esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Entropy-Based Feature Engineering Framework
#### 2.5.2 Vital Sign Entropy Feature Generation  
Consider a discrete vital sign variable \(V\) for vital sign feature \(k\), which occurs with probability \(P(V_{k,1}),...,P(V_{k,h})\), where \(h\) is the total number of possible states. According to the Shannon entropy, defined in [7], the entropy \(H\) of the discrete variable \(V\) at the \(q^{\text{th}}\) time(\(q\in[1,150]\)) can be written as follows:  
\[H_{q}(V)=-\sum_{q=0}^{h}P(V_{q})logP(V_{q}), \tag{1}\]  
where \(fw\) is the feature window length. Then, \(h\) is:  
\[h=\frac{|fw|}{4}. \tag{2}\]  
We propose our entropy-based vital sign feature, \(H\), as follows:  
\[H=\frac{1}{N}\sum_{q=0}^{N}H_{q}(V),\quad\text{where}\quad N=150. \tag{3}\]  
In our setting, we set \(h\) equal to the feature window divided by 4, such that \(h=[6,12]\). As our feature window started at 24 h, \(h\) started at 6 and was incremented to 12 as the feature window became longer. Let \(N\) be the number of computation times. We obtained the numbers of 4 and \(N\) from multiple trials. Our proposed entropy feature, from Equation (3), takes \(h\) data points from the vital signs \(V\) to calculate \(q\) times of entropy, \(H_{q}\), which are then accumulated and normalized \(N\) times to obtain our final entropy, \(H\). The benefit of this is  
Figure 3: Overall framework.  
that we obtain an entropy that objectively represents the overall data. The entropy can be regarded as another measure of the variation given feature windows.  
The calculation of entropy on all vital signs \(V\) provides a quantified information evaluation of the average uncertainty regarding the outcomes of the vital sign features, as well as the vital sign feature qualities. It could also be interpreted as an evaluation of the information carried by the vital signs \(V\).  
For the vital signs stated in Section 2.4, the Shannon entropy was calculated. We also calculated the mean and variance for each vital sign \(V\). Table 2 summarizes the feature generation for vital signs \(V\). Let \(V_{general}\) denote the mean and variance of vital signs \(V\) and \(V_{entropy}\) denote the entropy of vital signs of \(V\); then, \(V_{merge}\) is the combination of both \(V_{general}\) and \(V_{entropy}\).
## 2 Nonlinear Dynamic Modeling of Spur Gear Systems
### Influence of Damping
#### 3.3.1 Analysis of Meshing Damping  
Let the load torque \(T_{c}\) = 40 Nm, and the supporting stiffness be \(4\times 10^{6}\) N/mm. Figure 27 shows the bifurcation diagram of the system with respect to the meshing damping ratio, \(\xi\), in the range of \(\xi\in[0,\ 0.2]\).  
As shown in Figure 27, with an increase in meshing damping, the vibration displacement of the system significantly decreases, and the motion state of the system gradually evolves from an unstable motion to a stable periodic motion. When \(\xi<0.015\), the system is in a chaotic motion state, and its maximum vibration displacement reaches 1.5 mm. As shown in Figure 28c, the dimensionless vibration displacement is \((-5,\ 9)\). Since the backlash is \((-1,\ 1)\), it can be inferred that the slipping, back-to-back meshing, and impact phenomena have occurred. With an increase in meshing damping, e.g., meshing damping falls into (0.016, 0.02), the system is still in a chaotic state, but a jump phenomenon in vibration displacement arises. When \(\xi>0.03\), the system changes from the chaotic motion state to the quasi-multiperiodic motion state. When \(\xi=0.053\), along with the abrupt change in the vibration displacement, the system falls into the quasiperiod-doubling motion and then remains like this within a wide range. Afterward, the system's dynamic response gradually becomes stable with the increase in meshing damping and finally converges into a stable periodic motion, as shown in Figures 29 and 30. As shown in Figures 28c, 29c and 30c, the displacement amplitude along the LOA shows a decreasing trend as meshing damping increases.  
Figure 27: Bifurcation diagram with respect to \(\xi\).  
Figure 28: \(\xi\)= 0.015; (**a**) phase diagram; (**b**) Poincaré map; (**c**) time-domain diagram.  
Therefore, an increase in the meshing damping ratio, \(\xi\), will make the system gradually tend toward a stable response and reduce the maximum vibration displacement of the system.
## 4 Results and Discussion  
Thermodynamic models for two proposed systems were formed in the Engineering Equation Solver, and all of the above mentioned equations were solved in this software using R1234 ze(E), R227 ea, R134a, and R600a working fluids [23]. Effects of different parameters on \(\mathrm{COP_{sys}}\), \(\mathrm{\eta_{ex,sys}}\), and \(\mathrm{\dot{m}_{total}}\) were graphed. The most efficient working fluid was detected looking at these graphs. After determination of the most efficient working fluid, second law efficiency, exergy destruction rate, exergy destruction ratio, and degree of thermodynamic perfection were detected for components in both of proposed systems using this fluid.  
Mathematical model of the system was validated comparing its results for R1234 ze(E) with the results of [PERSON] [11]. \(\mathrm{\dot{Q}_{evap}}\) of 136.3 kW, \(\mathrm{COP_{VCR}}\) of 4.84, \(\mathrm{\dot{W}_{turb}}\) of 28.17 kW, and \(\mathrm{\eta_{ORC}}\) of 11.26% was found by [PERSON] [11] while our model suggests \(\mathrm{\dot{Q}_{evap}}\) of 135.7 kW, \(\mathrm{COP_{VCR}}\) of 4.868, \(\mathrm{\dot{W}_{turb}}\) of 27.88 kW, and \(\mathrm{\eta_{ORC}}\) of 11.22%.
## 6 Results and Discussion
### GAN Hyperparameter Optimization
Clearly, the prediction performance of the optimized GAN-based models outperformed the default models on at least 60% of the defect datasets. Based on our empirical results, we can observe that GAN-based methods are sensitive to hyperparameter optimization. Thus, in the remaining experiments, we utilized the optimized models. We can summarize the answer of the first research question _\"RQ1. Will hyperparameters optimization of GAN-based methods yield enhanced defect prediction performance over default hyperparameters? \"_, as follows:  
**RQ1** answer. Empirical evidence demonstrated that hyperparameters optimization of GAN-based methods has a vital role in software defect prediction performance. The performance of the optimized models was superior over the default models on most of the defect datasets. The magnitude of the impact of hyperparameter optimization differs based on the GAN methods and defect datasets. Therefore, we recommend the optimization of GAN hyperparameters in imbalanced software defect prediction.
# Instability of High Liquid Limit Soil Slope for the Expressway Induced by Rainfall
## 3 Numerical Simulations
### Analysis of Numerical Simulation Results
#### 3.3.5 Soil Displacement Characteristics  
To study the effects of rainfall intensity and rainfall time on cumulative displacement, the dimensionless displacement coefficient K is proposed.  
\[\text{K}=X_{t,v}/X_{24,0.01}\]  
where \(X_{t,v}\) is the cumulative displacement after \(t\) time when the rainfall intensity is \(v\); \(X_{24,0.01}\) is the cumulative displacement of the shallow layer with rainfall time of 24 h and rainfall intensity of 0.01 m/h.  
For the shallow layer, the linear relationship between dimensionless K, rainfall time \(t\), and rainfall intensity \(v\) is established for each monitoring hole. For the area without a detector, the cumulative displacement of each depth can be inferred by numerical simulation, and then, the relationship can be established by this method.  
\[\begin{array}{l}\text{K}=a\cdot T_{k}\cdot V_{k}\\ T_{k}=\frac{t}{24}\\ V_{k}=\frac{v}{0.01}\\ X_{t,v}=\text{K}\cdot X_{24,0.01}\end{array}\]  
With the help of MATLAB, the parameter \(a_{i}\) is calculated for the existing calculation results, and the average value of \(a_{i}\) is 1.1957. To facilitate the calculation, the parameter \(a\) of the slope takes 1.2. Thus, we can figure out the following equations.  
\[X_{t,v}=1.2\cdot\frac{t}{24}\cdot\frac{v}{0.01}\cdot X_{24,0.01}\]  
Figure 13: Plastic strain area variation nephogram of Group B: (**a**) 0 h; (**b**) 96 h; and (**c**) strength reduced to damage.  
Let \(t\cdot v=Q\), we can get the final performance of the equation.  
\[X_{t,v}=5\cdot Q\cdot X_{24,0.01}\]  
Figure 14 shows the in-site monitoring and numerical simulation of the CX-5 and CX-6 holes. The cumulative displacement of A direction in groups B and C varies with depth in each rainfall period. Compared with the in-site monitoring data, the in-site monitoring data are close to the data at the 48 h of Group B or the 24 h in Group C, indicating the numerical model has high accuracy. Although the sensor is damaged due to the burying of the CX-4 hole, its data can be speculated by taking the average of the data at 48 h for Group B or at 24 h for Group C, as shown in Figure 15. Compared with Figure 14a,b, the increase of rainfall intensity directly changes the cumulative displacement of high liquid limit soil slope at the same depth. The time of rainfall increases the cumulative displacement, but the influence is not as great as that of rainfall intensity.  
Figure 14: Cumulative displacement for Group B and Group C at A direction of CX-5 or CX-6 hole (mm): (**a**) CX-5 for group B; (**b**) CX-5 for group C; (**c**) CX-6 for group B; (**d**) CX-6 for group C.
# Watermarking Tiny MLCommons Image Applications Without Extra Deployability Costs
## 6 Approaches Being Modeled
### Watermarking by Means of Regularization
#### 6.1.3 Regularization and Updating of Weights  
The watermark is applied through the watermark regularizer class. It is defined as the \(R_{term}\) added to the original cost function to train the NN, as explained in Equation (1):  
\[New\,cost\,function=Original\,cost\,function+R_{term}. \tag{1}\]  
The additional regularized term is defined as the binary cross entropy between vector \(b\) and the sigmoid activation of the scalar product obtained from the watermark matrix and flattened weights, as in the following equation:  
\[R_{term}=k*\sum(-(b_{j}*\log(y_{j})+(1-b_{j})*\log(1-y_{j}))), \tag{2}\]  
where \(b\) is the watermark key, as explained in Section 6.1.1, \(y_{j}\) is the term defined in Equation (4), and \(k\) is the scale factor experimentally chosen. The regularizer term calculation was done through a function called automatic in the NN training process. Since the watermark can be applied to a single layer of the NN, it can be introduced as a custom regularizer in that layer. In this work, a convolutional layer was selected as the one to watermark. Specifically, during training, the weights \(w\) of the convolutional layer chosen for watermarking are multiplied by the watermark matrix \(M\), as shown in Equation (3):  
\[w^{\prime}=\sum M_{ji}\,*\,w_{i}. \tag{3}\]  
As explained in Section 6.1.2, the number of rows \(i\) of the watermark matrix, \(M_{ji}\), corresponds to the product of the first three dimensions of the weight tensor in the layer where the watermark is to be applied, and the number of columns \(i\) corresponds to the dimension of the key vector, as described in Section 6.1.1. The meaning behind this choice is to make sure that in the extraction process, the matrix can be multiplied by the weights of the WMd layer \(w_{i}\); also, the extracted vector then has the same size as the vector \(b\). The values different from zero in the watermark matrix influence the weights. The result of  
Figure 1: Process to set the watermark matrix using one of the three options.  
Equation (3) is then passed through a sigmoid activation, which normalizes all the values between 0 and 1, as shown in Equation (4):  
\[y_{j}=\sigma(w^{\prime})=\frac{1}{1+exp(-w^{\prime})}. \tag{4}\]  
Equation (4) and vector \(b\) are passed through a binary cross entropy, and then a scale factor \(k\) is applied to obtain the regularized additional loss term as per Equation (2).  
All these steps taken for the additional loss term belong to the so-called injection process. The whole process is shown in Figure 2.  
During training, a function to obtain the regularized term in Equation (2) is identified for every batch size in each epoch. The next step involves the extraction of the watermark, as discussed in Section 6.1.4.
# Trade-Off Analysis for Array Configurations of Chipless RFID Sensor Tag Designs
## 5 Simulation Study of Array Configurations  
In the CST Microwave Studio simulation configuration, the chipless RFID reader comprises a plane wave excitation and an RCS probe. These components are located at a distance of 30 cm from the tag. The plane-wave excitation transmits an interrogating signal that is linearly polarised and orientated in the z direction. Precisely, the electric field plane (E-plane) of the wave aligns with the y-axis, whereas the magnetic field plane (H-plane) aligns with the x-axis. The simulation covers a frequency span from 3.5 to 6 GHz and adopts an open boundary configuration in all spatial dimensions.
# Performance Analysis of Internet of Things Interactions via Simulation-Based Queueing Models
###### Abstract
However, such efforts are protocol-specific; thus, the research community analyzed the performance of well-known interaction paradigms [11; 12] that may abstract different IoT protocols. The publish/subscribe paradigm was analyzed using formal models [13] and evaluated using queueing networks [14] or queueing Petri nets [15]. Queueing networks can be considered as networks of connected service centers that provide simulation or analytical solutions for various performance measures (e.g., response time). Combining separate service centers in order to form a queueing network enables us to model an IoT interaction. Some of the queueing models presented in this paper were investigated in the context of previous works. In particular, in [16; 17; 18; 19], we identified a framework where various middleware protocol nodes (e.g., clients, servers, and brokers) are analyzed as queues while exchanged messages are represented as served jobs. To model QoS settings such as the intermittent connectivity [20] of mobile things, a separated queueing model was investigated with the aid of an ON/OFF queueing model [16; 17]. The applicability of our models was demonstrated in [16; 17; 18; 21; 22; 23] for the performance of publish/subscribe and data-streaming systems, respectively. In [19], we applied our approach to analyze the performance of IoT devices with heterogeneous QoS settings. This paper expands upon our previous work [24] that provides a summary of queueing models that represent the aforementioned QoS constraints of IoT applications. In this paper, we include two queueing systems that model the processing or transmission of diverse IoT messages (e.g., video data vs. temperature data) and the prioritization of urgent data in critical IoT applications (e.g., structural fire scenarios). The key contributions of this paper are:  
1. Description of a generalized ON/OFF model that includes message losses and message join probabilities. 2. Introduction of two queueing systems that model diverse message transmission, processing, and prioritization on the basis of delivery urgency requirements. 3. Description of additional features for queueing models that represent message availability and resource-constrained devices. 4. Comparison of different queueing models in terms of delivery success rate and response time through simulation-based experiments. The rest of this paper is organized as follows: related work is discussed in Section 2. In Section 3, we describe the queueing models that represent various QoS settings of IoT interactions and applications. In Section 4, we compare the performance of queueing models when message availabilities, message join probabilities, and finite buffer capacities are considered. In addition, we show how the presented queueing models can be leveraged to properly configure and tune an IoT system. We conclude this paper and provide possible future extensions in Section 5.
# Extraction of Visible Boundaries for Cadastral Mapping Based on UAV Imagery
## 1 Introduction
### Objective of the Study  
The study is based on the assumption that many cadastral boundaries are visible [2]. The study's main objective is to outline the potential of the ENVI FX module as well as its limitations for the automatic delineation of visible object boundaries for UAV-based cadastral mapping. It investigates which processing steps (scale level and merge level) using the ENVI FX module need to be applied for UAV-based cadastral mapping. The automatic delineated visible boundaries on UAV images, similarly as manual delineations, can be used for both the creation and updating/revision of cadastral maps.  
Overall, the study addresses the whole of the UAV-based cadastral mapping workflow steps, which include image pre-processing, automatic detection and extraction of visible object boundaries on the UAV image, and post-processing of extracted boundaries to more closely approximate cadastral boundaries.
## 2 The Duality of Linear Quadratic Regulation (LQR) and Estimation (LQE)
### Linear Quadratic Regulator (LQR)  
Consider a dynamic system with the following form  
\[\left\{\begin{array}{l}\mathbf{x}_{k+1}=\mathbf{F}_{k+1,k}\mathbf{x}_{k}+\mathbf{B}_{k}\mathbf{u}_{k} \\ \mathbf{u}_{k}=-\mathbf{K}_{k}\mathbf{x}_{k}\end{array}\right. \tag{3}\]  
where \(\mathbf{x}_{k}\) is an \(n\)-state vector of the system, the control law \(\mathbf{u}_{k}\) is an \(r\)-vector, and \(\mathbf{u}_{k}=-\mathbf{K}_{k}\mathbf{x}_{k}\), \(\mathbf{F}_{k+1,k}\) is \(n\times n\) state transition matrix, \(\mathbf{B}_{k}\) is \(n\times r\) control matrix, \(\mathbf{K}_{k}\) is \(r\times n\) optimal control gain matrix.  
In the LQR problem, the optimal solutions \(\mathbf{P}_{k}\), \(\mathbf{K}_{k}\) are determined by the recursion relations [19; 20]:  
\[\left\{\begin{array}{l}\mathbf{x}_{k+1}=\mathbf{F}_{k+1,k}\mathbf{x}_{k}+\mathbf{B}_{k}\mathbf{u }_{k}\\ \mathbf{P}_{k}=\left(\mathbf{F}_{k+1,k}-\mathbf{B}_{k}\mathbf{K}_{k}\right)^{T}\mathbf{P}_{k+1} \left(\mathbf{F}_{k+1,k}-\mathbf{B}_{k}\mathbf{K}_{k}\right)+\mathbf{Q}+\mathbf{K}_{k}^{T}\mathbf{R} \mathbf{K}_{k}\\ \mathbf{K}_{k}=\left(\mathbf{B}_{k}^{T}\mathbf{P}_{k+1}\mathbf{B}_{k}+\mathbf{R}_{k}\right)^{-1} \mathbf{B}_{k}^{T}\mathbf{P}_{k+1}\mathbf{F}_{k+1,k}\end{array}\right. \tag{4}\]  
where \(\mathbf{P}_{k}\) is a matrix of quadratic form of the performance index under optimal regulation, \(\mathbf{Q}\) is state-cost weighting matrix, \(\mathbf{R}\) is control-cost weighting matrix, and \(\mathbf{P}_{k}\) is the \(r\times n\) optimal control gain matrix and is determined by minimizing the cost function \(J_{k}=\mathbf{x}_{k}^{T}\mathbf{P}_{k}\mathbf{x}_{k}=J_{k+1}+\mathbf{x}_{k}^{T}\mathbf{Qx}_{k}+\bm {u}_{k}^{T}\mathbf{Ru}_{k}\). \(\mathbf{P}_{k}\), \(\mathbf{Q}\), and \(\mathbf{R}\) are positive-definite symmetric matrices. It should be noted that the physical meanings of \(\mathbf{P}_{k}\), \(\mathbf{Q}\), and \(\mathbf{R}\) in the LQR problem and LQE problem are different. \(\mathbf{x}_{k}^{T}\mathbf{Qx}_{k}\) denotes the distance between the actual state and the desired state in the hyperplane. \(\mathbf{u}_{k}^{T}\mathbf{Ru}_{k}\) denotes the cost of the energy. The selection of \(\mathbf{Q}\) and \(\mathbf{R}\) will influence the performance of transient response process.  
The optimal values of \(\mathbf{K}_{k}\) and \(\mathbf{P}_{k}\) can be calculated iteratively after setting the initial conditions \(\mathbf{P}_{N}\), \(\mathbf{Q}\), \(\mathbf{R}\), where \(N\) is the terminal time.
# Amharic Speech Search Using Text Word Query Based on Automatic Sentence-like Segmentation
## 5 Experiments  
### Experimental Setup  
We used the following experimental setup to test the proposed work. The whole ASR experiment was conducted on Ubuntu 18.04 release and Intel(r) Core (tm)i7-6500U CPU @2.50 GHz 2.60 GHz RAM-8.00 GB computer (LENOVO, Hong Kong, China). This was developed using the popular CMU Sphinx toolkit that includes various libraries for creating speech applications [37]. To train our acoustic model, we were required to prepare a file dictionary, phone, filler, LM, transcription, and the list of the wave files. The acoustic model was developed using the triphones HMM structure. Since we trained continuous models for a large vocabulary, we used a Gaussian number of 8. The acoustic front-end was made using mel-cepstrum MFCC features with noise tracking and spectral subtraction for noise reduction used by the recognizer. The acoustic model that was developed for the comparison of automatically and manually segmented test speech (Bible speech corpus) was done using a grapheme pronunciation dictionary, which was created by writing a simple python script. The LM was developed using the SRI language modeling toolkit (SRILM) as a tool. Using this tool, 3-gram language modeling was developed. Manually segmented test speeches were prepared using the audio processing software Audacity [22] whereas automatic segmentations were made using python's pydub package. This package is a speech segmentation package based on silence detection on the energy [38].  
On the other hand, the text-based STD was done on Windows 10 pro. We used Netbeans 8.0.2 for java development and also implemented python in an anaconda environment. One of the components of the STD system was used to segment the given unsegmented audio file into a list of automatically segmented audio files using the pydub package. The other component that was used in text-based STD was the LVCSR's acoustic model, which was developed using the CMU Sphinx toolkit. Two acoustic models were developed; the first using the broadcast domain and the second by combining the two speech corpora (the news and the Bible).  
Figure 2: The proposed ASSTWQ architecture.  
The LM for both acoustic models were developed using SRILM, but the LM that is used with the second acoustic model was developed by combining two LMs (the news and the Bible) using different lambda values [0.1-1]. The time frame of each segment is located using the python's pydub library. By using the acoustic model, LM, phonetic dictionary, and Sphinx4 API, automatically segmented speech was decoded. The alignment of the time frame and decoded text was made using java and python in combination and stored in the Windows file system. Therefore, the file was easily searched using a simple Java string mapping function that compares query terms and the aligned text and retrieves the time with the transcribed text. The search results were displayed using a graphical user interface (GUI) that used Java's JFrame, which is a top-level container that provides a window on the screen.
# Comparative Analysis of Current Control Techniques to Support Virtual Inertia Applications +
## 4 Simulation Results and Comparative Analysis
### Supplementary ADP Controller Simulation Results  
The output current of the grid-connected inverter when a supplementary ADP control approach was used in the current control loop is shown in Figure 12a. The main objective of the controller is to improve the transient performance of a traditional PI controller. It is worth noting here that the neural network does not completely replace the original PI controller, but only supplements the generated control actions. Figure 12b shows the change in the active power of the grid-connected inverter corresponding to the change in the output current. The overshoot in the active power in this case was reduced to about 26% while improving the response time. This reduction as stated before can bring economic savings. It is also worth noting that the ADP control actions did not increase the switching frequency, as in the case of the hysteresis and MPC controllers.  
Figure 11: Simulation results with MPC. (**a**) Three-phase output current of inverter. (**b**) Active and reactive power output.  
Figure 12: Simulation results with the ADP controller. (**a**) Three-phase output current of inverter. (**b**) Active and reactive power output.