esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiment and Analysis
### Comparative Analysis of Splitting Performance
For example, SENet can improve the model's sensitivity to key features such as the black dorsal fin, which enhances the model's ability to segment these important parts. At the same time, SENet can also suppress some irrelevant features in the segmentation process, reducing the possibility of wrong segmentation. Based on the aforementioned experimental findings, in our study, precise segmentation of body parts is not merely a technical matter but also a means to establish a more accurate and reliable foundational dataset for future phenotypic measurement research. Figure 10: Comparison of network model IoU values.
# Reversible Data Hiding in Encrypted Color Halftone Images with High Capacity
## 4 Experimental Results and Discussions
### Computational Complexity  
For the proposed scheme, the computational complexity of encrypting and decrypting pixels is \(\mathcal{O}(N)\) (\(\mathcal{O}\) notation is used to classify algorithms according to how their running time or space requirements grow as the input size grows [32].) because they are linear-time algorithms and the  
Figure 9: Comparison of image quality with different embedding capacity: (**a**) image quality in terms of PSNR; and (**b**) image quality in terms of SSIM.  
running time increases at most linearly with the size \(N\) of the image. The computational complexity of embedding and extraction procedure is \(\mathcal{O}(mn^{2})\) according to Equation (1), where \(m\) and \(n\) are the length of the embedded bits of each block and the length of each block, respectively.
## 3 Calibration Circuit and Testbed for a Large Number of Antennas
### Testbed Configuration and Performance  
Figure 5 shows the configuration of the testbed used to realize the calibration scheme. The main purpose of this testbed was to clarify the amplitude and phase-error characteristics for a 16-element MIMO system. \(T_{i}R_{j}\) (\(i=1\)-\(16\), \(j=1\)-\(16\)) can be obtained from a directional coupler (DC) and divider. The number of transmitters is 16, and 16 multi-user transmission is assumed at the maximum. The radio frequency is 2.425 GHz and the bandwidth is 50 MHz. This testbed can be utilized from 400 MHz to 6 GHz by changing the local oscillator in Figure 5.  
Because the isolation between the transmitters and receivers is one of the key important issues when realizing the proposed calibration, on-off switches under DCs and an attenuator with between 1 and 16 dividers is implemented in our testbed.  
Figures 6 and 7 show the phase and amplitude errors at the receivers given by the testbed illustrated in Figure 5, both before and after calibration. In order to evaluate the basic performance, the continuous narrowband signal using minimum shift keying is transmitted, as shown in Figure 5. For a 10 ms interval, the transmit signals are switched for the calibration. The on-off switches in Figure 5 are used to switch the transmit signal from each transmitter.  
For these measurements, the amplitude and phase errors yielded by the DCs and dividers in Figure 5 were measured in advance and removed. In addition, Figures 6 and 7 show the relative phase and amplitude errors of receiver \(k\) Txk against Tx1. As can be seen from Figure 5(a), the phase error varied for each transmitter prior to calibration, and these values ranged from \(-60^{\circ}\) to \(150^{\circ}\). In contrast, when the calibration circuit in Figure 5 was applied, the phase errors could be eliminated, and these values were reduced to less than \(1.5^{\circ}\). We note that, in addition, we have clarified the transmitter phase errors using the calibration circuit in Figure 5. Similarly, as can be seen from Figure 7, the amplitude errors could be reduced from \(\pm 1\) to \(\pm 0.1\) dB using the calibration circuit shown in Figure 5. Therefore, the effectiveness of the hardware in the testbed and the proposed calibration met  
Figure 5: Testbed configuration: (**a**) Transmitters, and (**b**) Receivers and calibration circuit.  
Figure 6: Phase errors of Txk against Tx#1: (**a**) without, and (**b**) with calibration.  
Figure 7: Amplitude errors of Txk against Tx#1: (**a**) without, and (**b**) with calibration.
## 3 Antenna Structure Design  
The designed antenna structure is shown in Figure 3. Figure 3a represents the bottom outline of the antenna, Figure 3b represents the main view of the overall structure of the antenna, and Figure 3c represents the structure diagram of the internal coaxial structure. The antenna is a circular polarization omnidirectional antenna designed based on the main structure composed of a circular waveguide and a truncated cavity. The packing material between the inner and outer conductors is polytetrafluoroethylene, whose relative dielectric constant and the overall wall thickness of the waveguide t = 2 mm. The whole antenna structure can be divided into three parts, namely, the X-shaped radiation unit, the stepped coaxial matching section and the conical line feeding section. The length and width of the X-shaped radiation unit correspond to the parameters H and W, respectively, in Figure 1. Based on the coaxial line transmission impedance theory, three sections of coaxial impedance with different characteristics were designed to achieve impedance transition and reduce signal attenuation. The conical line feed part uses a flat truncate to connect the SMA interface to the impedance matching section, so that the small size feed port is connected to the large matching section without changing the impedance, and the loss caused by the size change is reduced. The structural dimension parameters of each part are shown in Table 1:  
Figure 2: Overall impedance of the antenna: (**a**) Schematic diagram of coaxial cascade (**b**) Schematic diagram of overall impedance matching of the antenna.
## 7 SMEP Algorithms
### AuxDataQueue Compression Algorithm  
The _AuxDataQueueCompression_ algorithm is the algorithm for compressing a sensor data sequence in _AuxDataQueue_. This algorithm is illustrated Algorithm 1. ```
Parameters \(m\): the number of zones such that DataQueue[0..2\"] Global Variables Local Variables \(01\) zoneCValue: the number of compressions of sensor data sequence in the DataQueue[m - 1] zone \(c\): the number of compressions for sensor data sequence in AuxDataQueue \(03\) DQMax\(ABP\): the greatest absolute position covered by DataQueue \(04\) ZoneBpSq0, ZoneBpSq1: bit string variables for an \(l\)-bit sequence generation Procedure \(01\) Set \(z\)onceValue = \(Zones[m-1]\): \(02\) Set \(DQABP=\sum_{j=0}^{j=m-2}2^{m-j-1}.2^{Zone(j)}\ +2.2^{Zone(m-1)}\) \(03\) Initialize \(c=1\) \(04\) Initialize ZoneBpSq0 = cb (an empty bit string) \(05\) while (\(c\leq zzoneCValue\)) do Set \(\mathrm{C}\) to the \(2\)-size compression interval covering on a sensor data sequence in \(AuxDataQueue10\).2 \(z^{\mathrm{AuxDataUpdate1}}1\) \(07\) InitializeZoneBpSq1 = cb (an empty bit string) \(08\) Set \(AuxDataQueue10\) = ZoneCimplRistor[m - 1, cl.Value \(09\) for each \(I_{c-1,2k}\) in \(c\) such that \(0\leq k\leq 2^{zzoneCValue}\)) do  Select \(S_{c-1,j}\) from \(I_{c-1,2k+1}\) by the _selection by pattern_ Rule \(11\) Select \(S_{c-1,p}\) from \(I_{c-1,2k+2}\) by the _selection by minimum error_ Rule using \(ZoneComplifym-1\), \(c\)] as a base sensor data and its absolute position, this is, \(p=\ \ \ \ \ \ \ \ \ \



position is very important in that it is recorded into _ZoneCompHistory_ with the selected sensor data value and it can be used as the absolute position of a base sensor data in the future compressions. Finally, the algorithm executes the _zoneCValue th compression in the line 19 applying the selection by pattern_ rule, selects only one sensor data, and returns its value, absolute position and I-bit position. ### Last Zone Compression Algorithm

The _LastZoneCompression_ is a procedure that reads ground sensor data and compresses them using _AuxDataQueueCompression_ procedure so to increase one more the number of compressions of the last zone. The _LastZoneCompression_ procedure is called by the _SMEP_ main procedure introduced in Section 7.4 when the last zone becomes full and the number of compressions in the last zone is less than the number in its previous neighbor zone. The last zone compression algorithm is shown in Algorithm 2.
# CKGAT: Collaborative Knowledge-Aware Graph Attention Network for Top-N Recommendation
## 4 Proposed CKGAT Method
### User-Item Interaction Probability Prediction Layer  
This layer in CKGAT uses the user representation set \(\mathcal{T}_{u}\) and the item representation set \(\mathcal{T}_{v}\), which are uniformly denoted as \(\mathcal{T}_{o}\), to respectively generate the user embedding and the item embedding, thereby calculating the predicted probability that the user will interact with the item. Inspired by the attention mechanism [36; 37; 38], for the input user/item representation set \(\mathcal{T}_{o}\), the prediction layer uses the attention aggregator to generate the user/item embedding \(\mathbf{e}_{o}\). Specifically, the attention aggregator first learns an attention score \(\alpha_{o}^{(i)}\) for each embedding \(\mathbf{e}_{o}^{(i)}\in\mathcal{T}_{o}\). Then, it normalizes the score to obtain the weight coefficient \(\tilde{\alpha}_{o}^{(i)}\) of the embedding \(\mathbf{e}_{o}^{(i)}\). Finally, the attention aggregator performs weighted aggregation on all \(\mathbf{e}_{o}^{(i)}\) to obtain the user/item embedding \(\mathbf{e}_{o}\). The above process is defined in Equation (13).  
\[\begin{split}\alpha_{o}^{(i)}&=\mathbf{w}_{2}^{ \top}\tanh\left(\mathbf{W}_{2}\mathbf{e}_{o}^{(i)}\right)\\ \tilde{\alpha}_{o}^{(i)}&=\frac{\exp\left(\alpha_{o }^{(i)}\right)}{\sum_{i^{\prime}=1}^{|\mathcal{T}_{e}|}\exp\left(\alpha_{o}^{ (i^{\prime})}\right)}\\ \mathbf{e}_{o}=&\sigma\left(\mathbf{W}_{3}\sum_{ \mathbf{e}_{o}^{(i)}\in\mathcal{T}_{o}}\tilde{\alpha}_{o}^{(i)}\mathbf{e}_{o} ^{(i)}+\mathbf{b}\right)\end{split} \tag{13}\]  
where \(\tanh\) is an activation function which endows the prediction model with nonlinearity. Parameters \(\mathbf{w}_{2}\in\mathbb{R}^{d}\) and \(\mathbf{W}_{2},\mathbf{W}_{3}\in\mathbb{R}^{d\times d}\) are the weight vector and the weight matrices, respectively. \(\mathbf{b}\in\mathbb{R}^{d}\) is the bias. These parameters are determined through parameter learning.  
After obtaining the user embedding \(\mathbf{e}_{u}\) and the item embedding \(\mathbf{e}_{v}\), CKGAT calculates the predicted probability \(\hat{y}_{uv}\) that the user \(u\) will interact with the item \(v\) by performing the inner product operation on the two embeddings, as defined in Equation (14).  
\[\hat{y}_{uv}=\mathbf{e}_{u}^{\top}\mathbf{e}_{v} \tag{14}\]
## 3 Methodology
### Prediction Model Development  
In this study, a heat pump system performance prediction model was developed using the data collected in Section 2.1. As described in Section 2.1, the operation data of the heat pump system collected from the laboratory was used to develop a heat pump system performance prediction model using machine learning. Problems such as overfitting, sampling noise, and sampling bias may occur if the scale of the characteristics of the data is significantly different when developing a machine-learning model. To prevent problems before the data are learned, preprocessing was performed so that the data are reflected in the same scale. A predictive model was developed using normalized data that transforms the data interval into a range of 0 to 1 through Equation (8).  
\[X=(x-x_{min})(x_{max}-x_{min}) \tag{8}\]  
where \(x\) is the measure value, \(x_{min}\) is the minimum value, \(x_{max}\) is maximum value.  
A total of 5124 pieces of data corresponding to 70% of the collected heating and cooling operation data were used as training data for predictive model development, and 30% of the data (2196 pieces) not used as training data are testing data to verify the performance of the developed model. As the input variables, as in Section 2.2, the inlet/outlet temperature of the heat source side and the inlet/outlet temperature of the load side were used. A predictive model was developed using R studio (ver. 1.2.1335), which is used as a research and industrial application for statistics, machine learning, and data mining. The Figure 4 is an example of R studio screen used for predictive model development.  
Figure 4: R studio.  
The parameter configuration of ANN prediction model is shown in Table 4. Each parameter was set inside the R studio code, and the number of layers and neurons of the hidden layer was selected based on previous studies [9]. The optimization algorithm used Adam (Adaptive Moment Estimation), which is effective by reducing computational memory even when the number of data, layers, and neurons increases. The activation function used the sigmoid.  
The parameter configuration of SVM prediction model is shown in Table 5. The kernel function is Gaussian and kernel scale is 1.2.  
The parameter configuration of RF model is shown in Table 6. The random forest model used 400 decision trees, and the number of randomly selected input values was 1/3 of the total input values. The parameter configuration of KNN model is shown in Table 7. The KNN model uses the Euclidean distance equation, and the number of K-nearest neighbors is set to 3.
# Performance Analysis of Amplitude Comparison Monopulse Direction-of-Arrival Estimation
## 6 Conclusions  
In this paper, monopulse radar was used to detect the angular error through a single pulse of the received signal. The tracking performance of the amplitude-comparison monopulse radar was analysed assuming that the signals received from the four antennas have different amplitudes from the same phase. Also, this paper deals with an analytic expression of the MSEs of the target angle estimate obtained from the approximation. The derivation is based on the assumption that additive noises of the measured angle components are Gaussian-distributed.  
The usefulness of the derived expression is that the MSEs of the monopulse algorithm angle estimate using the different gain of received signal at array antenna can be available from the derived expression without actually performing the computationally intensive Monte Carlo simulation,  
Figure 5: Execution time of MSEs of target estimate for ACM radar.  
which is illustrated in the numerical results. By using the derived expression, we can get a quantitative measure of how accurate the estimates are without actually performing the computationally intensive Monte Carlo simulation.  
To quantify the computational reduction in getting the MSE analytically in comparison with the Monte Carlo simulation-based MSE, execution time with respect to the number of repetitions in the Monte Carlo simulation is illustrated in Figure 5, where the reduction in computational complexity associated with the analytically derived MSE in comparison with the Monte Carlo simulation-based MSE is clearly shown.  
**Author Contributions**: [PERSON] made a Matlab simulation and wrote the initial draft. [PERSON] and [PERSON] derived the mathematical formulation of the proposed scheme. In addition, [PERSON] checked the numerical results and corrected the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding**: The authors gratefully acknowledge the support from Electronic Warfare Research Center (EWRC) at Gwangju Institute of Science and Technology (GIST), originally funded by Defense Acquisition Program Administration (DAPA) and Agency for Defense Development (ADD).  
**Conflicts of Interest**: The authors declare no conflicts of interest.
# Model-Based Virtual Components in Event-Based Controls: Linking the FMI and IEC 61499
## 3 Generic Virtual Components
### System Model  
A generic virtual component cannot rely on the system architecture of a particular plant or control system. On the contrary, a suitable abstract architecture needs to be found for further analysis. In order to feature a generalized discussion in the context of IEC 61499, it is assumed that:  
* the virtual component can be accessed via a dedicated FB,
* communication is restricted to the occurrence of events,
* each event \(e\) is triggered at a particular instant of time \(t(e)\)[27], and that
* \(e\) holds a set of variables that are either sent to the virtual component or to the automation system.  
Although a control algorithm may choose to alter a variable between two consecutive events, values are not communicated across FB boundaries. From an outside perspective, a continuous virtual component holds exposed values constant unless they are communicated via an event.  
Still, the FMI-based representation of the virtual component needs to be mapped to a set of properly timed events. An abstract system model of a generic virtual component is presented in Figure 1, which defines the following major components:  
* Representational mapping: A generic virtual component has to map the hybrid representation defined by the FMI to discrete events that can be exchanged within the automation system. Such a mapping comprises the numerical computation of the state of the FMU based on its ODEs and the algorithm mapping possibly continuous in- and out-puts to discrete events \(e\). The algorithm will be discussed in more detail in the following sections.
* Time synchronization: Since IEC 61499 events do not include a notion of time, the current progress of simulation time has to be synchronized, e.g., to the clock of a controller. Synchronization may also influence the range of feasible approaches for representational mapping since such a projection may have to be conducted in real time.
* Automation system interface: The actual integration of the virtual component into the automation system is implemented in the automation system interface, e.g., via a network connection. It is assumed that the automation system interface simply relays discrete events \(e\) without changing their abstract semantics.  
Figure 1: Abstract system model.
## 2 Algorithms And Methods
### Realization and Representation of Objectives  
Vertical handover systems have experienced numerous improvements, and various new advances have been presented in the most recent couple of years. There are, however, still a few gaps that require further investigation; this section summarizes them.  
1. When vertical handover happens, providing the required QoS over diverse remote systems is a significant issue, and elevated levels of versatility cause further difficulties. To ensure the QoS, VHO methods should cautiously consider client portability and system conditions to pick the best applicant to organize and carry out a quick handover.
2. Guaranteeing the QoS is not sufficient to provide the most ideal experience for clients. Strength of experience (SoE) is an idea that has been gaining significance. It is identifiedwith clients' fulfillment. Providing great system administration does not necessarily provide complete fulfillment to the end clients. Consequently, there is also a need to consider client inclinations and varying gear when planning VHO procedures and strategies.
3. In heterogeneous conditions, systems should be oriented with the end goal that the clients are, in every case, connected as well as possible. The issues identified with a system's administration, including charging for and evaluating the utilization of the system and other such issues between administrators, must be solved in a way that ensures the quality of VHO, the strength of experience (SoE), and the strength of service (SoS). The between-system administration of these dissimilar remote systems has become a difficult and significant area of research. Diverse access systems, including 3 GPP (e.g., EDGE, HSPA, UMTS, LTE) and non-3 GPP (e.g., WiMAX, Wi-Fi) gauges, should be associated in an ideal way to provide clients with a decent QoS.
4. A standardized VHO assessment approach is absent. In this way, a typical approach is required so that analysts, designers, and clients can effectively look at and assess the various VHO methods employed. There is a need to establish standards or rules for good practices for VHO assessment. We have framed the objectives of our research work to overcome these pitfalls.  
The objectives of the work are:  
1. Optimization of vertical handover decision by minimizing the handover delay and maximizing the throughput using the proposed algorithm;
2. Optimization of vertical handover decision by minimizing the handover failure probability using the proposed algorithm based on the quality of service constraints;
3. Optimizing the vertical handover decision using the proposed handover algorithm.
## 3 Simulation Results
### Prediction Based on Static Data
The regression with NN (i.e., PI = 33.89) proved the least efficient method for approximating training data. On the other hand, in the testing phase (i.e., prediction from observations that were not input in the training phase), the MARS regression method with the piecewise-cubic model achieved the best performance (i.e., PI = 17.76). The MARS model was able to approximate target data most precisely. The NN was the least effective in the testing phase. The carbon content prediction with the NN has proven to be the least effective in the testing phase (i.e., PI = 34.67). In this paper, only one configuration of NN, i.e., with a single hidden layer, was compared, but by further optimization of the NN, its performance can be increased. Other reasons for  
Trained machine models exist in the PC in the form of many learned coefficients, parameters, and weights in various program structures. The problem with machine learning methods can be the portability of the learned models to different industrial automation devices. However, the exception is the MARS model, which is given by an exact mathematical equation that can be programmed on a PC or programmable logic controller (PLC). The regression equation of the MARS model is composed of basis functions (BFs), the form of which depends on the model type (i.e., piecewise-cubic or piecewise linear MARS). Because in the testing phase, the piecewise-cubic MARS model was most effective in predicting temperature on unknown data, its equation is presented in this paper (see Equation (29)). The list of corresponding basis functions is shown in Table 8. Variables \(x_{6}\), \(x_{9}\), \(x_{11}\), \(x_{14}\), and \(x_{17}\) have the lowest relative importance and were not used in the piecewise-cubic MARS model of melt temperature. The optimal number of maximal BFs in the final model was estimated by the generalized cross-validation criterion (GCV) and 10-fold cross-validation. In all cross-validation iterations, a new MARS model was created and reduced using the GCV in the in-fold (i.e., training) data. In addition, there was also calculated MSE criterion on out-of-fold (i.e., testing) data in the reducing phase of each MARS model.
# Investigations into Picture Defogging Techniques Based on Dark Channel Prior and Retinex Theory
## 3 The Proposed Methods
### An Evaluation of the Results of Image Defogging  
At present, the commonly used quality evaluation standards for restoration images are mainly divided into subjective evaluation and objective evaluation conducted in two ways. Subjective evaluation involves considering the subjective human visual experience of an image; objective evaluation takes into account the relevant evaluation parameter standards for image evaluation. Usually a combination of subjective and objective evaluation is used.