esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Controllability of Processes without Surprises
## 7 Checking Semi-Dynamic Controllability with STNUs
### Checking Semi-Dynamic Controllability  
In Algorithm 1 we describe a possible procedure for checking semi-dynamic controllability. First, a process model \(P\) is mapped into an STNU \(T\) by applying the mapping rules. A data structure \(S_{T}\) is created for listing the STNU nodes representing semi-contingent activities, and for identifying sudden termination patterns.  
The STNU \(T\) is checked for dynamic controllability by applying any constraint propagation procedure \(check\_dc(T)\). As discussed above, the procedure has the side effect of computing the closure of the set of explicit and implicit constraints. If the procedure returns True, the STNU, hence the process, is dynamically controllable (\(dc\)); otherwise, the algorithm terminates. If \(T\) is \(dc\), procedure \(find\_stp(T,S_{T})\) searches and returns all STPs in the STNU.  
Figure 11: STNU derived from the example process shown in Figure 2, transformed as in Figure 10.  
Then, the following three steps are repeated as long as the network is \(dc\) and there are unresolved STPs:  
1. For each STP \(p\) found, edges corresponding to the constraints to resolve \(p\) to avoid the STP are added to \(T\).
2. Check for dynamic controllability is performed, deriving additional implicit constraints that may have been introduced with the new edges.
3. A new search for unresolved STPs is performed. If, at the end of the repeated execution of the three steps, \(T\) remains \(dc\), then it is also \(sdc\) and the algorithm returns True.  
Applying the algorithm, one can verify that the process of the running example is not \(sdc\), due to the negative cycle derived in Figure 12 between \(W_{F}\).\(s\) and \(A.e\), formed by the edges with weights \(-6\) and \(5\).  
**Theorem 14**.: _Algorithm 1 is a sound and complete procedure to check the semi-dynamic controllability._  
Proof.: In [9] it has been shown that the procedure we apply for dc check is sound and complete. In Section 5 we enumerated all constellations between a contingent and a semi-contingent activity that could lead to a sudden termination. For each of the constellations we derived additional constraints and we showed that these const  
Figure 12: STNU resulting from the application of Algorithm 1 to the process of Figure 2.  
sufficient to be able to avoid sudden termination, if the network with these additional constraints is dc. The procedure \(find\_stp(T,S_{T})\) delivers all constellations described in Section 5. The procedure is monotonic as it only adds constraints but does not remove a constraint.
The techniques based on the iterative analysis of impedance curves consider one-dimensional models and monomodal resonances which use different resonance modes and resonator shapes to identify different material parameters, and also provide an alternative to standard procedures and their results that are appropriated for the three-dimensional modeling of piezoceramics [12; 13]. In a previous work, material coefficients of BNBT ceramics were determined as a function of the temperature using an iterative process of analysis of impedance curves at the monomodal electromechanical resonances of thickness-poled thin disks (radial) and plates (shear) [1; 4; 14]. Measurements were made as the sample was heated. The studied ceramics were obtained by hot pressing (at 800 \({}^{\circ}\)C for 2 h) from autcombustion sol-gel powder and recrystallization (at 1050 \({}^{\circ}\)C for 2 h) to minimize grain size and non-stoichiometry [15]. Depolarization temperatures were obtained well above 100 \({}^{\circ}\)C when they were measured from uncoupled shear resonances of, thickness-poled, thin plates. Shear resonances were still detected at 140 \({}^{\circ}\)C [1]. When depolarization was determined from the planar or thickness mode of thin disks, thickness-poled, depolarization took place at 100 \({}^{\circ}\)C. This distinct thermal evolution have been observed in the BNBT100x system, not only for BNBT6 [1; 4; 14], but also for BNBT4 [16; 17]. Since each resonance mode studied is related with a different set of material parameters, the observed phenomenology [1; 4; 14; 16; 17] reveals the anisotropy of the depolarization process when studied by measurement of the electromechanical resonances. However, in these previous works the resonance modes were measured using different samples, specially constructed to uncouple different resonances. Here we are exploring the thermal depolarization and related phase transition from measurements in a sample where all modes, both pure and coupled modes, contribute together. In this way, we can assess such electromechanical anisotropy of the thermal depolarization beyond any doubt. In this work, two different results are presented. On the one hand, the evolution of the resonance modes (radial, thickness, and other complex modes) of a BNB76 thin disk is evaluated as a function of the temperature. The evaluation is made on the resonance frequency and in the modulus of the electrical impedance. On the other hand, we jointly characterize the thermal evolution of the material parameters, extracted from analysis of the impedance spectra using only one sample by methods that are a feasible and advantageous alternatives to the current standards of measurements.
# An Energy Aware Adaptive Sampling Algorithm for Energy Harvesting WSN with Energy Hungry Sensors
## 5 Experimental Results
Due to the additional states in deployment (II), Equation (4) has to be rewritten such that includes all of the states described in Figure 6:  
\[t_{ACTIVE}=t_{TX}\ +\ t_{RX}+t_{SensorSamp}+t_{PROC}+t_{SD}+\ t_{ZB}+t_{INIT} \tag{12}\]  
where \(t_{SensorSamp}=\sum_{i=1}^{n}t_{i\ sensor}\) is the sum of the sampling times for all of the sensors, \(t_{SD}\) is the time to write the message to a SD card and \(t_{ZB}\) is the time to join the ZigBEE network. The total active time (\(t_{ACTIVE}\)) for the microcontroller to perform all of the tasks in this deployment was 173 s. The sleep period (\(t_{SLEEP}\)) was set to be 10 s which makes the total period \(T\) for the measurement scenario to be \(T=183\) s.  
Due to the long sampling period of the sensors, the energy required for sampling the five sensors (Table 2), has the most significant effect on the overall energy consumed by an application duty cycled node. This is purely due to the associated warm up period, which is 41 s for four of the sensors and 1.6 s for the O\({}_{2}\) sensor. The energy harvested by the solar panel in deployment (II) was estimated through the information of the battery level, which was acquired with measuring the battery voltage. The period \(T\) for deployment (II) was 14,400 s. Having the data of the battery level for every 14,400 s (or every 4 h), and knowing how much energy is spent in the active and the sleep state allows the derivation of a rough figure for how much energy is harvested by the solar panel over the 4 h. Using Equation (11), we can calculate the consumed energy after each cycle \(T\). Using Equation (12), the time taken to execute all of the sub states of the active state which for deployment (II) was 173 s. Subtracting this time from the period \(T\) will give the period spent in sleep state (\(t_{SLEEP}=14\),227 s). If it is assumed that the battery has capacity \(C\) (mAh), the battery level at moment t seconds was \(X(t)\) (in %), and after 4 h the level is \(X(t+14400)\), then the harvested energy with the solar panel could be estimated using Equation (13):  
\[P_{Wind}=\frac{1}{2}\eta\ A\ \rho c_{p}\hat{\sigma}_{AVG}^{3} \tag{8}\]0  
If the result from Equation (13) is negative, we assume that no energy has been harvested. The power harvested is then calculated using Equation (14). \[P_{Wind}=\frac{1}{2}\eta\ A\ \rho c_{p}\hat{\sigma}_{AVG}^{3} \tag{8}\]1  
Figure 7 gives the estimated power harvested by the solar panel for the days of deployment (II), derived using Equations (13) and (14). The battery used for both the calculations and the deployment (II) had a capacity of 800 mAh. The solar panel had the following specifications: \(P_{MAX}=1.3\) W, \(V_{MAX}=6.5\) V, \(I_{MAX}=205\) mA and dimensions of 0.111 m \(\times\) 0.091 m.
## 3 Finite Element Simulation
### Thermal Analysis  
The same conditions as in the previous structural analysis were used in the next analysis. Identical pressure of the pad was applied as in the structural analysis, and it was assumed that the pressure would be applied continuously over time. The boundary conditions are found in Figure 7.  
In order to check the process through which the heat of the disc is dissipated over time, the heat flux was set as a function of time through calculation [18]. In addition, with  
Figure 6: Von Mises stress distribution.  
Figure 7: Boundary conditions of thermal analysis model.  
regard to the heat flux, it was assumed that all the kinetic energy of the running vehicle would be converted into thermal energy due to the friction between the brake disc and the pad during braking. The area of the heat flux was set to the area where the disc rotor is compressed by the pad so that frictional heat is generated. The kinetic energy generated during running and the heat flux due to friction were calculated with Equations (4) and (5) and Table 1. The derived values are as follows [19]:  
\[\text{KE}=\frac{1}{2}Mv^{2} \tag{4}\]  
\[\text{q}=\frac{Q}{tA_{c}} \tag{5}\]  
where \(M\) is the weight of the vehicle (kg), and \(v\) is the initial speed (m/s). \(Q\) is the thermal energy, \(t\) is the stopping time (s), and \(A_{c}\) is the surface of the pad that comes into contact with the disc (\(m^{2}\)).  
The distribution of the braking torque between the front and rear axles was assumed to be 7:3, and the heat flux of each front axle is shown in Equation (6) below [20].  
\[q_{front}=\frac{0.7q}{2}. \tag{6}\]  
The initial temperature of the disc was 20 \({}^{\circ}\)C, and the convective heat transfer coefficient of air was applied to all contact areas between the disc and air using the stagnant air-simplified case data provided by the ANSYS program.  
The derived temperature distribution of the brake disc is shown in Figure 8. Due to the cooling effect of the ventilation blades, the temperature around the ventilation blades was lower than that of the surroundings, and distributions of higher temperatures can be identified in areas closer to the outside of the disc. As the temperature rises, the durability of the disc decreases rapidly. Therefore, the ventilation performance should be improved because high temperatures can cause fatal thermal problems such as permanent disc deformation and changes in the friction coefficient.
# MT-VTON: Multilevel Transformation-Based Virtual Try-On for Enhancing Realism of Clothing
## 5 Experiments  
### Dataset  
Instead of using the Zalando dataset [1], we selected the Korean fashion dataset maintained by AIHub [11] for our research, as we believe it better aligns with real-world scenarios. We observed that clothing items within the Zalando dataset are pre-processed and include volumetric effects. Conversely, most retail platforms have images of flattened clothes. This distinction implies that models trained on the Zalando dataset might not be practical for real-world applications. In contrast, the Korean fashion dataset presents images of flattened clothes, as illustrated in Figure 3, which are more closely related to real-world conditions.  
The dataset was divided into two subsets, one for training and another for validation. The training set included 33,170 clothing items and 94,089 individuals wearing those items in various poses. In our experiment, we restricted our focus to images where the individuals were facing nearly straight ahead, amounting to 16,399 images. The validation set comprised 7978 clothing items and 12,374 person images. Similar to the training set, we only utilized 2036 person images facing forward.  
Figure 3: Demonstration of the Korean fashion dataset. As illustrated, the clothing items are presented in a flattened manner, lacking cloth folding details.  
Figure 2: The cloth refinement process. The first row of this figure represents the procedure of computing the logo regions’ mask using the variance map and the active contour. The second row indicates the logo substitution process using the mask.  
Each item in the dataset comes with cloth segmentation maps. For upper-body clothing, segmentation maps involve masks for the left and right arms, as well as a mask for the torso. For pants, the segmentation maps include left and right leg masks and a hip mask. Skirt segmentation maps have a single mask encapsulating the entire skirt region. Additionally, human parsing maps are also available for each data item. By leveraging these parsing maps, we extracted the warped cloth segmentation maps, which were utilized as ground truth maps during the training of the segmentation warping process. All the images, initially with dimensions of \(1280\times 720\), were resized to \(512\times 256\) after cropping out white spaces.
###### Abstract  
The reduced inertia in the power system due to renewable energy integration introduces operation challenges in frequency stability and control. The current options for virtual inertia and frequency support are limited by the energy resources and the power electronic interface. Considering the demand on response speed and energy capacity, a general virtual synchronous machine (VSM) control based on various forms of energy storage systems (ESS) is proposed. The steady-state energy variation of energy storage is found to be proportional to the virtual damping or governor gain, while inversely proportional to the integral gain of system frequency control. It is found that the size of energy storage can be at the second time scale (for example, 6.8 p.u.-s) for VSM implementation, which is significantly smaller than the conventional hour-scale energy storage in the power system. Based on energy dynamic analysis, stability requirement, and bandwidth separation rules, an energy recovery control is designed to maintain constant state of charge (for example, 50%) while avoiding conflicts with frequency regulation. The time scale of the designed energy recovery control loop (for example, hundreds of seconds) is longer than the secondary frequency control. The effectiveness of the proposed control is verified through comprehensive case studies.  
virtual synchronous machine (VSM); energy storage system (ESS); energy recovery control; bandwidth separation +
Footnote †: journal: Applied Sciences  
control parameters or coordination with protection, which have been extensively reported in the literature [3; 4; 5; 6; 7; 8]. By contrast, less research covered the slower dynamics of VSM such as the energy management in VSM operation.
# A 3-D Metal-Printed Dual-Polarized Ridged Waveguide Slot Array Antenna for X-Band Applications
## 5 Conclusions  
We designed, constructed, and experimentally verified dual-polarized X-band ridged waveguide slot array antennas. The proposed design has a compact and simple structure suitable for fabrication by 3D printing technology. The unit cell simulation method was applied to easily extract the slot characteristic parameters, from which the array configuration achieved fast and accurate results. Two orthogonal linear polarizations were performed with the longitudinal slot for the VP array and the V-shaped slot for the HP array arranged alternately to reduce mutual coupling and to obtain low cross-polarization.  
The dual-polarized array achieved a wide operating bandwidth through a center feed technique that was specifically designed for each of the VP and HP linear arrays and excited with a coaxial probe attached directly to the RWG. The measured impedance bandwidths were 12.2% and 11.2% for the VP and HP arrays with peak realized gains of 14.6 and 15.5 dBi, respectively. The high isolation between the two polarizations was over 40 dB, and the low cross-polarization was better than \(-40\) dB in both the \(E\) and \(H\) planes over the entire operating bandwidth.  
The obtained results show that the proposed antenna had a wide bandwidth, good polarization purity in a compact aperture, and high mechanical structure stability, thus, being suitable for radar and communication applications. Furthermore, the proposed design shows the suitability for the subarray feeding system of a large antenna array. Therefore, this antenna is an attractive candidate for the development of large dual-polarization arrays in communication and radar systems.  
Conceptualization, T.V.T., J.P. and K.C.H.; methodology, T.V.T. and J.P.; software, T.V.T. and J.P.; validation, T.V.T., J.P., C.M.S. and K.C.H.; formal analysis, T.V.T. and J.P.; investigation, T.V.T. and J.P.; resources, T.V.T., C.M.S. and S.S.; data curation, T.V.T., J.P., C.M.S. and S.S.; writing--original draft preparation, T.V.T.; writing--review and editing, T.V.T. and K.C.H.; visualization, T.V.T.; supervision, K.C.H.; project administration, K.C.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a grant-in-aid of HANWHA SYSTEMS.  
Not applicable.  
All data are included in the study.  
The authors declare no conflict of interest.
## 4 Routing in 3D Sensor Network without Internal Holes
### Theoretical Insights
#### 4.1.3 Spherical Harmonic Function  
Similar to that in a smooth setting, we can impose Dirichlet boundary conditions on the discrete volumetric harmonic function, by fixing the value of \(f\) on certain vertices, \(v_{i}\in\mathbb{V}_{c}\), where \(\mathbb{V}_{c}\) is the set of constraint vertices. It is important to control boundary conditions in this work. Particularly, spherical boundaries are desired to support greedy routing.  
The spherical harmonic function maps a closed topologically spherical surface ( _i.e._, a surface with no holes) to a sphere. It is similar to the volumetric harmonic function. They share the same harmonic energy as defined in Equation (4), but differ in how to assign weight \(k_{ij}\). For a topologically spherical surface, an edge is shared by two faces only. For example, given edge \(e_{ij}\) shared by triangle faces \(f_{ijk}\) and \(f_{jil}\), its weight is defined as:  
\[k_{ij}=\frac{1}{2}(\cot\theta_{l}+\cot\theta_{k}) \tag{5}\]  
where \(\theta_{l}=\angle v_{i}v_{l}v_{j}\) and \(\theta_{k}=\angle v_{i}v_{k}v_{j}\).  
[PERSON] _et al._ proposed an efficient computational method for spherical harmonic function using progressive optimization in [36].
## 4 Results and Discussion
### Buffer Measure  
#### 4.3.1 Buffer Scheme  
To tackle the challenges posed to the stability and safety of HSTs when passing through wind barrier areas in crosswinds, this section proposes a buffer structure installed at the end of the bridge wind barrier. The objective of the buffer structure is to provide a smooth transition for HSTs between the interior and exterior areas of the bridge wind barrier. The buffer structure consists of three sections of curved wind barriers with gradually varying porosities, as depicted in Figure 14a. The curved wind barrier on the bridge has a porosity of 30%. The three sections of the buffer structure are consistent with the curvature of the curved wind barrier and have porosities of 40%, 50%, and 60%, respectively.  
Figure 14b illustrates a schematic diagram of buffer structures with three different lengths (45 m, 90 m, and 135 m). Taking the process of the HST entering the wind barrier area of the bridge as an example, there is a distance of 290 m between the nose tip of the head car and the bridge wind barrier. At a crosswind speed of 15 m/s, the HST travels through the buffer structure at a speed of 250 km/h until reaching the middle of the bridge wind barrier area, covering a running distance of approximately 415 m.  
To examine the effects of buffer structures on the sudden change in aerodynamic load coefficients of the HST, two evaluation indices are introduced: the rate of reduction in the amplitude of changes in the aerodynamic load coefficients (\(D\)) and the rate of reduction in the amplitude of the rate of changes in the aerodynamic load coefficients (\(D^{\prime}\)).  
\[D=\frac{\Delta C-\Delta C_{S}}{\Delta C}\times 100\% \tag{11}\]  
\[Dt=\frac{\Delta C^{\prime}_{\max}-\Delta C_{S}t_{\max}}{\Delta C^{\prime}_{\max }}\times 100\% \tag{12}\]  
where \(\Delta C_{S}\) represents the amplitude of changes in the aerodynamic load coefficients and \(\Delta C_{S}t_{\max}\) represents the amplitude of the rate of changes in the aerodynamic load coefficients within 0.035 s when the HST enters the wind barrier area after installation of buffer structures, respectively.  
Figure 14: Buffer scheme at the end of the curved wind barrier: (**a**) schematic of the buffer structure and (**b**) schematic of buffer structures with three different lengths.
## 3 Proposed Algorithms
### Fitness Function
#### 3.2.1 K-coverage Calculation  
During the execution of the function, the number of points which require k-coverage as well as the degree of coverage are checked. For every spot, the algorithm withholds the necessary number of nodes which is equal to the degree of coverage of that spot; at the same time it keeps in its memory the nodes which have been already used for the coverage of previous spots. Each time, the Euclidian distance between the spot and the engaged group of sensor nodes to cover this spot is calculated. If the required coverage is satisfied, then the distance which occurs after the calculation is equal to zero. In every other case, the sum of the distances of the nodes from the spot is calculated. This process is repeated for all the spots which require k-coverage. The return value is the algebraic sum of all the distances that have been calculated from all the spots. It becomes apparent that if all the spots are covered, then the return value of the function is equal to zero. The methodology upon which the algorithm was developed is presented in Figure 1:
## 2 Materials and Methods
### Testing Methods
#### 2.3.3 Cyclic Compression test  
For the cyclic compression test, two cases were studied: (1) Single unloading/reloading cycle and (2) three unloading/reloading cycles. The cylinder samples 100 mm \(\times\) 50 mm for each series were subjected to cyclic loading to displacement levels of 0.1 mm, 0.2 mm, 0.3 mm,......, 0.9 mm, 1 mm, 2 mm, 3 mm, 4 mm, and 5 mm. At first, the specimens were loaded at a displacement rate of 0.1 mm/min. After reaching the displacement level of 2 mm, the loading rate was increased to 1 mm/min. During the first case, the sample was subjected to loading to a prescribed value of axial displacement, and then unloading was done up to a target load level by reducing the axial displacement. Reloading of the sample was performed to the next given displacement. In the second case, three unloading/reloading cycles were applied to the other cylinders of each series at each given unloading displacement level. For both cases, the target load level of 2 KN was affixed during the unloading and reloading of the sample. Two linear variable displacement transducers (LVDTs) were attached at 180\({}^{\circ}\) apart to measure the axial displacements. All the loading arrangements were automatically achieved by a computer program.  
Figure 1: Experimental loading pattern.