esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results  
### Experiment Setup  
To comprehensively evaluate the performance and robustness of our proposed method, we conducted experiments on synthetic datasets representing gene regulatory networks (GRNs) of two different network sizes \(N=50\) and \(N=100\). These sizes were chosen to reflect small-to-intermediate complex biological systems, allowing us to examine the scalability of our method under different conditions. For each network size, we generated \(10\) structurally distinct ground truth networks using the Barabasi-Albert (BA) model [57], which is a widely used generative model that captures the scale-free properties commonly observed in real-world biological networks. This resulted in a total of 20 unique ground truth networks used in our experiments. For each ground truth network, we simulated discretized gene expression data over \(K\) observed time steps using a dynamic model outlined in Algorithms S1 and S2 (see Supplementary Materials). These simulated time-series datasets serve as the foundation for evaluating our method's ability to infer network structure from temporal gene expression patterns. Two levels of discretization were considered to reflect different degrees of resolution in experimental measurements, namely binary (two states: 0 or 1) and ternary (three states: 0, 1, or 2). This allowed us to assess the method's sensitivity and effectiveness across datasets with varying levels of granularity. Our method takes the observed gene expression data over the initial \(K\) time steps as input and synthesizes the gene expression values for the remaining \((T-K)\) time steps, where the total length of the time-series is fixed at \(T=100\). The ability to generate synthetic future data is a critical component of our approach, as it allows us to enrich the dataset and potentially improve inference accuracy, especially in cases where only a limited number of observations are available. To systematically investigate the influence of the number of observed time steps on inference performance, we varied \(K\) from 10 to 90 in increments of 10. This setup enables us to explore how the quantity of available temporal data affects both the quality of synthetic data generation and the subsequent network inference. We applied the network inference algorithm, MIDNI, to both the original and synthesized datasets. The inferred networks were then compared against the corresponding ground truth networks using the evaluation metrics explained in Section 2.3. Performance was analyzed separately for datasets discretized into ternary levels (see Figure 2) and binary levels (see Figure 3). Comprehensive results are provided in Tables S1 and S2 (see Supplementary Materials). These comparisons provide insight into how well our method reconstructs the underlying network structure under different data conditions and discretization schemes.
## 3 Results and Discussion
### Microbiological Quality  
The inclusion of MOLP did not affect the microbial properties of the ground beef during cold storage. The results show that the microbial population significantly increased during the storage days. Treated ground beef samples had lower microbiological counts at day 0 and increased with storage time from day 5 to 15, as indicated in Table 3. This could be attributed to the low dosage of MOLP, which was not enough to impart an antimicrobial effect on microbial growth. However, treated ground beef samples had lower microbial count during storage days in comparison with the control. The decrease in microbial count could be attributed to MOLP being an excellent source of phytochemicals such as flavonoids and phenolic acids, which are used as antimicrobials agents [57]. _Morringa deifera_ has a strong antimicrobial activity, with this being in line with the work of [PERSON] [58], which notes that MO quantitative phytochemical screening showed that it contains alkaloids, flavonoids, cyanogenic glycosides, tannins, and saponins that can be successfully used to reduce and eventually destroy microbes in appropriate dosages. Moreover, internal factors such as high protein and fat content, together with various external factors such as temperature and oxygen, which influence the behaviour of bacteria in food system as well as acting synergistically with preservatives such as antimicrobials, might also have contributed to the low microbial count in treated ground beef samples [59]. [PERSON] [60] reported a wide spectrum of antimicrobial action in MO that works against most bacteria (Gram-positive and Gram-negative). Increasing MO level had a good influence because the antimicrobial mechanisms of phenol compounds rely on their level. Moreover, the coliform bacteria group, yeast, and mould could be reduced by decreasing free water. This is caused by the increased water binding ability of MOLP, which retards their growth [61]. The inclusion of MOLP in ground beef significantly (\(p\leq 0.05\)) decreased the coliforms in comparison with the control sample, demonstrating the protective role of MOLP, which could improve the safety of ground beef during cold storage. Nevertheless,
# A Realistic Approach of the Maximum Work Extraction from Solar Thermal Collectors
## 3 Suggested Model
### Mathematical Description
#### 3.2.3 Total System Energy Balance  
The system efficiency is the ratio of the produced work to the available solar irradiation. It is easily proved that this parameter is the product of thermal engine efficiency and collector efficiency. By using the proper symbols, Equation (16) is the final version of the system efficiency. Parameter (\(X\)) is the ratio of the receiver temperature to the ambient temperature, according to Equation (17).  
\[\eta_{sys}=\frac{W}{Q_{solar}}=\eta_{th}\cdot\eta_{coll}=\eta_{opt}\cdot \left(1-\frac{1}{X}\right)\cdot\left[1-p\cdot\left(X^{4}-1\right)\right] \tag{16}\]  
\[X=\frac{T_{r}}{T_{amb}} \tag{17}\]  
Parameter (\(p\)) is a characteristic of the collector that is dependent on its quality and the ambient conditions. Parameter (\(z\)) shows its quality; high values of this collector lead quality collector, while low values mean low quality. These quantities are determined below by Equations (18) and (19).  
These equations have been defined properly in order to simplify the present analysis and to present the results (and the methodology) in a brief way.  
\[p=\frac{\varepsilon_{r}\cdot\sigma\cdot T_{amb}^{4}}{C\cdot G\cdot\eta_{opt}}= \frac{1}{z}\cdot\frac{\sigma\cdot T_{amb}^{4}}{G} \tag{18}\]  
\[z=\frac{C\cdot\eta_{opt}}{\varepsilon_{r}} \tag{19}\]  
An important result of this analysis is that the optimum performance of the system is dependent on only one parameter (\(p\)). This parameter expresses the losses to the environment to the energy delivered by the sun. Small values of this parameter mean low performance due to operating conditions and design configuration.
# Resilience Assessment for the Northern Sea Route Based on a Fuzzy Bayesian Network
## 3 Model Established for NSR Resilience Measurement
### Resilience Capacity Decomposition for the Arctic Northeast Route
#### 3.3.2 Adaptive Capacity
\begin{table}
\begin{tabular}{c c} \hline \hline
**Features** & **Description** \\ \hline Ice-breaking capacity & The ice-breaking capacity allows the vessels to adapt to a risk or disruption caused by floating ice in ice-covered waters along the NSR. The ice-breaking capacity can be improved by strengthening the hull to bear ice loads for safe navigation in ice fields [70]. \\ \hline Information prediction & Information prediction services include those for sea ice coverage, thickness, and motion, as well as weather conditions [1]. Under the conditions of predictable information, in the case of a disruption, seafarers and operators can take action in advance, for example, by anchoring to avoid a disruption or risk, changing the designed route, or requesting assistance from an icebreaker. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Adaptive capacity of NSR resilience.
## 3 The Obtained Data and Their Discussion
### Wear Testing  
The wear rate for Al/B4C and Al/TiC nanocomposites is extremely dependent on the reinforcing nanoparticles and their concentrations. The increase in wt.% of B4C and TiC leads to decreased wear rate (increasing wear resistance), this is due to the bindings at the interfaces between the reinforcing nanoparticles and Al particles, and then creating thermal stresses at their interfaces because of the differences in melting points of the reinforcing nanoparticles and aluminum particles, and then these thermal stresses will increase the dislocations interlocking.  
The reinforcing nanoparticles will inhibit the movement of dislocations and increase wear resistance (decreasing wear rate). Figure 6 shows that the increment of B4C will decrease the wear rate more than TiC, which is in line with [33]. The increment in applied loading at a constant time will increase the wear rate of Al/B4C and TiC. Increasing the friction between the sample (Al/B4C, Al/TiC) and the disc will increase the temperature and then create thin oxidized films. Afterwards, thin oxidation film will be fragmented and grooved at worn surfaces; this is in agreement with [39]. Figure 6 depicts the relationships between wear rate and applied loading for different wt.% of nanoparticles. The increment in applied loading will increase the wear rate for all samples. The wear rate of nanocomposites reinforcing by B4C is lower than for TiC and pure Al particles as a matrix for many reasons mentioned previously.  
Figure 4: (**a**) Show the experimental and (**b**) Show the experimental and theoretical densities vs. wt.% of B4C and theoretical densities vs. wt.% of TiC.  
Figure 5: Show the porosity (%) vs. wt.% of B4C and TiC.  
The surface topography of nanocomposites was analyzed by an ordinary optic microscope (OM), as shown in Figure 7, which depicts the grooves of the surfaces for Al/B4C and Al/TiC nanocomposites. The grooves of Al/B4C nanocomposites are finer than the grooves of Al/TiC nanocomposites because the B4C nanoparticles are harder than the TiC nanoparticles. Adhesive and abrasive wear are created for all the specimens. At lower loads, abrasive wear is the main wear mechanism, while at higher loads, adhesive wear is the major wear mechanism. For Al/B4C nanocomposites and Al/TiC nanocomposites, there are adhesive wear and abrasive wear will be created. This agrees with [27].
# Optimal View Estimation Algorithm and Evaluation with Deviation Angle Analysis
## 3 Methods
### Accuracy Evaluation of Estimated Viewpoint  
During the model testing phase, or when comparing image-based viewpoint estimation methods, the accuracy of image-based viewpoint estimation is also evaluated using Equations (8) and (9). The value of the parameter \(\delta_{0}\) can be specified by the user based on the number of viewpoints and error tolerance. For example, \(\delta_{0}=\pi/6\).
## 2 Materials and Methods
Each condition was tested with five specimens. The erosion rate is defined by the total mass of the removed material divided by that of the eroded particles hitting the surface, showing the erosion rate changing at different angles [25; 26]. The phase of as-manufactured (F) was analyzed with cold rolling (FR) and the two-stage heat treatment after cold rolling (FRH) of the CCDR Al-Si alloy, as well as the phase transformation induced by particle erosion wear on the specimen surface. The F, FR, and FRH groups were named the FE, FRE, and FRHE groups, respectively, after the erosion wear. An X-ray diffractometer (Bruker AXS GmbH, Karlsruhe, Germany) detected the phase structure of the surfaces of all the specimens to clarify the particle erosion mechanism. Moreover, to further investigate the surface crystal structure and fine grains caused by erosion-induced phase transformation, the focused ion beam (FEI Helios G3 CX, Houston, TX, USA) cutting a cross-section of FRH with the best wear resistance under a high angle was surveyed by transmission electron microscopy (TEM) (JEOL JEM-2100F, Tokyo, Japan) [25; 26; 33].
## 3 Background and Related Work
### Continuous Improvement Process Framework Vis4 Sec  
Continuous improvement is applied in several standards, such as the International Organization for Standardization (ISO)/International Electrotechnical Commission (IEC) 27001 [47], and, hence, is an established topic with fewer current publications having a focus on security. [PERSON] and [PERSON] [48] summarized how to measure security, whereas [PERSON] et al. [49] measured the effectiveness of information security controls. [PERSON] et al. [50] emphasized the involvement of employees. [PERSON] et al. [51] assessed security during the migration of virtual machines. In contrast, [PERSON] et al. [52] proposed tool support to continuously improve security. [PERSON] [53] described how to better integrate continuous improvement into security monitoring by utilizing post-incident activities.  
The process framework Vis4 Sec for security visualization. introduced in [3; 54] and adapted for Linux server authentication in [6], enhances the Deming cycle [10] by visualization. It supports the generation of an overview and the manageability of an organization's ICT, its processes, selected security-specific tasks, and the data they rely on. Vis4 Sec thereby generates knowledge for various stakeholders and the organization through transfer and transformation. In consequence, it consists of the following states and phases (see Figure 3):  
**Initiation:**: Information about the environment, requirements, stakeholders, and planned actions are gathered.
**Question Phase:**: The question is driven by the goal of the current iteration; for example, the result of a security incident or the goal to improve security.
**Data Management Phase:**: Required data and its sources are identified, and a data model is utilized. The data is then collected and analyzed. This step consists of the phases: (1) define data; (2) acquire data; (3) analyze data; (4) ensure data quality; and (5) dispose or reuse data (see Figure 3).
**Visualization Phase:**: The data is visualized to provide actionable information in an overview. This might be stakeholder-dependent.
**Interaction Phase:**: The data source quality and stakeholder-specific visualizations are presented, leading to communication between the stakeholders.  
During iterations, postponed issues are reviewed and related questions, data sources, visualizations, and interactions are refined, based on feedback.
# RF Exposure Assessment by Drone-Based Technology
## 2 Materials and Methods
### Personal Exposimeter  
To measure the RF E-field, we used the personal exposimeter (PEM) EME SPY 200 (Microwave Vision Group, Courtaboeuf, France) [16]. It is lightweight and portable, with dimensions of 168.5 mm \(\times\) 79 mm \(\times\) 49.7 mm and a weight of 440 g. It consists of three orthogonal electric-field probes that continuously measure in 20 pre-defined frequency bands within the range of 87 MHz-5.85 GHz. The device can store up to 80,000 measurement points with a sampling interval between 4 and 255 s. It has a button to mark data points during measurements. It also integrates a GPS to geo-reference the locations where data are collected. Table 1 lists the technical specifications of this PEM, including frequency bands, sensitivity, and standard uncertainty due to axial isotropy in the vertical and horizontal planes for each band.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Frequency Band**} & \multirow{2}{*}{\begin{tabular}{c} **F\({}_{\text{min}}\)** \\ **(MHz)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **F\({}_{\text{max}}\)** \\ **(MHz)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Sensitivity,** \\ **L (V/m)** \\ \end{tabular} } & \multicolumn{2}{c}{**Axial Isotropy**} \\ \cline{4-5}  & & & & **Vertical** & **Horizontal** \\  & & & & **(\(\pm\)dB)** & **(\(\pm\)dB)** \\ \hline FM & 87 & 107 & 0.01 & 0.1 & 1.8 \\ TV3 & 174 & 223 & 0.01 & 0.5 & 1.5 \\ TETRA I & 380 & 400 & 0.01 & 0.2 & 1.5 \\ TETRA II & 410 & 430 & 0.01 & 0.5 & 0.6 \\ TETRA III & 450 & 470 & 0.01 & 0.5 & 1.5 \\ TV445 & 479 & 770 & 0.01 & 0.3 & 2.1 \\ LTE 800 DL & 791 & 821 & 0.005 & 0.3 & 1.1 \\ LTE 800 UL & 832 & 862 & 0.005 & 0.3 & 1.0 \\ GSM + UMTS 900 UL & 880 & 915 & 0.005 & 0.3 & 1.0 \\ GSM + UMTS 900 DL & 925 & 960 & 0.005 & 1.0 & 1.1 \\ GSM 1800 UL & 1710 & 1785 & 0.005 & 1.2 & 1.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The technical specifications of the EME SPY 200 exposimeter, including frequency bands, sensitivity, and standard uncertainty due to axial isotropy in the vertical and horizontal planes. UL: uplink; DL: downlink.  
Figure 1: Google Earth image of the Polytechnic School of the University of Extremadura in Cáceres, Spain. The location of the mobile base station antennas and the orientation of their sectors are indicated.
## 3 Methodology
### Data Collection by Mapping IFC  
Data was obtained from a BIM model in IFC data exchange for the implementation of necessary building component properties in the CBR model. Data is extracted from a 3D model through IFC extraction. Building properties are obtained from IFC entities. Each entity contains properties related to the object, which include the object location, geometry, type, and material. From the information extracted from the BIM model, necessary information such as the story, position, size, material,  
Figure 1: Research framework. Industry Foundation Class (IFC); Building information modeling (BIM).  
accessibility code, and duration are stored in the CBR repository. Figure 2 shows that there are many factors to be considered in the estimation of replacement costs. In this study, a window replacement is considered as an example wherein the factors to be taken into consideration are the story; location, size, accessibility, and materials of a window; the installation date; and the manufacturer. All of the aforementioned factors are required to be taken into consideration when identifying the costs and duration for a window.  
Properties such as the window size, material, and location are obtained from the IFC file. The window location includes the details of the building floor, whether the window is interior or exterior, and whether it is placed in a corner or in the middle of a wall. This information is pertinent for accurately estimating window installation costs. The information regarding each building component was parsed from the IFC file. This information is mapped within IFC entities such as IFCWINDOW, IFCDOOR, IFCWALL, and IFCROOF. Each IFC entity begins with an identification number that is referenced to other entities. By following the identification numbers within an IFC entity such as IFCSLAB, the system locates information such as the slab's relative location, location coordinates, orientation, thickness, and story elevation. Each window within the BIM model is identified using a number followed by \"IFCWINDOW.\" The mapping of IFCWINDOW is followed by IFCRODUCTDEFINITIONSHAPE. IFCRODUCTDEFINITIONSHAPE ultimately defines the specific 3D points for the object representation. IFCWINDOW is also mapped with IFCOWNERHISTORY and IFLOCALPACEMENT. IFCOWNERHISTORY simply contains the modeling program used  
Figure 2: Factors influencing cost and duration estimation.  
to create the IFC and the user of the file. IFCLOCALPLACEMENT identifies the window's location within the BIM model.  
The data extracted from the IFC file is used for 3D representation to identify a particular window that is required to be replaced. The 3D model is a complete model, is interactive, and can be rotated to be viewed from various angles. The interactivity of this model is used to select a particular window, which presents the properties of the window, as shown in Figure 3.
## 2 The Personalized Diagnosis Methodology
### The Personalized Diagnosis Method Using WPT and SVM
#### 2.2.3 The Proposed Method to Detect Faults in Shafts  
In this section, the basic idea for the personalized diagnosis methodology is applied to detect faults in shafts. Both WPT and SVM are employed to generate the personalized diagnosis method, and the flowchart of the proposed fault diagnosis method is shown in Figure 3. Three main steps are given in detail as follow:  
(1) Build the FE model and obtain the simulation signals:  
The numerical model of the shaft can be constructed by commercial Finite element analysis (FEA) software ANSYS (version 10.0, ANSYS Inc., Pittsburgh, PA, USA, 2011), and the simulation signals will be obtained by applying the specific loads. The FE model updating technique (e.g., Theil's inequality coefficient, TIC [37]) is used to determine the stiffness and damping coefficients of the boundary  
Figure 3: The flowchart of the proposed fault diagnosis method. Solid and dotted arrows mean the training and testing paths to realize the method.  
condition (as shown in Figure 4) using the measured signals of the intact mechanical systems to reduce the difference between the simulations and real-world systems. If the difference between the two signals is within the agreeable range (in engineering applications, lower than five percent is commonly accepted), the FE model of shafts will well represent the real-world ones. Then, the faulty models will be inserted into the FE model to simulate the dynamic response of the shaft with faults.
2 Decompose and reconstruct the vibration signal to train SVM: WPT is employed to decompose and reconstruct the vibration signal. In the present work, the Daubechies wavelet is employed as the basis function to decompose the raw signal at a certain  
Figure 4: The finite element model of the shaft. (**a**) The geometry of the shaft, note: \(L_{1}\) = 0.1 m, \(L_{2}\) = 0.1 m, \(L_{3}\) = 0.25 m, \(L_{4}\) = 0.5 m, \(d_{1}\) = 0.01 m, \(d_{2}\) = 0.078 m, \(h_{1}\) = 0.01 m, \(\Delta L\) = 0.002 m; (**b**) the linear bearing model.  
level to obtain a reasonable number of frequency bands. For each frequency band in the maximum layer, the single branch reconstruction technique is performed by wavelet packet reconstruction to obtain the signals at each frequency band. In order to detect the shaft faults, five time domain feature parameters, such as SD, peak, kurtosis, CF and IF, as shown in Table 1, are calculated from the signals at each frequency band to generate the feature vector as training samples to obtain the trained SVM.  
(3) Detect the faults using the measured vibration signals and the trained SVM:  
The signal of the faulty shaft is measured from the real-world rotor-bearing system. The signal will be decomposed and reconstructed by WPT at the last layer to form the sub-signals. The five time domain feature parameters of each sub-signal will further be calculated, and then, the feature vectors serve as a testing sample for the trained SVM. The fault pattern will finally be obtained.
## 4 Discussion  
Several tests were carried out to evaluate the performance of the proposed method. The results obtained by the nearest-neighbor ant colony optimization (ACO-NN) algorithm are compared to renowned metaheuristic algorithms such as artificial bee colony (ABC), genetic algorithm (GA), simulated annealing (SA), ant colony optimization (ACO), camel herd algorithm (CHA), black hole (BH), greedy randomized adaptive search procedure (GRASP), particle swarm optimization (PSO), traveling salesman problem based on simulated annealing and gene expression programming (TSP-SAGEP), simulated-annealing-based symbiotic organisms search (SOS-SA), multifspring genetic algorithm (MO-GA), and discrete tree-seed algorithm (DTSA) with their variants (DTSA0, DTSAI, DTSAII).