 30 & 6.52 \(\pm\) 0.00 bc & 6.78 \(\pm\) 0.02 e & 4.4 \(\times\) 10\({}^{9}\) b \\
8 & 70 & 10 & 15 & 6.37 \(\pm\) 0.00 de & 6.87 \(\pm\) 0.00 c & 2.4 \(\times\) 10\({}^{9}\) c \\
9 & 70 & 5 & 45 & 6.45 \(\pm\) 0.09 cd & 6.89 \(\pm\) 0.02 c & 1.6 \(\times\) 10\({}^{10}\) a \\
10 & 0 & 100 & 0 & 6.38 \(\pm\) 0.02 de & 6.12 \(\pm\) 0.02 g & 1.4 \(\times\) 10\({}^{10}\) a \\
11 & 100 & 0 & 0 & 6.13 \(\pm\) 0.01 f & 6.17 \(\pm\) 0.01 f & 1.8 \(\times\) 10\({}^{4}\) e \\ \hline \hline \end{tabular}  
* a, prickly ash seeds; b, soybean powder; c, the colony density of Tpb55 on the fourth day of fermentation. Different letters within a column indicate significant differences among biochar-amended soils at \(p<0.05\).  
\end{table}
Table 1: The results of PAS fermentation with _B. subtilis_ inoculation and biochar addition.  
The RT-qPCR reaction system had a total volume of 20 \(\upmu\)L, consisting of 2.0 \(\upmu\)L cDNA, 10.0 \(\upmu\)L SYBR premix (Takara, Japan), 0.4 \(\upmu\)LP SP (10 \(\upmu\)M/\(\upmu\)L), 0.4 \(\upmu\)LP AP (10 \(\upmu\)M/\(\upmu\)L), 0.4 \(\upmu\)LROX Reference DyeII (50\(\times\)) and 6.8 \(\upmu\)LddH\({}_{2}\)O. The amplification procedure was as follows: 94 \({}^{\circ}\)C for 5 min, 94 \({}^{\circ}\)C for 20 s, 65 \({}^{\circ}\)C for 40 s, and 72 \({}^{\circ}\)C for 40 s, with a total of 40 cycles.
# FAmesh: Generating Frequency Adaptive Meshes from Single Images under 2D Hole Constraints
## 3 Method
### Overview  
Our goal is to recover smooth meshes with complex topology and fine details from a single RGBA image. In this paper, the mesh is represented as a graph \(G=(V,E,F)\), where \(V=\left\{v_{i}\in\mathbb{R}^{3}\right\}_{i=1}^{N}\) is the set of \(N\) vertices of the mesh. \(E\) is the set of directed edges connecting adjacent vertices, and \(F\) is the set of faces of the mesh. We gradually fit the target surface \(S\) by deforming a 0-genus sphere mesh \(G_{0}\) by using a progressive structure, where the template \(G_{0}\) has 2562 vertices. Our pipeline is depicted in Figure 3, and we adopt an encode-decode architecture that transitions from global to local. The whole framework consists of the following five parts: image feature extractor, deformation block, pruning module, extend patch extract module and local refinement module. The image feature extractor maps the RGBA image \(I\) to the latent layer vector \(h\), and the MLP-based global deformation block performs topological deformation on \(G_{0}\) to generate a coarse mesh. Notably, we use a mesh pruning module under 2D hole constraints during the global deformation stage to generate meshes with complex topologies. Next, we use an extended patch extract module to separate the coarse mesh into sets of high-curvature local patches \(G_{sub}^{h}=\left\{g_{i}^{h}\right\}_{i=1}^{n}\) and sets of low-curvature local patches \(G_{sub}^{l}=\left\{g_{i}^{l}\right\}_{i=1}^{n}\). \(g_{i}^{h}\) and \(g_{i}^{l}\) represent the \(i\)-th high-curvature and \(i\)-th low-curvature patches, respectively. Meanwhile, the frame transitions from global shape recovery to local refinement. Finally, we utilize the dual-branch local refinement module to perform fine-tuning.
## 3 Results
### Changes in Performance of Colloid Mill Depending on Rotor Speed and Knife Design  
This study examined the dependence of the colloid mill's poultry meat-and-bone material grinding performance on both rotor speed (1000, 2000, 3000, and 4000 rpm) and the type of working tools (control and experimental). All experiments were conducted with a fixed gap of 0.1 mm using a constant initial raw material mass of 5 kg. The obtained data on grinding time, mass flow rate, and volumetric flow rate are presented in Tables 2 and 3.  
A comparative analysis of grinding time and productivity using the control (45\({}^{\circ}\) tooth angle, 1.5 mm pitch) and experimental (20\({}^{\circ}\) tooth angle, 0.5 mm pitch) working tools showed that in both cases, increasing the rotor speed from 1000 to 3000 rpm led to a noticeable reduction in processing time and an increase in mass flow rate. In the control setup, the grinding time decreased from 68 to 41 s, and the mass flow rate increased from  
Figure 2: Main working elements of colloid mill.  
264.71 to 439.02 kg/h; in the experimental setup, the grinding time was reduced from 75 to 45 s, and the mass flow rate rose from 240 to 400 kg/h (Table 2).  
However, further increasing the rotor speed to 4000 rpm did not significantly accelerate the grinding process (42 s in the control and 48 s in the experimental configuration), and the mass flow rate either slightly decreased (from 439.02 to 428.57 kg/h in the control) or showed no substantial growth (remaining within approximately 375-400 kg/h in the experimental version).  
A similar trend was observed for the volumetric flow rate: in the control setup, it increased from 0.27 to 0.45 m\({}^{3}\)/h at 1000-3000 rpm and slightly decreased to 0.44 m\({}^{3}\)/h at 4000 rpm; a comparable peak at medium speeds was noted in the experimental setup as well (Table 3).  
Thus, in both the control and experimental configurations, the greatest increase in productivity and reduction in grinding time were achieved when the rotor speed was increased from 1000 to 3000 rpm. Further increases, resulting in speeds of up to 4000 rpm, did not result in significant improvements in performance. On the contrary, in some cases, the mass flow rate slightly decreased, likely due to increased turbulence, flow redistribution, and possible \"slippage\" of larger particles. Statistical analysis (\(p<0.05\)) confirmed that the differences in efficiency between 3000 and 4000 rpm were not significant; however, at 4000 rpm, the risk of tool wear and thermal heating of the product increases. Based on the obtained results, a rotor speed of 3000 rpm can be considered an optimal operating condition, as further increases to 4000 rpm do not yield statistically significant improvements in productivity or grinding fineness (\(p<0.05\)), while energy consumption, tool wear, and heat generation are likely to rise [16].
## 4 Cost Functions  
The goal is to derive cost functions for the individual TES technologies that express total storage costs in terms of storage capacity and maximum heat load which can be used in the MILP/MIQP model presented in Section 3. For this reason, a predefined number of storage configurations in terms of geometries, thermal capacities and heat loads are calculated and evaluated. A detailed description for the technology-specific calculation of these configurations is presented in the following sections. Costs are calculated for every configuration using information from a cost database and from the literature. Suboptimal configurations in terms of total costs are eliminated. Suboptimal in this case means, that there are other storage configurations that have either at least the same maximum heat load at equal capacity but at lower total costs. A least squares fit is carried out for the remaining optimal configurations resulting in the desired cost function. In the case of a linear function the cost-function can be written as:  
\[C_{s}=c_{s,0}+c_{s,1}C+c_{s,2}L, \tag{26}\]  
or in the case of a quadratic function  
\[C_{s}=c_{s,0}+c_{s,1}C+c_{s,2}L+c_{s,3}CL+c_{s,4}C^{2}+c_{s,5}L^{2}, \tag{27}\]  
where \(C_{s}\) is the storage costs, \(C\) is the storage capacity, \(L\) is the maximum storage heat load and \(c_{s,1...5}\) are the cost coefficients.  
The equipment considered within the individual cost functions and the parameters that impact the specific cost drivers is listed in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **Ruths Steam** & **Latent Heat Thermal** & **Molten Salt** & **Concrete** \\  & & **Storage** & **Energy Storage** & **Storage** & **Storage** \\ \hline Heat storage & & & max./min. & & \\ material & PCM, salt, concrete & - & temperature, volume & volume & volume \\ Steel tubes [18] & Seamless, stainless steel & - & tube diameter, tube & - & tube diameter, tube length \\ Steel plates [18] & S234 JR & - & surface area & - & - \\ E-motors [19] & - & - & - & heat load & - \\ Pumps [18] & Single stage, cast iron & - & - & heat load & - \\ Vertical storage & & & - & volume & - \\ tanks [18] & Cone roof, carbon steel & - & - & volume & - \\ Cylindrical storage & & volume, required & & & \\ vessels [18] & Carbon steel & wall thickness & - & - & - \\ Heat exchangers [18] & U-Type, Stainless steel & - & - & heat load & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Components and key variables that impact the respective component costs for the selected thermal energy storage (TES) technologies.
# Towards Hyper-Dimensional Variography Using the Product-Sum Covariance Model
## 2 Theory  
Modeling a variogram typically proceeds by creating a raw variogram, binning and averaging the raw variogram by distance to obtain an experimental (empirical) variogram, and then fitting a theoretical variogram to the experimental variogram (e.g., [6; 26]). A valid theoretical variogram model exhibits certain mathematical properties, i.e., non-negativity, conditionally negative-definite function (see [1] for details), strict positive-definiteness [15; 21], etc.  
Two main difficulties arise in modeling spatio-temporal and other hyper-dimensional correlations: (a) how to ensure one has a valid model [21; 27], and (b) how to fit the model to data [22]. An overview of the available classes of spatio-temporal covariance models is given in [PERSON] et al. [3] and [11]. Possible models include metric [28], linear [29], product [30], non-separable [31], and generalized product-sum models [22]. Here we adopt the product-sum model, which has recently gained popularity due to several advantages (e.g., sequential modeling, computational feasibility) it offers for environmental applications such as satellite observations [26; 32; 33; 34].  
In what follows, the terms covariance and variogram are used interchangeably due to a simple relationship between them [1]:  
\[C_{h}(h)=C_{h}(0)-\gamma_{h}(h) \tag{1}\]  
where \(C_{h}\) denotes covariance and \(\gamma_{h}\) variogram. To simplify notation, we will use symbol \"V\" for variogram and \"COV\" for covariance, and the number in the superscript next to the symbols will denote their dimensionality: V\({}^{(1)}\) stands for one-dimensional, V\({}^{(2)}\) two-dimensional variogram, etc., with corresponding covariance COV\({}^{(1)}\), COV\({}^{(2)}\), etc. Common spatio-temporal modeling (only horizontal spatial and temporal distance considered) belongs to V\({}^{(2)}\) class.
# Hybrid FSK-FDM Scheme for Data Rate Enhancement in Dual-Function Radar and Communication
## 5 Simulation Results
### Example 4: Probability of Target Detection  
In this subsection, we discuss the probability of target detection at different SNR levels. A single target is considered in this case, located at \(\theta_{r}=0^{\circ}\) with \(M_{R}=10\) antenna elements. The receiver operating characteristic (ROC) curves are calculated between the probability of detection \(\textbf{P}_{d}\) and the probability of a false alarm \(\textbf{P}_{FA}\) at different SNR levels.  
Mathematically, ROC can be calculated as (12)  
\[\textbf{P}_{d}=\frac{1}{2}[erfc(erfc^{-1}(2P_{FA})-\sqrt{\chi})]. \tag{12}\]  
where \(\chi\) is the SNR.  
Figure 14 shows the detection probability at an SNR of \(-5\) dB and \(5\) dB. Finally, it is observed that embedding information into the radar emission does not affect radar operation.  
A comparison of the proposed technique with other existing techniques in terms of BER vs. SNR are provided in Table 3.  
Figure 14: The receiver operating characteristic curves of the proposed scheme.  
Figure 13: The proposed composite FDM signal received at the radar receiver.  
A total of \(10^{6}\) bits were transmitted. The security against intercepts is presented in Table 4. As can be seen in the table, the proposed technique outperforms the other techniques.  
Implementing an FSK-FDM-based DFRC transmitter requires special attention to the SNR since it directly impacts the data rate. Based on simulation results, it is clear that the proposed scheme performs well at higher SNR values. Moreover, we have addressed jamming and the computational complexity of the system at the communication and radar receivers. The proposed system achieves a better trade-off between jamming robustness and data rate than other systems. It also has lower computational complexity, making it a cost-effective solution. Finally, it offers greater flexibility in terms of modulation format and data rate.
# An Effective Ensemble Approach for Preventing and Detecting Phishing Attacks in Textual Form
## 2 Related Work
### Identification of Phishing Emails Employing Analysis of Textual Characteristics
Using this method, 71% of the phishing emails that Sophos had previously incorrectly classified were properly recognized. It also achieved a 15% false positive rate (FPR). Twenty-six features were retrieved by [PERSON] and [PERSON] (2018) by taking word counts, punctuation, and stopwords into account. Then, different machine learning classifiers used these attributes as inputs. With a true positive rate (TPR) of 83% and a true negative rate (TNR) of 96%, Support Vector Machine (SVM) performed the best. The main drawback of [PERSON] and [PERSON] (2018) is that their feature sets are weak and easily manipulated because they mainly rely on terminating words, punctuation counts, word variations, and feature ratios. The utilization of Recurrent Neural Networks (RNNs) by the authors [PERSON] et al. (2019) enabled the identification of phishing emails through the utilization of text-based features. The RNN classifier's efficacy was evaluated against the \"text analysis\" model. The authors' inadequate disclosure of the phishing emails used in the study is the source of this approach's flaws. As a result, the technique is unaffected and continues to be able to identify phishing emails in real time. Additionally, it is still unclear if the authors tested the method using actual use case data that was unbalanced. [PERSON] et al. (2019) employed domain-level attributes, encompassing the most frequently occurring terms and a list of special characters, as well as text-based features acquired through the utilization of the TF-IDF approach. Among the various classifiers evaluated, the Logistic Regression classifier achieved the highest F1 score of 98%. There are two limitations of this investigation. Firstly, the dataset employed necessitates updating, thus hindering the accuracy and relevance of the obtained outcomes. Secondly, the vulnerability of the domain-level characteristics utilized in the technique renders them susceptible to being easily bypassed, thereby diminishing the strategy's overall effectiveness. In a separate investigation, [PERSON] et al. (2019) conducted a comparison between the TF-IDF approach and Doc2 Vec, a word-embedding methodology derived from Word2 Vec, to ascertain their respective efficacy in extracting text-based attributes from emails. Through experimentation with various machine learning techniques, the authors concluded that the utilization of Doc2 Vec leads to improved detection performance. However, a notable drawback of this study is the possibility that the achieved accuracy of 88.4% may be higher when compared to similar studies conducted in this field.
## 4 Results
### Modeling Layer
#### 4.2.2 Building and Operating System of a Digital Twin Space  
To secure the interoperability of the 3D model data in this study, the IFC standard, which is the BIM standard format, was applied; to secure the interoperability of the digital twin of an underground utility tunnel, this study proposes the OGC standard (OGC 3D Tiles Specification)-based 3D spatial data model (DTS-DM) for linking BIM data and GIS data. Furthermore, a real-time digital twin space service is supported based on a 3D spatial data model according to the OGC 3D tiles standard, and an open platform that can utilize various application services was developed.  
The digital twin spatial data model DTS-DM proposed in this study consists of a database schema that can store BIM data for effective inquiry and search in large-scale BIM data and GIS data, as well as prompt screen displays for the interoperability of BIM and GIS data. The DTS-DM model, which was built based on the OGC 3D Tiles Specification standard, is a BIM/GIS spatial data model that can express exterior representation objects, the LOD concept of CityGML (GIS), and information on interior building elements of IFC. Figure 7 shows the concept of DTS-DM.  
DTS-DM defines and expresses an underground utility tunnel (UCA) region as a space model. Each space model consists of the exterior model of such elements as ceiling and wall, and the interior model of such elements as stairs, doors, and internal structures. Furthermore, linear facilities such as water supply lines, communications lines, and power lines that are crucial facilities of an underground utility tunnel are defined as lifeline models, while sensor devices such as CCTV are defined as sensor models.  
To build a digital twin of an underground utility tunnel, a DTS-DM-based digital twin space is built and a platform system is established for real-time services; the overall flow of the system service is shown in Figure 8. The platform system receives the original data of the underground utility tunnel model in the IFC form and then converts it to the DTS-DM digital twin spatial data model for providing services to users. Service data converted by DTS-DM largely consist of OGC 3D tiles, which have the hierarchical file structure per LOD, spatial shape for renewal, and database for storing properties. Users can perform a high-speed search by using the metadata (index structure) of a 3D tile structure and can then use the services of a digital twin space.
# Resource Mapping Allocation Scheme in 6G Satellite Twin Network
## 5 Serverless-Based Decentralized Simulation Development Model  
In this section, we describe the design of the three-dimensional hierarchical simulation framework and the decentralized adaptive development model. The component architecture and application process of the 6G satellite twin network are introduced.
## 3 Methodology--Mathematical Modelling and Heuristic Optimization Method
#### 3.1.2 Real-Time Logistics Resource Optimization
As the figure shows, the assigned new supply-demands have a great impact on the performance of supply routes. \[EC^{NSP}=\sum_{r=1}^{r_{max}}\sum_{\psi=1}^{\psi_{max}}x_{rA\psi}^{P}\Big{(} \xi_{s_{rA-1}\psi}\Big{(}p_{s_{rA-1}}^{x},p_{s_{rA-1}}^{y},p_{\psi}^{x},p_{\psi }^{y}\Big{)}+\xi_{s_{rA}\psi}\Big{(}p_{s_{rA}}^{x},p_{s_{rA}}^{y},p_{\psi}^{x},p _{\psi}^{y}\Big{)}\Big{)}e_{r} \tag{21}\]0 The structure of the mathematical model including parameters, local and global constraint and decision variables. However, without constraints of time frame and capacity the new supply-demand can be assigned to the nearest matrix cells, but in the case of time and capacity related constraints the assignment and scheduling is an NP-hard optimization problem, therefore we suggest a heuristic approach to solve it. Heuristic Optimization for Extended and Real-Time Logistics Resource Optimization Based on Black Hole Algorithm  
Within the frame of this part of the article, a multiphase optimization algorithm is described. The algorithm includes an extended and real-time optimization phase. Within the extended phase, the in-plant supply of known supply-demands of the matrix production system is optimized. This extended optimization includes the clustering of supply-demands, the routing and the scheduling of clustered supply-demands. The second phase of the optimization is the real-time rerouting and rescheduling, where the existing routes are redesigned in order to insert new supply-demands into the existing routes. The objective function of the optimization algorithm includes time-related aspects, while time-frames and capacities are taken into consideration. The algorithm makes it possible to analyze the solutions from a sustainability point of view because greenhouse gas emission can be calculated. Within the frame of performance analysis, various heuristic algorithms are tested to measure their efficiency. Table 1 shows the results of this performance analysis. As the performance analysis shows, the results of black hole and flower pollination algorithms are comparable with genetic and harmony search algorithms.