esa-sceva/satcom-chunk-collection huggingface dataset:
# FEM Based Preliminary Design Optimization in Case of Large Power Transformers
## 2 Proposed Methodology
### FEM Model  
The analytical methods generally compute only the axial components of the magnetic field in the working window of a transformer [3; 20]. Therefore, those effects, which caused by the radial component of the magnetic field, cannot be considered by the analytical methods. The role of the applied FEM model is to provide a more accurate magnetic field calculation in the working window of the transformer. A 2D, magneto-static FEM method is used for this calculation. This technique originally published and implemented by [PERSON] [20]. This simple FEM method is widely used in the industry to determine the load losses and the short-circuit forces and impedance of the transformer [3]. Besides its accuracy, the calculation time of one model is within 1 s.  
The magnetic core can be defined by its relative permeability (\(\mu_{r}\)), it can be some of tens of thousands. During the simulations it was defined as \(\mu_{r}\) = 10,000. It can be a number between 10,000 and 50,000 [3]. However it doesn't effect on the solution, because almost all energy is stored in the non-magnetic regions, where \(\mu_{r}\) = 1, outside of the core. We can also use the assumption of [20], that the radial component of the magnetic flux density is perpendicular to the core. Other regions, including the windings are defined by \(\mu_{r}\) = 1. The magnetic field in the working window of a transformer that is generally nonlinear can be described by the magnetic vector potential \(\vec{A}\) in the following form:  
\[\Delta\vec{A}=\mu\vec{J}, \tag{2}\]  
where \(\mu\) denotes the magnetic permeability. Symbol \(\vec{J}\) denotes the density of field currents in the windings. The boundary condition along a sufficiently distant boundary is Dirichlet type. The magnetic permeability in every cell of the discretization mesh is assumed constant and corresponds to the corresponding magnetic flux density. By the solution of this problem, the value of the short-circuit reactance can be calculated from the total magnetic energy (\(W_{m}\)), evaluated at the peak current (\(I_{p}\)) [9; 20]:  
\[x_{L}=\frac{4\cdot f\cdot W_{m}}{I_{p}^{2}} \tag{3}\]  
The other result of the calculation is the maximum values of the \(B_{ax}\) and \(B_{z}\) values in the windings. These values are used for the load loss calculation in the windings.
## 4 Simulation Results and Analysis
### Utility of Allocation  
Recalling that the proposed algorithm can perform well in the poor uplink state, Figure 9 illustrates the utility curve of different resource allocation algorithms in case of \(\alpha=5\), where it is seen that,  
Figure 8: System throughput for different allocation algorithms in case of \(\alpha=5\).  
Figure 7: Throughput of D2D pairs in different uplink states for proposed WE-I-A algorithm.  
the utility is low when the number of D2D pairs is small, since CUs mainly contribute to the system throughput at that instant. The throughput of D2D pairs increases with the ascending number of D2D pairs, whereas CUs slightly decreases, as shown in Figure 7. Nevertheless, the utility gradually increases, it does not increase continuously due to the constraint on the finite number of RBs even with more D2D pairs. Therefore, the uptrend of the utility curve would slow down with the ascending number of D2D pairs.
# An Approach to Analyze Vulnerability of Information Flow in Software Architecture
## 2 Related Work  
### Software Vulnerability  
In this paper, we will research the definition of information flow vulnerability in software architecture. So, we will firstly review the research on definitions of software vulnerability.  
Research on the definitions of software vulnerability can help researchers better understand the essence of software vulnerability. As a result, researchers have proposed various definitions and elaborations for software vulnerability from different perspectives.  
Current definitions of software vulnerability can be classified into four categories: definitions based on access control, definitions based on state space, fuzzy definitions, and definitions based on security policies, as shown in Table 1.  
Among the four kinds of definitions of software vulnerability, fuzzy definition is widely used in books, dictionaries, standards and cyber security literature. But this definition is too general and abstract. It lacks the clear elaboration of the essence of software vulnerability.  
Compared with fuzzy definition, the definitions based on access control and state space are clearer. But access control definition can be only used for describing the vulnerabilities that violate access control models. State space definition on the other hand is targeted at only UNIX and Windows NT, not suitable for other software.  
By comparison, the definition of software vulnerability based on security policies clearly states that software vulnerabilities exist in every phase of software development cycle, and describes the relationship between software vulnerabilities and security policies that specifies what is illegal, what can happen, and what is allowed in a software system. It emphasizes that in essence software vulnerabilities are errors that violate security policies. However, this definition still does not associate with specific features of different phases in software development lifecycle. Thus, in this paper, on the basis of the definition of software vulnerability based on security policies, and with a focus on the information flow and propagation on architecture level, we will research into the definition of information flow vulnerability in software architecture.
# Analytic Model for U-Nb Liquidus and U-6 Nb Melting Curve
## 3 Application to the Uranium-Niobium System  
We now apply Formula (8) to the uranium-niobium system. Note that, as the previous example of the Mo-W system shows, in a general case, the value of \(C\) can be determined from fitting \(T_{m}(0)\) of the functional form (8) to the experimental and/or theoretical data on the liquidus of a mixture. In the case of U-Nb, we will determine the value of \(C\) by fitting to the experimental data on the U-rich portion of the U-Nb liquidus of Ref. [14]. We consider U as a host and Nb as a dopant. The other parameter values required for the application of Equation (8) to U-Nb, which we use for the determination of the values of \(C\), are \(T_{\ell,1}(0)\), \(T^{\prime}_{\ell,1}(0)\), and \(T_{\ell,2}(0)\), \(T^{\prime}_{\ell,2}(0)\). Given that the latter pair of values follows directly from the published melting curve of Nb of Ref. [20],  
\[T^{\text{Nb}}_{\ell}(P)\,=\,2750\,\bigg{(}1\,+\,\frac{P}{22.6}\bigg{)}^{0.30}, \tag{10}\]  
the following can be deduced:  
\[T_{\ell,2}(0)\,=\,2750\;\text{K},\quad T^{\prime}_{\ell,2}(0)\,=\,36.5\;\text{ K}/\text{GPa}. \tag{11}\]  
However, while the value of the ambient \(T_{m}\) of U is known with certainty, \(1407\pm 2\) K [21], its \(P=0\) value of \(dT_{m}(P)/dP\) has been ambiguous. According to [PERSON] [22], its value can be as low as \(\sim\)30 and as high as \(\sim\)180 K/GPa, a difference of a factor of 6. Some typical literature values are (in K/GPa) \(\approx\)31 from the best fit to the low-\(P\) experimental data of [23], \(\approx\)33 from the best fit to the experimental data to \(\sim\)50 GPa of Ref. [24], \(\approx\)50 from the fit to the data of [24] with the fixed value of \(T_{m}(0)=1408\) K, and 40.5 [25] from the melting curve of U obtained by the Lindemann approach using the Gruneisen parameter given by the analytic model of [PERSON] and [PERSON] [26; 27]. Based on these three references alone, the ambient value of \(dT_{m}(P)/dP\) of U lies in the interval \(\sim\)30-50 K/GPa and cannot be chosen unambiguously. In order to proceed, we need to re-evaluate this value more accurately using the most contemporary research tools available. Such tools are QMD simulations using the Z method.
# Performance Analysis of MIMO System with Single RF Link Based on Switched Parasitic Antenna
## 3 Modeling of MIMO System in SystemVue
### Receiver Modeling  
In order to verify the accuracy and effectiveness of SystemVue simulation platform and test other characteristics of the MIMO system, a more mature super-heterodyne receiver is proposed. Super-heterodyne receiver uses uniform linear array antenna as a receiving antenna to recover two binary digital signal by converting the two received signals with a certain algorithm. Comparing the binary digital signal with the input signals, the system symbol error rate will be acquired.  
The internal structure of the receiver module is shown in Figure 6. The core component of the RF module is the mixer for down conversion. In order to make the best performance of the mixer, the filter, amplifier and isolator are added to the input signal to make the waveform more stable and smooth, and to reduce the interference of vibration signal at the antenna end as much as possible. With tunable attenuator, amplifier and filter after the local oscillator, it is convenient for subsequent demodulation and decoding.  
The specific parameters of each component at the RF transmitter module are listed in Table 3. The receiver RF module uses the elliptic band-pass filter to filter the received RF signal clutter. Other element parameters that are not mentioned in Table 3 are the same as the transmitter module.  
Figure 5: The input and output spectrum of the transmitter link chain. (**a**) The input spectrum; (**b**) The output spectrum.  
Figure 6: Diagram of the receiver module.  
In the actual situation, the signal may be interfered by mixed clutter, resulting in multiple input signal of continuous wave in the spectrum. For example, signals with center frequency of 2400 MHz might produce 2395 MHz and 2405 MHz components due to the channel spacing and there may also exist image signal with center frequency of 2180 MHz in the super-heterodyne receiver.  
In order to ensure that the receiving end will not affect the operation of the whole system, the input and output spectrum of the RF link should be measured, as is shown in Figure 7. The result compares input and output spectrum of RF receiver, with an input center frequency of 2400 MHz and a peak power of \(-120\) dBm. The center frequency of the output IF signal is 220 MHz, with a peak power of \(-13\) dBm, which increased 107 dBm compared with the input terminal. It proves that the RF link receiver effectively realized the conversion process and the output power matches with the parameters set up previously.
# A Reliable Publish-Subscribe Mechanism for Internet of Things-Enabled Smart Greenhouses
###### Abstract  
Messaging protocols for the Internet of Things (IoT) play a crucial role in facilitating efficient product creation and waste reduction, and in enhancing agricultural process efficiency within the realm of smart greenhouses. Publish-subscribe (pub-sub) systems improve communication between IoT devices and cloud platforms. Nevertheless, IoT technology is required to effectively handle a considerable volume of subscriptions or topic adjustments from several clients concurrently. In addition, subscription throughput is an essential factor of the pub-sub mechanism, as it directly influences the speed at which messages may be sent to subscribers. The primary focus of this paper pertains to a performance assessment of the proposed message categorization architecture for the Message Queue Telemetry Transport (MQTT) broker. This architecture aims to establish a standardized approach to pub-sub topics and generate new topics with various performance characteristics. We also standardize the form of MQTT protocol broker topic categorization and payload based on greenhouse specifications. The establishment of topic classification enhances the operational effectiveness of the broker, reduces data volume, and concurrently augments the number of messages and events transmitted from the greenhouse environment to the central server on a per-second basis. Our proposed architecture is validated across multiple MQTT brokers, including Mosquitott, ActiveMQ, Beyvwise, and EMQ X, showing enhanced flexibility, extensibility, and simplicity while maintaining full compatibility with greenhouse environments. Key findings demonstrate significant improvements in performance metrics. The message processing time for the proposed Active MQ broker was increased approximately five-fold across all QoS levels compared to the original. Subscription throughput for the Beyvwise MQTT Route 2.0 broker at QoS0 reached 1453.053, compared to 290.610 for the original broker. The number of messages in the Active MQ broker at QoS0 surged from 394.79 to 1973.95. These improvements demonstrate the architecture's potential for broader IoT applications in pub-sub systems.  
MQTT protocol broker; Internet of Things; publish-subscribe systems; data volume; subscription throughput +
Footnote †: journal: Applied  
1
# Antigate Melt-Electrowritten Biodegradable Mesh Implants for Enhanced Pelvic Organ Prolapse Repair
## 2 Methodology
### MEW Device Calibration  
The printing process involved two main steps: calibration of the MEW device, followed by the fabrication of the desired meshes. For calibration purposes, 90 \(\times\) 90 \(\upmu\)m square meshes were printed with fiber diameters of 160, 200, and 260 \(\upmu\)m. Fiber diameter was measured using a ZEISS AxioPhot microscope (ZEISS, Oberkochen, Germany) in combination with OLYMPUS Stream Basic software.  
Calibration began with technical-grade PCL due to its lower cost compared to medical-grade PCL. Only square meshes were printed during this step to minimize material waste. As technical-grade PCL is supplied in filament form, it was cut into small segments--referred to as pellets--for compatibility with the pellet extruder. The distance between the nozzle and collector (Z-height) was fixed at 0.3 mm to eliminate its influence as a variable parameter. Table 1 summarizes the printing parameters tested during the calibration process.  
\begin{table}
\begin{tabular}{l c} \hline Temperature (\({}^{\circ}\)C) & 160–180–200–220 \\ \hline Collector’s Speed (mm/min) & 500–1000–1200–1400–1800 \\ \hline Voltage (kV) & 2.63–3.03–3.23–3.43–3.63–4.03–4.63 \\ \hline \end{tabular}
\end{table}
Table 1: Technical-grade PCL calibration printing parameters.  
To initiate the calibration process, square meshes with a fiber diameter of 260 \(\upmu\)m were first printed to minimize material waste. Subsequently, meshes with fiber diameters of 160 and 200 \(\upmu\)m were fabricated. A single-parameter variation approach was adopted to ensure precise calibration, in which only one printing parameter--such as temperature, collector speed, or applied voltage--was adjusted at a time.  
The initial printing parameters were selected based on a previous study [9], which reported that an ideal voltage for printing PCL meshes was 6 kV. However, in the present setup, stable printing was achieved with voltage values in the range of 3-4 kV.
# A Review of Reactor Designs for Hydrogen Storage in Clathrate Hydrates
## 2 Hydrate-Formation Technologies
### High-Pressure Optical Cell  
To study the structure of hydrate via spectroscopy techniques such as Raman, in many studies, a small optical cell with and without gas flow is employed [PERSON] et al. (2018); [PERSON] et al. (2018); [PERSON] et al. (2018); [PERSON] et al. (2018). Figure 10 shows one of the designs used by [PERSON] et. al [PERSON] et al. (2018), where a hydrate forms between two sapphire windows, a resistance temperature detector (RTD) used for temperature measurement and the pressure of the chamber is installed on the gas inlet line on the top of the reactor. An external cooling channel was also designed to control the temperature of the reactor. In this design the whole volume of the hydrate-formation chamber is only 1.4 mL.  
The thermodynamics of the hydrate-formation/dissociation can be studied using a high-pressure micro-differential scanning calorimeter. In this system, the pressure can be gauged between 0.1-60 MPa, and the temperature can be controlled accurately (\(\pm\)0.05 \({}^{\circ}\)C) in the range of \(-\)160 to 200 \({}^{\circ}\)C. For the high-pressure DSC sample cell with inner volume of 4 mL was composed of a specific alloy or ceramics to eliminate corrosion or contamination [PERSON] et al. (2018); [PERSON] et al. (2018).
## 3 Methods
### Measuring Sea Ice Motion and Deformation
#### 3.1.3 Validating Satellite Image-Derived Sea Ice Motion  
A comparison with a Lagrangian buoy track has been generally applied to the validation of satellite image-derived sea ice motion [4; 37; 38; 39; 40; 41]. For validation of the sea ice motion, first, the locations of the ITP80 buoy (recorded in the closest time of satellite image acquisition times) were linearly interpolated at the prior and posterior image acquisition times. The motion vector of which the center was closest from the center of two time-interpolated locations was retrieved, and the validation performed by comparing the selected satellite image-derived motion vector with the vector connecting the time-interpolated buoy locations [17; 42]. The validations for moving distance, direction and velocity were performed for both cases of 4 m and 15 m spatial resolutions. Differences between the GPS based ITP80 motion and satellite image based motion were considered in statistical analyses to estimate bias and root mean square error (RMSE). During the validation, other error sources such as GPS positioning uncertainty and image geolocation errors were also considered.
# Extraction, Characterization, and Applications of Pectins from Plant By-Products
## 2 Structure and Production of Pectins
### Structure Classification  
#### 2.2.1 Degree of Methylation  
Pectins can be classified according to their degree of methylation (DM) [1] expressed as a percentage, which corresponds to the number of methylated carboxylic functions per 100 units of galacturonic acid in the main chain. According to their degree of methylation, a distinction is made between:  
* High-methoxy (HM) pectin (Figure 3A) with a DM > 50%, mostly present in nature as native pectin.
* Low-methoxy (LM) pectin (Figure 3B) with a DM < 50%. This LM pectin is only obtained after demethylation by enzymatic (methylesterases) or alkaline treatments of HM pectin. There are also several unconventional sources of low-methoxy pectin.  
The methoxyl content reflects the dispersibility of pectin in water and its ability to form a hydrogel [24]. Low- and high-methoxy lectins have different physico-chemical properties and thus diverse applications. The degree of methylation of the extracted pectin depends on the type of plant, its age, and degree of maturation (notably for fruits). Therefore, pectins from fruits do not have the same degree of methylation. Pectins with a low degree of methylation form gels in the presence of calcium ions whereas those with a high degree of methylation gelify with the addition of different sugars, such as sucrose, under acidic conditions [10; 25]. The extraction methods affect the structure of pectin.
## 3 ITF Diagnosis Based on Current Vector Pattern
### Fault Diagnosis Algorithm  
Through the co-simulation of the FEA model and circuit analysis, the current vector pattern under ITF conditions is analyzed, and a fault diagnosis algorithm is proposed.  
Figure 4: Current vector pattern for healthy and faulty conditions.  
Figure 5: Current vector pattern for the fault resistance and number of fault turns when the ITF occurs in Phase A: (**a**) fault resistance; (**b**) the ratio of shorted turns; (**c**) speed; (**d**) slip.  
Based on the co-simulation results, an ITF diagnosis algorithm tailored for \(\Delta\)-connected windings is developed, as illustrated in Figure 6. In this approach, a positive-sequence voltage is applied to the IM, and the stator current in the stationary coordinate frame is obtained using the measured three-phase currents. Since the stationary coordinate transformation is employed, the use of an encoder is not required, which simplifies the implementation of the diagnostic method. The angle of the maximum current vector is calculated from the stationary-frame current, and fault detection is performed based on this angle. As summarized in Table 2 and illustrated in Figure 6, the proposed algorithm enables effective identification of the faulted phase without the need for complex signal processing or rotor position information.
# An Abstract Framework for Non-Cooperative Multi-Agent Planning
## 5 Case Study
### Goal Allocation  
First, goal allocation is performed through an auction system. In this way, truck agents can bid for the goals they are most interested in based mainly on the distance of origin of the package and the current position of the truck. We assume that the payment or reward they receive for packages is proportional to the distance between the origin and destination of the package, so we can consider that there are no particular or superior incentives for specific packages. In other words, all packages are worth the same depending on the route to be taken to deliver it. This implies that the agents will prioritize the packages that are closest to them and those that come to them in passing with other deliveries.  
The goal allocation problem is similar to what is traditionally called resource allocation. Thus, the Bertsekas auction algorithm [65; 66] can be used to solve the goal allocation in our framework. This algorithm can be used when there are \(n\) agents and \(m\) goals to assign (with \(m\geq n\)). Each goal can be assigned to an agent with a certain cost and the main objective of the process is to minimize the total cost of the global assignment. The idea of this algorithm is that agents bid for their preferred goal (the least expensive) and the value of that bid is the difference between the best cost and the second best cost (for this agent). In addition, each goal has a price (which starts at 0), and, if there are several agents who want the same goal, it increases in price. A goal is assigned to an agent if the agent's bid is the highest. This process is repeated until all goals are assigned.  
Based on the representation of Figure 4 of this case study, we assume that the costs of each of the agents associated with the delivery of each of the packages are those shown in Table 1. Therefore, it is easy to assume the allocation that will be made considering the costs. It should be noted that these costs are proportional to the distance between the agent and the point of origin of the package, as well as the distance to take the package from its origin to its destination. Once the [PERSON] auction algorithm has been applied (for simplicity's sake, we obviate its application), agent A would be assigned the package \(p_{1}\), agent B the package \(p_{2}\), and package \(p_{3}\) would be assigned to agent C.