esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### MicroCT Analysis of 3D Printed Porous Ceramic Models  
To prevent brittle fracturing of the porous ceramic ossicle model details, which were rather fragile due to the small anatomical model size, a solution of 4% wt/vol alginate (Sigma-Aldrich) was applied with a pipette on the models' top, following the contours of the prints, before removing them from the glass support. To allow for the complete diffusion of alginate into the pores of the constructs, these were left to soak in this solution for 30 min. The gel was then crosslinked with either 10% wt/vol CaCl\({}_{2}\) or BaCl\({}_{2}\). BaCl\({}_{2}\) was preferred for microCT scanning, due to the increased contrast in x-ray imaging [18]; however, only CaCl\({}_{2}\) will be considered for in vitro implantation due to the possible BaCl\({}_{2}\) cytotoxicity [19]. The crosslinking solution was applied over the gel for 30 min to allow for the full crosslinking of the hydrogel. To keep the hydrogel hydrated after crosslinking, the models were stored in distilled water until scanning.  
The porous, gel-infused ossicle models were then analyzed by Micro CT using the Skyscan 1176 (Accela, San Ramon, CA, USA). The samples were scanned at 2000 \(\times\) 1336 pixels, with an AL 0.5 mm filter and a 0.80\({}^{\circ}\) rotation angle. The scans were reconstructed and analyzed using software provided with the Skyscan system (Bruker, Billercia, MA, USA) consisting of NRecon v1.7.4.6, DataViewer v1.5.6.2, and CTVox v3.3.0.0. Model reconstruction was performed using NRecon software with smoothing enabled (level 2), ring artifact reduction enabled (level 8), beam-hardening correction enabled (30%), and a Gaussian smoothing kernel. The reconstructed scans were analyzed using DataViewer software to inspect virtual cross sections of the models. Using color visualization of grayscale threshold, the distribution of crosslinked gel within the model could be visualized. 3D reconstructions were also generated using CTVox software for further visual observation of the printed model and gel distribution.  
To determine the pore size distribution, 12 central pores were selected and measured by ImageJ software v1.8.0 ([[https://imagej.nih.gov/ji/download.html](https://imagej.nih.gov/ji/download.html)]([https://imagej.nih.gov/ji/download.html](https://imagej.nih.gov/ji/download.html)); accessed on 1 February 2022). The scale of the image was set by use of a provided scale bar from DataViewer images. A rectangular measuring tool was utilized to record the estimated height and width of each selected pore. These values were averaged, and the standard deviation was calculated to determine the average size and variance of the pores.
## 2 Materials and Methods
### Serum Biochemical Parameters  
Additionally, blood parameters were evaluated by taking a sample by caudal vein puncture according to the method described by [PERSON] (17) (sample of 8 fish for T1, 12 fish for T2, and T3). For this, fish were anesthetized by immersion in a container with clean water and an emulsion of clove oil (Viarden(r) S.A. de C.V., Mexico City, Mexico) (0.3 mL/L) dissolved in ethanol (96%) with a 1:10 ratio [18] until they presented erratic swimming. Once under the effects of anesthesia, approximately 400 \(\mu\)L of blood per fish was collected and divided into two microtubes; for this purpose, a puncture was performed in the caudal vein with a 1 mL insulin syringe with a 30 G \(\times\) 13 mm needle without heparinization. The first part of the sample was placed in a pediatric microtainer with EDTA-K2 anticoagulant (SARSTEDT(r) Sarstedt, Numbrecht, Germany) and the second in a pediatric serum microtainer without anticoagulant. To carry out the analysis, specific kits (Mexlab(r) MexLab Group, Jalisco, Mexico) were used to determine the concentration of albumin with bromocresol green (BCG, 620 nm), cholesterol by cholesterol oxidase and peroxidase (CHOD-PAP, 505 nm), glucose by glucose oxidase and peroxidase (God-Pad, 505 nm), total protein (BIURET, 540 nm), triglycerides using glycerol phosphate oxidase and peroxidase enzyme (GPO-PAP, 510 nm), and cortisol was measured using enzyme-linked immunosorbent assay (ELISA) at 450 nm. In addition, electrolytes chlorine (Bio-Chlorine, 480 nm) and calcium by cresolphthalein complexone (CFC, 570 nm) present in the blood serum were analyzed. The analysis of each parameter was done by spectrophotometry (Velab(r) VE-5000 V Mexico City, Mexico). Globulin concentration was calculated by the difference between total protein and albumin concentration and the albumin/globulin ratio (A/G) was determined.
## 2 The Basic Structure and Operation Principle of DLSRG
### Operation Principle  
The operation of DLSRG follows the principle of magneto resistive minimization [19; 20; 21]. When the DLSRG is running, the inductance is inversely proportional to the reluctance. When the rotor tooth is aligned with the center line of the stator tooth, the inductance of the phase winding reaches  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Numerical Value** & **Parameter** & **Numerical Value** \\ \hline Statorslot width \(\mathrm{s_{s}/mm}\) & 33 & Motor body width \(\mathrm{w/mm}\) & 150 \\ Stator tooth width \(\mathrm{s_{p}/mm}\) & 33 & air gap \(\mathrm{g/mm}\) & 1.5 \\ Statortooth height \(\mathrm{s_{p}/mm}\) & 54 & Stator silicon steel sheet \(\mathrm{s_{si}/sheet}\) & 300 \\ Stator yoke \(\mathrm{s_{c}/mm}\) & 21 & Rotor silicon steel sheet \(\mathrm{t_{si}/sheet}\) & 300 \\ Rotor slot width \(\mathrm{t_{s}/mm}\) & 66 & number of turns of winding \(\mathrm{N/circle}\) & 273 \\ Rotor tooth width \(\mathrm{t_{p}/mm}\) & 33 & Enamelled wire diameter \(\mathrm{d/mm}\) & 1.38 \\ Rotortooth height \(\mathrm{t_{ph}/mm}\) & 19 & Enamelled wire cross section \(\mathrm{S/mm^{2}}\) & 1.496 \\ Rotor yoke \(\mathrm{t_{c}/mm}\) & 48 & Full tank rate \(\mathrm{K_{s}}\) & 0.4596 \\ \hline \end{tabular}
\end{table}
Table 1: DLSRG parameters.  
Figure 1: Basic structure of Double-sided Linear Switched Reluctance Generator (DLRSG). The motor consists of the stator, the rotor, the motor housing, the windings, the slot wedges, the linear bearing.  
Figure 2: Schematic diagram of DLSRG. The A, B, C represents the three-phase winding. In DLSRG winding, the three phase excitation is equal, the phase difference is 0, the structural parameters of DLSRG are shown in Table 1.  
the maximum value and the reluctance is the smallest. When the rotor tooth is separated from the center line of the stator tooth by one tooth width, the phase winding inductance reaches the minimum value, and the magnetoresistance reaches the maximum. When running in a single cycle, it is divided into excitation stage and power generation stage. In the excitation stage, the magnetic pull force is in the same direction as the external mechanical force, and the magnetic field energy is stored; in the power generation stage, the magnetic pulling force is opposite to the external mechanical force, which overcomes the work done by the external mechanical force, changes the external mechanical energy into electric energy, and also releases the stored magnetic field energy.
# Thermographic Non-Destructive Evaluation for Natural Fiber-Reinforced Composite Laminates
## 3 Experiments  
### Infrared Thermography  
Pulsed thermography (PT) and lock-in thermography (LT) are two main optical excitation IRT methods. PT uses high-energy lamps to generate a uniform heating on an object surface. An infrared camera is used to record the surface temperature profile. In PT, a subsurface defect causes a non-uniform cooling, which can be identified in an imaging sequence. As a time-domain sequenced imaging approach, PT allows the application of the advanced imaging processing techniques to obtain more visible imprints of the defects. LT is known as a modulated technique. In LT, the absorption of modulated heating leads to a temperature modulation. LT requires long acquisition time, especially for deep defects (low modulation frequencies). Sinusoidal waves are typically used in LT, which have the following advantages: (1) frequency and shape of the response are preserved, (2) only the amplitude and phase delay of the wave may change [26; 27].  
Figure 4 shows the schematic and experimental set-up for optical excitation IRT. Two 'OMNLUX PAR64' (1000 W) halogen lamps were used to generate pulse or lock-in sinusoidal signals for PT and LT, respectively. A Balcar FX 60 (6.4 KJ, 2 ms duration) producing photographic flashes was used for PT. A mid-wave IR camera 'FLIR Phoenix' (see Table 4) with the frame rate of \(\sim\)55 fps was used to record the temperature profile. The cooling time for PT is 15 s/20 s for flashes/halogen lamps modality, respectively.  
As an ultrasonic excitation thermographic approach, vibrothermography (VT) was also used in this paper. Different from optical excitation IRT, VT uses ultrasonic waves to stimulate a subsurface defect without heating the surface. The stimulation of the subsurface defect causes a complex combination of absorption, scattering, beam spreading and dispersion. The ultrasonic waves spread towards all of the orientations in the form of heat [26; 27].  
Figure 5 shows the VT set-up. The same IR camera with the frame rate of \(\sim\)55 fps was used. The transducer was pressed (200 Pa) against the specimen and two periods of 0.2 Hz (10 s) lock-in ultrasonic waves were delivered.
# Enhanced Scalability and Security in Blockchain-Based Transportation Systems for Mass Gatherings
## 4 Results and Findings
### Comparing the Cost of the Proposed System with Other References  
Integrating various costs into a comparative assessment of blockchain solutions is necessary to compare the economic effectiveness of the multiple strategies. The cost factors usually standard for a proposed system include infrastructural costs, transaction costs, security costs, and the cost of scalability solutions. They all translate into the total cost of ownership and should be addressed when comparing the proposed system to reference systems. The proposed system comprises multiple components. Equation (16) for the total cost of the proposed system is formulated as follows [44]:  
\[Cost_{proposed}= \varsigma_{infra}\.Nodes.\beta_{tx}.transaction\ volume+\gamma_{sec}.security\ overhead+\delta_{scalability} \tag{16}\]  
where \(\varsigma_{infra}\) represents the infrastructure cost coefficient per node, \(\beta_{tx}\) denotes the transaction cost coefficient per transaction (or per transaction per second, TPS), \(\gamma_{sec}\) refers to the security overhead cost, and \(\delta_{scalability}\) captures the cost of scalability features such as State Channels or Rollups. Furthermore, the above equation models the total cost of the proposed system by incorporating infrastructure, security, and scalability factors. For those costs, we refer to \(\varsigma_{infra}\.Nodes.\beta_{tx}\) transaction volume term as a base infrastructure cost, based on the number of nodes and transaction volume. With State Channels and rollup scaling throughput, the transaction volume component becomes larger, ultimately leading to increased infrastructure costs. The computational overhead incurred for ensuring trust in scalability solutions represented by the \(\gamma_{sec}.security\ overhead\) term, which includes additional cryptographic proofs and fraud prevention mechanisms that are necessary for such schemes but are computationally expensive. Lastly, because these inherent costs are not incurred via State Channels or Rollups, we express this as \(\delta_{scalability}\). These scalability solutions reduce network congestion and transaction costs, but they also generate overhead, which further increases the overall error rate. Thus, the tradeoff is highlighted in Equation (16), where in turn, the more scalable the solutions, the greater the performance achieved at the expense of infrastructure, security, and additional resources required by the implementation overhead. Table 3 shows the coefficients and detailed cost calculations for the proposed system, including baselines, State Channels, and Rollups. The costs for the reference systems are assumed to depend on the infrastructure, frequency of transactions, and scalability solutions such as State Channels and Rollups.
# Research on a Rail Defect Location Method Based on a Single Mode Extraction Algorithm
## 2 An Accurate Modal Identification Method
### Modal Identification
#### 2.3.1 Theoretical Derivation  
According to the excitation response analysis method, the vibration displacement of any point of the rail is equal to the superposition of all the propagable modes' vibration displacements at that point. However, when there are both direct and reflected waves in the rail, the vibration displacement at any point is the superposition of the direct and reflected waves, that is, the superposition of all direct modes and all reflected modes at that point. Therefore, when considering the influence of the reflection wave caused by the defect, the expression of the excitation response of the node is in the form of Equation (8), in which the upper line \"-\" represents the relevant parameters of the reflected modes.  
Figure 4: Schematic diagram of defect location.  
\[V(y,z,f) =\widehat{F}(f)\cdot U(y,z,f)\] \[=\widehat{F}(f)\cdot(\sum_{m=1}^{M}\Omega_{m}\mathcal{U}_{m}^{Rup}e^ {-i[\xi_{m}(x-x_{s})]}+\sum_{m=1}^{M}\Omega_{m}\bar{\Omega}_{m}^{Rup}e^{-i[\xi_{ m}(x-x_{s})]}) \tag{8}\]  
If we let \(\Omega=\begin{bmatrix}\Omega_{m}&\bar{\Omega}_{m}\end{bmatrix}\), then with to the sampling results of a simulation or an experiment, the frequency domain signal \(V(f)\) of any \(N\) points' vibration displacement on the rail can be obtained. By substituting the wave number, the mode shape, the distance between the sampling point and excitation point, and the frequency domain signals of the excitation signal at the corresponding frequency of each mode into Equation (8), the parameters \(\Omega\) including the amplitude and phase of each mode can be achieved. It should be noted that the number of sampling points should be enough to ensure that the solution can be obtained relatively accurately, that is, \(N>2M\).
# Detecting IoT Attacks Using an Ensemble Machine Learning Model
## 2 Background and Related Work
### IoT System with Cloud and Fog  
Figure 1 illustrates the benefits of using the cloud for data processing because it may have the resources necessary to perform complex computations. The cloud, however, has several inherent weaknesses, including high costs, long latency, and limited bandwidth [39].  
Further, due to proximity to IoT devices, fog is well suited for solving a variety of issues including long latency, communication, control, and computation [40]. With fog computing, time-sensitive data can be stored and analyzed locally [41]. Furthermore, by reducing the amount and distance of data sent to the cloud, IoT applications can be made more secure and private [42; 43].  
Researchers have employed a number of approaches and techniques to overcome data transfer challenges in fog, including encryption-based data transfer, as described in [44; 45]. Furthermore, several researchers have proposed methods to improve security in fog, including game-based security [46]. However, these works do not have the advantage of functioning in real time. Currently, researchers are developing a method for predicting real-time scenarios and minimizing the overall time factor by balancing cloud computing with fog computing and optimizing the trade-off between the two (e.g., [47]). Likewise, this approach is used in our paper to move resource-intensive and time-sensitive tasks to the cloud and real-time tasks to the fog layer.
## 5 Successive Approximation Using CS-WSNs
### Moving along \((M,b)\) Pairs  
Increments in \(M\) and \(B\) may occur in several ways: the number of measurements increases while bit-depth is unaltered, the coefficients quantization may be refined while number of measurements is unaltered, or both are incremented simultaneously. The first two possibilities correspond to paths  
Figure 7: Optimal coding path on the \(M\times B\) plane, the points that correspond to the RD convex hull, for the reconstruction of a temperature signal with LASSO.  
along horizontal or vertical directions on the \(M\times B\) plane, respectively. In the more general case, instead, both \(M\) and \(B\) could change jointly, moving the coder setup up and to the right in Figure 7. Therefore, starting from a given \((M_{i},B_{i})\) operational RD point, the coder should move to a new \((M_{j},B_{j})\) pair leading to the minimal distortion for the resulting increment in the rate.  
Each operational point represents a specific CS-quantized coding setup on the \(M\times B\) plane. The quantizers are designed such that their input-output mappings intersect. The quantization rule using \(B+1\) bits refines the quantization rule for \(B\) bits. Thus, when moving from the operational point \(i\) to the operational point \(i+1\) just the refinements of previously transmitted values need to be transmitted, i.e., \(B_{i+1}-B_{i}\) bits per value.  
The initial RD-optimal setup point \((M_{0},B_{0})\) is defined from the initial rate. The \(M_{0}\) measurements are quantized and transmitted with \(B_{0}\) bits. If there is demand for more bits, then a new RD-optimal point is to be used. The new point \((M_{1},B_{1})\) may be selected from an operational setup table according to the desired rate increase. That is, for an operational point \((M_{i},B_{i})\) there may be several possible operational setups to use, the one to be used \((M_{i+1},B_{i+1})\) will depend on the actual increase in the quantity of transmitted bits subject to distortion reduction. Once that is decided, the sensor sends the \(M_{i+1}-M_{i}\) extra measurements quantized with \(B_{i+1}\) bits and the extra \(B_{i+1}-B_{i}\) bits refining the already transmitted \(M_{i}\) measurements.  
Nevertheless, one must still compute the operational point to which the sensor node must shift. A sensor node can not do that on-line, as its energy would be exhausted due to the extensive required computations. Alternatively, a training set can be employed to analyze the RD performance and generate a table indicating the best shift at a given operational point and the desired increment in the quantity of transmitted bits. That data can be used in two different fashions: sensor nodes can be aware of the table and fetch the new operational point for a given rate increment; or, alternatively, the sink may inform the sensor node of the new operational point; the later reduces the requirements on sensor node memory size and marginally increases the data sent by the sink to request the relevant bits for signal reconstruction improvement.
# Bacteriotherapy in Breast Cancer
## 3 Anticancer Substances Released by Bacteria
### Bacterial Toxins in Cancer Therapy
#### 3.3.4 Exotoxin T  
Exotoxin T (ExoT) is produced by _Pseudomonas aeruginosa_ that secretes via III secretion system (T3 SS) and facilitates pathogenesis. ExoT contains two domains, i.e., an N-terminal with GTPase-activating (GAP) and a C-terminal that is a domain with ADP ribosyltransferase (ADPRT), and both domain activities contribute to ExoT-induced apoptosis [86]. Exotoxin T (ExoT) is different from _Pseudomonas_ exotoxin A and diphtheia toxin that have a single putative target (e.g., eEF-2). This exotoxin has multiple cellular protein targets, which have critical roles in survival, proliferation, metastasis, and angiogenesis in cancer, by regulation of actin cytoskeletal dynamics, activation of protein kinases, cell cycle progression, cytokinesis, and the C10 regulator of kinases (CrKI, CrkII adaptor proteins) that are critical in the maintenance and formation of cellular focal adhesions and cytokinesis, and the glycolytic enzyme phosphoglycerate kinase 1 (PGK1) which is necessary for angiogenesis in cancer [87; 88; 89]. Thus, the resistance of cancer cells to cytotoxicity induced by ExoT is highly unlikely. In vitro studies have demonstrated that ExoT can cause cytotoxicity to reduce tumor establishment and growth in multiple cancer and breast cancer cell lines including MDA-MB-231, EMT6, 4T1 [73].
# Pneumatic Rotary Actuator Position Servo System Based on ADE-PD Control
## 2 Experimental Set-Up  
The rack and pinion pneumatic rotary actuator is the actuator of pneumatic servo control system. The composition and control principle of the system are described in Figure 1.  
In Figure 1, the pneumatic connections are shown in solid lines and the electrical connections are expressed in dashed lines. The air compressor generates compressed air, and the gas flows through air filter, air regulator and air lubricator to become pure gas with stable pressure, which provides power for the pneumatic system. Rack and pinion pneumatic rotary actuator is placed horizontally, and the load is installed on the rotary table of it. Proportional directional control valve dominates flow direction and flow of the gas at the inlet and outlet to control the rotation angle of the rotary table. The rotary encoder transmits the rotation angle of actuator to the data acquisition card in Industrial Personal Computer (IPC) by transistor-to-transistor logic (TTL) levels. The TTL levels are calculated by the counter in data acquisition card. Therefore, the rotation angle of the actuator can be obtained. The host computer established by Labview calls the control strategy compiled by MATLAB/Simulink to deal with the difference between actual rotation angle and set rotation angle, and converts the result into 0-10 V voltage signals. Then the signals are transmitted to the proportional directional control valve to control actuator to reduce the deviation of rotation angle. The position of pneumatic rotary actuator is controlled accurately in the above process of real time control.  
The experimental components are selected reasonably according to the principle of the system, and the experimental platform shown in Figure 2 is set up. Models and parameters of the main components are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Model** & **Parameters** \\ \hline Pneumatic rotary actuator & SMC MSQA30A & bore: 30 mm, maximum angle: 190\({}^{\circ}\) \\ Proportional directional control valve & FESTO MYPE-5-MS-010-B & control voltage: 0–10 V, rated flow:100 L/min \\ Data acquisition card & NIPI-629 & 32 bit counter, output voltage: –10–10 V \\ Rotary encoder & OMRON E6B2-CWZ3E & resolution: 1800 P/R \\ IPC & IPC-610H & standard configuration \\ F.R.L Unit & AirTAC AC2000 & maximum pressure: 1 MPa \\ \hline \hline \end{tabular}
\end{table}
Table 1: Models and parameters of main components.  
Figure 1: Schematic diagram of pneumatic rotary actuator position servo system (1—Air compressor; 2—Air filter; 3—Air regulator; 4—Air lubricator; 5—Proportional directional control valve; 6—Rotary encoder; 7—Rack and pinion pneumatic rotary actuator; and 8—IPC).  
Figure 2: Experimental platform of pneumatic rotary actuator position servo system.