esa-sceva/satcom-chunk-collection huggingface dataset:
# Reconstruction of the Physiological Behavior of Real and Synthetic Vessels in Controlled Conditions
###### Abstract  
The objective of this study is to assess the ability of an Artificial Circulatory Phantom (ACP) to verify its accuracy in simulating the movement of artificial vessels vs. real vessels under changing cardiovascular parameters such as heartbeat, ejection fraction, and total peripheral resistance. The experiments were conducted with blood-like fluid that flows through two types of vessels: iliac arteries and different types of ePTFE vascular prostheses. Parameters such as diameter and tortuosity were measured and analyzed. The flow characteristics included a pulsating pattern with a frequency of 60-120 min\({}^{-1}\) and ejection volumes ranging from 70 to 115 mL. The results showed a predominantly positive correlation between wall displacement (W\({}_{d}\)) and tortuosity index (T\({}_{i}\)) for the iliac artery (R\({}^{2}\) = 0.981), as well as between Wd and mean tortuosity index (MT\({}_{i}\)) (R\({}^{2}\) = 0.994). Similarly, positive correlations between Wd and T\({}_{i}\) (R\({}^{2}\) = 0.942) and Wd and MT\({}_{i}\) (R\({}^{2}\) = 0.922) were computed for the ePTFE vascular prosthesis. The ACP introduced in this study is a valuable tool for evaluating various vessel types and the spatial configurations of vascular prostheses under diverse hemodynamic conditions. These findings are promising for the advancement of novel approaches to the testing and design of vascular grafts, ultimately enhancing their patency rates in future applications.  
Keywords:vascular imaging; vascular prosthesis; PTFE vascular prosthesis; tortuosity; stress measurement; stress control; wall displacement; artificial vascular system +
Footnote †: journal: Applied Sciences
# A Noniterative Simultaneous Rigid Registration Method for Serial Sections of Biological Tissues +
## 3 Noniterative Simultaneous Rigid Registration Method (NSRR)
### Decoupling Rotation from Translation  
In Equation (1), the optimization of rotation and translation is coupled; consequently, optimizing either operation will interfere with the other. To simplify this complex situation, we propose a simple yet effective way to decouple the optimization of these two variables.  
Our scheme is that we first avoid interference from translation by alternately considering an approximate problem that registers multiple point sets all centered at the origin. Since adjacent point sets have common centroids (i.e., the origin), we only need to solve for a rotation transformation. This approximate problem can be constructed by moving the centroid of every point set to the origin and is referred to as the centralization of point sets. Then, we can concentrate on solving the optimal rotation of this approximate problem. Rotation optimization is performed by the following equation:  
\[\begin{split}\operatorname*{arg\,min}_{\mathbf{R}_{i}}& \sum\limits_{i=1}^{n-1}\|\mathbf{R}_{i}\widehat{\mathbf{X}}_{i,i+1}- \mathbf{R}_{i+1}\widehat{\mathbf{X}}_{i+1,i}\|_{F}^{2}\\ s.t.&\left\{\begin{array}{l}\mathbf{R}_{i}^{T} \mathbf{R}_{i}=\mathbf{I}\\ \det(\mathbf{R}_{i})=1\\ \mathbf{R}_{1}=\mathbf{R}_{n}=\mathbf{I}\end{array}\right.\end{split} \tag{2}\]  
where \(\widehat{\mathbf{X}}_{i,j}\) denotes the point set after centralization.  
After solving Equation (2), we substitute the optimal rotations obtained by solving Equation (2) into Equation (1); we fix the values of the obtained rotation and then independently solve the translation by optimizing Equation (1). Even though our decoupling scheme obtains only approximate solutions, in Section 3.6, we prove that, under ideal conditions, our approximate solutions are equal to the optimal solutions, and, in Section 4.1, we experimentally show that, even under nonideal conditions, our algorithm remains robust and provides reliable solutions.  
In following subsections, we explain our approach for estimating rotation and translation in Sections 3.3 and 3.4, respectively, and then provide the overall workflow of our method in Section 3.5.
# Dispatching Strategy for Low-Carbon Flexible Operation of Park-Level Integrated Energy System
## 3 Modeling of PIES
### Integrated Demand Response (IDR)  
Flexible loads can be divided into shiftable loads (\(SL\)s) and interruptible loads (\(ILs\)) according to their operating characteristics.  
\(IL\) refers to a load that can be interrupted to relieve the pressure of energy system operations during periods of energy supply shortages or high prices [25]. The interruptible electrical loads are expressed as Equation (9):  
\[\left\{\begin{array}{l}\frac{P_{t,\min}^{IL}}{2^{4}}\leq P_{t}^{IL}\leq P_{t,\max}^{IL}\\ \sum\limits_{t=1}^{24}U_{t,cut}\leq N_{cut}^{\max}\end{array}\right. \tag{9}\]  
where in period \(t\), \(P_{t}^{IL}\) is the interruption power of the load, \(P_{t,\max}^{IL}\) and \(P_{t,\min}^{IL}\) are the maximum and minimum limits of the interrupting load, \(U_{t,cut}\) is the binary variable for whether the load is interrupted or not, and \(N_{cut}^{\max}\) is the number of interruptions allowed in the dispatching cycle.  
\(SL\) implies that the equipment has a flexible start-up time, and the overall energy usage is unchanged when scheduling the equipment time [26]. \(SL\) is given as Equation (10):  
\[\left\{\begin{array}{l}P_{t,\min}^{SL}\leq P_{t}^{SL}\leq P_{t,\max}^{SL}\\ \sum\limits_{t=1}^{24}P_{t}^{SL}=0\end{array}\right. \tag{10}\]  
where in period \(t\), \(P_{t}^{SL}\) is a time-shiftable load and \(P_{t,\max}^{SL}\) and \(P_{t,\min}^{SL}\) are the maximum and minimum limits of the interrupted load.
# Power Efficient Random Access for Massive NB-IoT Connectivity
## 4 Average Power Consumption in Existing NB-IoT RAP  
In this section, we evaluate the average power consumption by NB-IoT devices in the existing NB-IoT RAP. While the the choice of CE level for the first RAP attempt is random, for the subsequent reattempt it is not. Moreover, failure at any attempt can occur either due to collision or due to poor channel. This is due to the fact that unlike the proposed PE-RAP, the devices in existing NB-IoT RAP on failure do not check the channel again.
## 2 Materials and Methods
### System Definition
#### 2.4.3 Electromechanics  
For the study of the EDS, the works consulted are [19; 20; 21]. The EDS used for this high-speed transportation system is very similar to that used for other magnetic levitation (maglev) vehicles, although in those maglev vehicles wheels are needed at low speeds because there is not enough induction magnetic field to levitate. The traditional EDS can be modeled as a LIM (linear induction motor) for levitation and as an LSM (linear synchronous motor) for propulsion. In order to eliminate the need for wheels, the LIM is replaced by an LSM when applying EDS to the high-speed transportation system, where the rotor will be mounted on the pod (short rotor) and the stator on the tube [21]. From this work, these expressions are taken: Equations (17) and (18). Furthermore, reference [20] contains explanations and formulae for levitation and the drag force generated by the EDS operation. These formulae can be found below, although expressed a little differently (Equations (20)-(22)). Lastly, the next equations from reference [19] have been used in the analysis (Equations (23)-(25)), where the number three indicates the number of phases of the motor:  
\[\eta_{EDS}=\frac{F_{x}v}{F_{x}v+3I_{1}^{2}R} \tag{17}\]\[cos\varphi=\frac{F_{x}v+3I_{1}^{2}R}{3V_{1}I_{1}} \tag{18}\]  
\[sin\varphi=\frac{X_{1}I_{1}^{2}+E_{1}I_{1}sin\gamma_{o}}{V_{1}I_{1}} \tag{19}\]  
\[F_{z}=m_{tot}g \tag{20}\]  
\[F_{D_{EDS}}=C_{D_{EDS}}F_{z} \tag{21}\]  
\[P_{D_{EDS}}=F_{D_{EDS}}v \tag{22}\]  
\[P=3V_{1}I_{1}cos\varphi \tag{23}\]  
\[Q=3V_{1}I_{1}sin\varphi \tag{24}\]  
\[3E_{1}I_{1}cos\gamma_{0}=F_{x}v \tag{25}\]  
The electrical model for the considered EDS is shown in [19]. This model is based on the LSM, which can be seen as a rotary synchronous motor rolled out flat. Subsequently, a resistance and a reactance are used at the stator (on the left). At the model air gap, electric power is equated to mechanical power. On the right, a damper and a spring are joined to represent mechanical losses. However, for a first parameter estimation, it is preferable to remove the damper and the spring, and to consider that all active power losses occur in the stator resistance (Figure 2c).
## 3 Laboratory Experiment  
### Measurements  
Two dimensional DIC measurements, applying one high-speed Phantom v341 camera (see Figure 2b) were conducted during vibrations of the frame caused by harmonic excitation at the frequency of 106 Hz (The recorded data are available at the reader's request. Please note that these are huge files (tens of Gigabytes)). The decision regarding the excitation frequency required the analysis of all natural frequencies of the investigated structure; possible changes of the natural frequencies(caused by some introduced damages) had to be taken into account. Finally, it was decided that an excitation with a frequency of 106 Hz would be applied, because this frequency was offset by at least 10% from all natural frequencies corresponding to global in-plane mode shapes. One of the local in-plane mode shapes (with moving beams, without any movement of columns) was very close to the chosen excitation frequency (the difference was below 1%); however, the excitation point was chosen so that this particular mode shape was not induced and no resonance appeared.  
The measurements were focused on one selected beam-to-column connection, marked in Figure 2c, made with eight pretensioned bolts of the M8.8 type (see Figure 2d,e). During DIC measurements, the bolts in the selected connection were loosened according to the scenarios shown in Figure 2d. The symbols used for the description of applied scenarios, namely Sxy, were the combination of the letter \"S\" (scenario) and two numbers, \(x\in\{1,2,3,4\}\) and \(y\in\{1,2,3,4\}\), which denote the row with loosened bolts (where 1 is the top row, 4 is the bottom row); in cases where bolts were loosened only in one row, \(x=y\) (e.g., S44 means that only the 4 th row of bolts was loosened). Despite the loosening of nodes, the changes in the whole connection, as well as in every bolt were invisible with the naked eye and their detection was possible only by manual verification. Three measurement series were carried out and all scenarios were implemented in each of them. Each single measurement (in one connection state) lasted one second, with the recording frequency equal to 2120 Hz.  
According to the requirements of the applied method, the surface surrounding the node was prepared by painting with a speckled pattern, shown in Figure 2e. Only the left area, indicated by the red oval, was considered in this paper. The right one will be analyzed in the future. Figure 2f shows point OO together with the other points on the crossing of red lines, in which the displacements were analyzed with an accuracy of 0.0007 mm. The obtained accuracy was the result of the specified measurement parameters (dimension of the region of interest: 51.48 \(\times\) 102.94 mm, picture size: 756 \(\times\) 1560 px) and the accuracy of the ISTRA 4D software (0.01 px) [53].
## 7 Comparative Analysis with Existing Schemes
### Comparative Study of Security and Functionality Attributes  
The comparison of the security and functionality attributes for the proposed LDAKM-EIoT and other related schemes is shown in Table 4. Various features (\(FSF_{1}\)-\(FSF_{4}\), \(FSF_{6}\), \(FSF_{17}\), and \(FSF_{18}\)) were not supported/available in the scheme of [PERSON] et al. [21], whereas [PERSON] et al.'s scheme [3] did not support the features \(FSF_{11}\) and \(FSF_{14}\), which were shown by [PERSON] et al. [37]. Moreover, other schemes, such as the schemes of [PERSON] and [PERSON] [39], [PERSON] et al. [41], and [PERSON] et al. [18] did not provide the required functionality and security features. On the other hand, LDAKM-EIoT provided better security and functionality features as compared to other related schemes.
## 2 Conventional Diagonal Loading Methods
### DOA Deduction through the Spatial Response Power and Iteration  
Although the procedures from Section 3.2 to Section 3.4 can effectively improve the performance of the CMB, the effectiveness of Equations (15) and (18) is seriously affected by the initial DOA of the target source. Therefore, spatial response power is used to derive a more accurate DOA of the target source. The spatial response power at the \(k\)th frequency bin of the \(l\)th frame can be expressed as follows:  
\[\psi(\vartheta)=\mathbf{w}_{\text{proposed}}^{H}\mathbf{a}(\vartheta)\mathbf{a} ^{H}(\vartheta)\mathbf{w}_{\text{proposed}} \tag{21}\]By statistically quantifying the maximum of the spatial response power in each frame, the more accurate DOA of the target source can be derived. In addition, since Equations (15), (18) and (21) are not optimal, the iterative operation is used to optimize the performance of the proposed method. The iterative procedure is given as follows:  
* **Step 1**: Calculate the CMOS \(\mathbf{R}_{\text{XX}}\) through Equation (3);
* **Step 2**: Calculate the loaded covariance matrix \(\mathbf{R}_{\text{IGLC}}\) through Equation (10);
* **Step 3**: Correct the steering vector of the target source through Equation (15) and normalize it;
* **Step 4**: Reconstruct the covariance matrix \(\mathbf{R}_{\text{rec}}\) through Equation (16), and normalize it;
* **Step 5**: Calculate the weighting vector \(\mathbf{w}_{\text{proposed}}\) through Equation (20);
* **Step 6**: Calculate the spatial response power \(\psi(\theta)\) through Equation (21) and derive a new DOA of the target source;
* **Step 7**: Update the DOA of the target source and return to Step 2 to repeat the procedures from Step 2 to Step 6 until the derived DOA of the target source is converged.  
The mean \(\gamma\) of the DOA difference between the last three iterations and the current iteration is used for judging whether the proposed algorithm is converged, i.e.,  
\[\gamma=\frac{1}{4}\sum_{\kappa=\tau-3}^{\tau}\Delta\theta_{\kappa} \tag{22}\]  
where \(\Delta\theta\kappa\) is the difference in the derived DOA between the \(\kappa\)th iteration and the \((\kappa-1)\)th iteration, and \(\tau\) is the number of the iteration. If \(\gamma\leq 0.1\), the proposed algorithm converges. Otherwise, the proposed algorithm does not converge.
## 3 Data Compression Technology-Based Intelligent Data Acquisition (IDAQ) System
### Logging and Controller System (L and Cs)  
Employing the wireless sensor nodes used in existing S-SHM systems may be difficult if the data size is increased due to an increase in the sampling ratio or expansion of the input channel, as these nodes have limitations in performance, as discussed previously. It was determined that configuring the wireless sensor nodes at the same level as a high-performance PC would be best to improve the processing speed and the storage capacity and management for the acquired data. Therefore, in this article, an Ethernet-based cDAQ-9139 provided by NI was used, as shown in Figures 6b and 7a. This system is embedded with a dual-core Intel i7 processor with a clock speed of 1.33 GHz, 2 GB RAM, and 32 GB hard disk within the stand-alone chassis. This innovative system provides an improved environment in which to acquire, process, and store large datasets with high speed in real-time. Furthermore, since the L and Cs are equipped with a Linux-based RTOS, guaranteeing stability and real-time operation, it can be operated independently in real-time. Particularly, when the pre-loaded DAQ assistance and DAQmx software support are also used, diverse logics can be embedded and executed with very little manual coding. As embedded within the cDAQ-9139, the CAFB developed in Section 2 operates to compress the data, but could be utilized to replace the existing analog filter bank completely.
## 3 Results and Discussion
### Pore Structure of PolyHIPE Membrane
#### 3.2.1 Pore Size Distribution  
Figure 2 shows the cumulative volume with respect to pore diameter measured with mercury porosimetry. The data of cumulative volume shows a two-step intrusion behavior. The first step at 1000 to 9000 nm describes the mercury intrusion into large pores of PolyHIPE, while the second step with slope change below 30 nm is for the small pores. Figure 2 also shows the log differential intrusion volume (\(\mathrm{d}V/\mathrm{d}(\log(D_{\mathit{pore}}))\) with respect to pore diameter, where \(V\) is the cumulative volume of mercury and \(D_{\mathit{pore}}\) is the pore diameter. The pore size of PolyHIPE membrane shows a bimodal distribution with two distinct pore diameter regions (10-30 nm and 1000-9000 nm) with a \"valley\" within 30-1000 nm. The relative pore volume of the two distinct pore size ranges is 11.6% (10-30 nm) and 88.4% (1000-9000 nm), respectively. Other characteristic properties of PolyHIPE membrane obtained from mercury porosimetry measurement are bulk density (0.23 g/mL), skeletal density (1.095 g/mL), and porosity (0.77).  
Figure 2: Pore diameter distribution of PolyHIPE membrane (30 wt % St, 60 wt % EHA, and 10 wt % DVB).
# Design and Calibration of a Sensory System of an Adaptive Gripper
## 2 Materials and Methods
### Design of the Electrical and Electronic Schematic of Each Gripping Module
The integration of the sensor system with the overall adaptive grip control architecture is based on a distributed architecture, with a main controller (Master) and five secondary controllers (Slave), one for each module. Although the provided source code does not explicitly present a complete Master-Slave architecture, we can deduce the operating principle based on the available information:  
* Data acquisition from sensors: Each module (Slave) has its own set of sensors: a force sensor and a slip sensor (incremental encoder). The Slave controller collects data from these sensors locally, with a specified sampling frequency. The collected data are pre-processed locally (filtering, scaling) before being transmitted to the Master controller. * Communication with the Master controller: The Slave controllers transmit module status data to the Master controller via a digital communication channel using binary logic. This communication is bidirectional, allowing the Master controller to send commands to the Slave modules. * Master-level data processing and control: The Master controller receives data from all five modules. It processes these data to assess the overall condition of the gripper and the object being handled. The PID-based control algorithm makes decisions based on these data, adapting the gripping force and movements of each module. * Slave-level local control: The Slave controller receives commands from the Master controller and executes them locally. Using the PID algorithm, each Slave controller adjusts the gripping force and jaw movement to reach the setpoint set by the Master controller and to compensate for any detected slippage.
## 4 Coordinated Control Example and Simulation
### Simulation
#### 4.2.2 Calibration of Simulation Parameters  
The car-following model and the lane-changing model in the VISSIM simulation software have a great influence on the driving state of the vehicle. To obtain a more realistic simulation effect, six parameters in the two models were calibrated, which were average stopping distance, additional part of safety distance, multiplier part of safety distance, minimum head gap, maximum deceleration of overtaking vehicle, and maximum deceleration of overtaken vehicle [26].  
The process of parameter calibration was as follows:  
1. The average delay and stopping time of vehicles were used as evaluation indicators, and the actual intersection data were counted.
2. A reasonable range of values and step sizes for the six parameters were determined, and the actual data from the statistics were input into the simulation model. The parameter values were changed for multiple simulations, and the average delay and stopping time for each inlet lane were extracted.
3. The simulated average vehicle delays and stopping times were compared with the statistical actual values to calculate the error for each simulation and determine the most suitable parameter combination. The errors before and after the parameter calibration were 25.5% and 20%, respectively, which were reduced by 5.5% and were more consistent with the actual operating conditions of the vehicle. The calibration results are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \begin{tabular}{c} **Average** \\ **Parking** \\ **Spacing** \\ \end{tabular} & \begin{tabular}{c} **Additional** \\ **Parts of the** \\ **Safety** \\ **Distance** \\ \end{tabular} & \begin{tabular}{c} **Multiplier Part** \\ **of the Safety** \\ **Distance** \\ \end{tabular} & \begin{tabular}{c} **Minimum** \\ **Headroom** \\ **Distance** \\ \end{tabular} & \begin{tabular}{c} **Overtake the** \\ **Maximum** \\ **Deceleration** \\ **of the Car** \\ \end{tabular} & \
\begin{tabular}{c} **Maximum** \\ **Deceleration** \\ **of Overtaken** \\ **Vehicles** \\ \end{tabular} \\ \hline
\begin{tabular}{c} Default Value \\ Calibration results \\ \end{tabular} & 2 & 2 & 3 & 0.5 & \(-4\) & \(-3.5\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Calibration results of driving behavior parameters.  
Figure 3: Simulation model of coordinated control of arterial road.
## 3 Results and Discussion
### Volatile Compounds Analysis Using GC-qMS
#### 3.4.5 Benzenes and Phenols  
The major benzenes found in these strain-fermented passion fruit wine samples included styrene and benzaldehyde (Table 3). These benzenes' concentration in these wines was below their odor threshold, indicating that their flavor features might not significantly be incorporated into the overall aroma in passion fruit wine. Phenols are yielded through the metabolisms of phenolic acids during wine fermentation, and these compounds can be further metabolized during the wine aging period [42]. It has been reported that the phenols composition is mainly determined by the fruit genotype and these compounds could play a role in improving the wine aroma under low concentration [3]. In this study, 4-vinylguaiacol and 2,4-di-tert-butylphenol were the only two phenols found in these wine samples. These phenols could introduce perfume aromatic scents to these passion fruit wines.
# Interoperability between Building Information Modelling (BIM) and Building Energy Model (BEM)
## 2 Method
### BEM Importing Software
#### 2.2.1 Design Builder  
First of all, the gbXML file was imported in Design Builder, which is a software that allows for the editing of BEM geometry, spaces, and materials. In this research, as the purpose was to study the interoperability from BIM to BEM, any modification was made in the imported model. Nevertheless, at the moment of the gbXML file insert, some parameters were activated and some templates were changed, as data related to the typology template, weather file, ideal loads, and occupation schedule were not detailed in Revit and, therefore, could not be imported with the gbXML file.  
In the interface, \"Import BIM Model\" was selected to import the thermal properties and the model as building blocks. The use of these parameters guaranteed that the materials defined in Revit would be transferred into Design Builder and that the model would be constructed with thermal zones. The activity template chosen was according to the type of the building and, in the openings tab, the layout of the external windows was defined as none. If these elements had not been defined properly, the BEM model would not work correctly and this could not be considered an interoperability error but, rather, a mistake in the configuration of the BEM file.