esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Routing in Integrated Networks
To mitigate the obstacles presented by the fast speed of LEO satellites, they present an agile approach to computation offloading and a resource distribution framework called DCOOL based on Lyapunov optimization, which aims to minimize power consumption and latency. As a method, the Lyapunov drift-plus-penalty technique is used to reformulate the stochastic optimization problem into an immediate optimization challenge. UE-GW communication in the C-band and GW-LEO communication in the Ka-band were modeled. Simulations were performed using a single satellite orbiting at an altitude of 550 km with an orbital velocity of 7.66 km/s. The level of background noise was set to \(-174\) dBm/Hz. The simulations demonstrate that the DCOOL algorithm outperforms other algorithms (Selfish users, Offloading-DVFS, and Offloading-fixed frequency), providing lower power consumption and stable workload processing. DCOOL is particularly effective in the low latency regime [126].
## 4 Experiments and Results
### Discussion  
In the field of OD and OC image segmentation, the labeled fundus images are small. Overfitting problems often occur when training network models on small datasets. Thus, we use other datasets (e.g., ImageNet [38]) to pre-train the model weights. To investigate the effect of pre-training, we set up a comparison experiment. Table 3 compares the performance. From these results, we observe that the performance of using pre-trained weights showed an increase in Dice scores for both OD and OC.
## 4 Results and Discussion
### Performance Evaluation
#### 4.2.3 \"Foreman\" Video Sequence

Table 6: Visual quality and rate performance evaluation of different codecs for “Foreman” video sequence at frame rate of 15 Hz. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Video** \\ **Sequence** \\ \end{tabular} } & \multirow{2}{*}{\(\mathbf{Q_{m}}\)} & \multicolumn{3}{c}{**Average Channel Decoding Rate per WZF (bits)**} & \multicolumn{3}{c}{**Average PSNR Gain**} \\  & & & & **(dB)** & \\ \cline{3-8}  & & **DIS** & **DIVCOM** & **PDIVCOM** & **DIVCOM** & **PDIVCOM** \\ \hline \multirow{8}{*}{Foreman} & 1 & 7452 & 7278 & 7407 & \(-\)0.1003 & \(-\)0.0922 \\ \cline{2-8}  & 2 & 8229 & 8010 & 8161 & 0.0627 & \(-\)0.2043 \\ \cline{1-1} \cline{2-8}  & 3 & 12,448 & 12,103 & 12,270 & \(-\)0.1939 & \(-\)0.06 \\ \cline{1-1} \cline{2-8}  & 4 & 23,925 & 23,083 & 23,610 & \(-\)0.0857 & \(-\)0.2419 \\ \cline{1-1} \cline{2-8}  & 5 & 28,260 & 27,326 & 27,666 & \(-\)0.2149 & \(-\)0.02645 \\ \cline{1-1} \cline{2-8}  & 6 & 38,148 & 37,281 & 38,183 & 0.0089 & 0.0434 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Coding efficiency analysis of proposed novel online CNM for “Foreman” video sequence.
# Pseudo-Phoneme Label Loss for Text-Independent Speaker Verification
## 2 Related Work
### Self-Supervised and Multi-Task Learning
#### 2.2.2 Multi-Task Learning  
The multi-task learning [29] features learning multiple related tasks by sharing parameter representations at the same time. If the shared layer is considered as a feature extractor, its optimization goal is to extract feature representations that are valid in each sub-task.  
c-vector introduces pronunciation information into the TI-SV task using a multi-task learning approach. The c-vector structure is divided into two parts: one part is the vocal pattern feature vector extraction model, whose output layer nodes correspond to the speaker labels of the training set and are responsible for the speaker classification task during training. The other part and the shared implicit layer form the ASR model, whose output layer nodes correspond to the pronunciation unit labels and are responsible for the pronunciation recognition task during training.  
The c-vector adopts the method of training the vocal feature vector extraction model and the ASR model, alternately. The network integrates the vocal features and pronunciation information in speech, and the features extracted from the shared implicit layer will contain both vocal and pronunciation information, which makes the frame-level features better reflect the pronounced counterparts after incorporating the pronunciation information.  
There are two categories of training data: speaker data and speech content data. The corresponding two cost functions are the speaker feature vector cost function \(L_{S}\) with the ASR model cost function \(L_{P}\). The final model parameters depend on both \(L_{S}\) and \(L_{P}\) working together.  
By introducing a pre-trained ASR model into the speaker feature vector extraction model, c-vector introduces pronunciation information at the frame level, which improves the performance of the text-independent speaker recognition system.
# NESO-Based Path Following Control for
## 3 Control Strategy Design
### The NESO for Hovercrafts Subject to Unknown Nonlinear Time-Varying Uncertainties
Thus, all observer signals of the NESO are globally asymptotically stable for the bounded total uncertainties by Lemma 3, that is, there exists positive parameters \(b_{u},b_{v},b_{r}b_{du}b_{dv}\) and \(b_{dr}\) such that  
\[\|\overline{\mathbf{v}}\|\leq\|[b_{u},b_{v},b_{r}]^{T}\| \tag{25}\]  
\[\|\overline{\mathbf{d}}\|\leq\|[b_{du},b_{dv},b_{dr}]^{T}\| \tag{26}\]  
then, the Theorem 1 has been proven. \(\Box\)  
**Remark 4**. : _Note that the unknown nonlinear time-varying uncertainties can be approximated by fuzzy logic [34] or neural networks [9]. However, the algorithm will be more complex in practice. Compared with the fuzzy logic or neural network, the NESO has the following advantages: 1) The NESO can approximate the uncertainties without any prior knowledge and the precise mathematical model. 2) The NESO does not need data training like fuzzy logic or a neural network, greatly reducing the computation time, and realizing the real-time control for the underactuated hovercraft. The designed NESO can approximate the total uncertainties induced by the internal unmodeled dynamics and the external nonlinear time-varying ocean disturbances, which will greatly simplify the design and achieve a better performance of the control system._  
The advantages of the proposed NESO for an underactuated hovercraft can be summarized as follows. * First, the uncertainties can be approximated simultaneously via the position-heading of the underactuated hovercraft from the GPS. * Second, the proposed homogenous NESO does not rely on the model parameter information, and the design of the NESO is independent of the control system. * Third, the proposed NESO has better approximate performance--more accuracy and better transience than the LESO. * Moreover, the proposed NESO can compensate the unknown nonlinear time-varying total uncertainties of underactuated hovercrafts in the control system design, which will greatly improve the accuracy and performance of the designed control system.
## 5 Results  
The \"FloatingBlue\" proposal was tested in an agricultural environment to verify its effectiveness. During the tests, the overall functionality of the system, as well as the energy consumption and communication range of the SN and associated beacon, were evaluated. These nodes were designed to be energy-efficient in remote areas.
# Communication Technologies for Smart Grid: A Comprehensive Survey
## 3 Smart Grid Communication
### Interoperability  
With the different equipment interconnected in the SG, interoperability must be ensured for seamless communication [13]. Interoperability ensures that if any device supplied by one manufacturer with a similar device from another manufacturer, the application will continue to operate as before [51]. Interoperability must also be ensured for legacy and evolving communication protocols. Standardization of communication is imperative to achieve a fully connected SG. The IEC 61850 standard offers interoperability of devices across manufacturers, and was initially introduced as a standard for substation communication. In recent years this standard has been utilized for different equipment such as smart meters, virtual power plants, and V2G [52; 53; 51].
# Robust Control with Uncertain Disturbances for Vehicle Drift Motions
###### Abstract  
Professor drivers, including racing drivers, can drive cars to achieve drift motions by taking control of the steering angle in high tire slip ratios, which provides a way to improve the driving safety of autonomous vehicles. The existing studies can be divided into two kinds based on analysis methods, and the theory-based is chosen in this study. Because the recent theory based is most applied for planar models with neglect of the rollover accident risk, the nonlinear vehicle model is established by considering longitudinal, lateral, roll, and yaw motions and rolling safety with the nonlinear tire model UniTire. The drift motion mechanism is analyzed in steady and transient states to obtain drift motion conditions, including the velocity limitation and the relationship between sideslip angle and yaw rate, and vehicle main status parameters including the velocity, side-slip angle and yaw rate in drift conditions. The state-feedback controller is designed based on robust theory and LMI (linear matrix inequation) with uncertain disturbances to realize circle motions in drift conditions. The designed controller in simulations realizes drift circle motions aiming at analyzed status target values by matching the front-wheel steering angle with saturated tire forces, which satisfies the Lyapunov stability with robustness. Robust control in drift conditions solves the problem of how to control vehicles to perform drift motions with uncertain disturbances and improves the driving safety of autonomous vehicles.  
vehicle drift motion; motion mechanism analysis; robust control
###### Abstract  
The application of drones, also known as unmanned aerial vehicles deployed as unmanned aerial base stations (UABSs), has received extensive interest for public safety communications (PSC) to fill the coverage gaps and establish ubiquitous connectivity. In this article, we design a PSC LTE-Advanced air-ground-based HetNet (AG-HetNet) that is a scenario representation of a geographical area during and after a disaster. As part of the AG-HetNet infrastructure, we have UABSs and ground user equipment (GUE) flocking together in clusters at safe places or evacuation shelters. AG-HetNet uses cell range expansion (CRE), intercell interference coordination (ICIC), and 3D beamforming techniques to ensure ubiquitous connectivity. Through system-level simulations and using a brute-force technique, we evaluate the performance of the AG-HetNet in terms of fifth-percentile spectral efficiency (5 pSE) and coverage probability. We compare system-wide 5 pSE and coverage probability when UABSs are deployed on a hexagonal grid and for different clustering distributions of GUEs. The results show that reduced power subframes (FeICIC) defined in 3 GPP Release-11 can provide practical gains in 5 pSE and coverage probability than the 3 GPP Release-10 with almost blank subframes (eICIC).  
cell range expansion; clustered users; eICIC; FeICIC; FirstNet; interference management; Matern cluster process; public safety; Thomas cluster process; UAV +
Footnote †: journal:  
0
Footnote 0: Copyright** © 2023 by the author. Liconsee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)) 4.0/).
## 3 Numerical Simulation of Shockwave Soil Loosening
### Governing Equations and the Preconditioning System
\tag{8}\]  
Subsequently, the Navier-Stokes equations are employed as the governing equations:  
\[\frac{\partial}{\partial t}\int_{\Omega}Wd\Omega+\oint_{\partial\Omega}[ \overset{\rightarrow}{F}-\overset{\rightarrow}{G}]\cdot d\overset{ \rightarrow}{A}=0 \tag{9}\]where \(\Omega\) denotes an arbitrary control volume, \(\overset{\rightarrow}{A}\) represents the surface area, \(W\) is the vector of conservative variables, and \(\overset{\rightarrow}{G}\) corresponds to the viscous flux vector in the standard conservation form. The definitions of \(W\) and \(\overset{\rightarrow}{G}\) are as follows:  
\[W= \begin{bmatrix}\rho\\ \rho v_{x}\\ \rho v_{y}\\ \rho v_{z}\\ \rho E\end{bmatrix} ,\overset{\rightarrow}{G}= \begin{bmatrix}0\\ \tau_{xi}\\ \tau_{yi}\\ \tau_{zi}\\ \tau_{ij}v_{j}+q\end{bmatrix}  \tag{10}\]  
In the preconditioning method, we convert the governing equations defined by the conservative variable W into the form defined by the primitive variable \(Q=(p,\,v_{x},\,v_{y},\,v_{z},\,T)^{T}\) as follows:  
\[\Gamma\dfrac{\partial}{\partial t}\iiint\bigotimes QdV+\iint\bigotimes[ \overset{\rightarrow}{F}-\overset{\rightarrow}{G}]\cdot\overset{ \rightarrow}{dA}=0 \tag{11}\]  
where \(\Gamma\) represents the preconditioning matrix, originally introduced by [PERSON] and [PERSON] [28] and later refined by [PERSON] and [PERSON] [29]. The formal definition of \(\Gamma\) is as follows:  
\[\Gamma= \begin{bmatrix}\Theta+\frac{1}{RT}&0&0&0&-\frac{\rho}{T}\\ v_{x}\big{(}\Theta+\frac{1}{RT}\big{)}&\rho&0&0&-\frac{\rho v_{x}}{T}\\ v_{y}\big{(}\Theta+\frac{1}{RT}\big{)}&0&\rho&0&-\frac{\rho v_{y}}{T}\\ v_{z}\big{(}\Theta+\frac{1}{RT}\big{)}&0&0&\rho&-\frac{\rho v_{z}}{T}\\ H\Big{(}\Theta+\frac{1}{RT}\Big{)}-1&\rho v_{x}&\rho v_{y}&\rho v_{z}&\rho\Big{[} C_{p}-\Big{(}\frac{\mu}{T}\Big{)}\Big{]}\end{bmatrix}  \tag{12}\]  
where \(R\) is the gas constant, and \(\Theta\) and \(H\) are defined as follows:  
\[\Theta=\dfrac{1}{U_{ref}^{2}}-\dfrac{1}{a^{2}} \tag{13}\]  
\[U_{ref}^{2}=min\Big{\{}a^{2},\,\,max\Big{(}|V|^{2},K|V_{\infty}|^{2}\Big{)} \Big{\}} \tag{14}\]  
\[H=C_{p}T+\dfrac{1}{2}\Big{(}{v_{x}}^{2}+{v_{y}}^{2}+{v_{z}}^{2}\Big{)} \tag{15}\]  
where \(\,\,|\,V\,|\,\) is the local velocity, \(\,|\,V_{\infty}\,|\,\) is the fixed reference velocity, \(a\) is the speed of sound, and \(K\) is the constant. The Weiss-Smith preconditioner is formed by adding the vector [1, \(v_{x},\,v_{y},\,v_{z},\,T\)]\({}^{T}\) to the Jacobian matrix \(\partial W/\partial Q\), where \(W\) is a vector of conservative variables. In this study, \(K\) is fixed at 0.25, and the eigenvalues of \(\Gamma^{-1}A\) (\(A=\partial F/\partial W\)) are \(v_{x}\), \({v_{x}}^{\prime}\pm a^{\prime}\), where \(v_{x}\) is the velocity component in the \(x\) direction, and  
\[{v_{x}}^{\prime}\pm a^{\prime}=\dfrac{1}{2}\Bigg{|}\Big{(}1+M_{ref}^{2}\Big{)} v_{x}\pm a\sqrt{\Big{(}1-M_{ref}^{2}\Big{)}^{2}M^{2}+4M_{ref}^{2}}\Bigg{|} \tag{16}\]  
\[M_{ref}^{2}=\dfrac{U_{ref}^{2}}{a^{2}} \tag{17}\]
## 4 Results and Discussions
### Calorific Value and Possible Electricity Generation
#### 4.2.3 Determination of Calorific Value and Potential Electrical Energy  
After determining the chemical composition of the MSW, the calorific value of the waste was determined from [PERSON]'s formula using Equation (1), and the obtained value was 14.90 MJ/kg. Similar calorific value of MSW in RCC have been mentioned in previous studies [9; 10]. A 15.51 MJ/kg calorific value of MSW (dry) in RCC was found in the work done by [PERSON] et al. [9]. In another work, the authors estimated the calorific value of MSW collected in RCC. They determined that the calorific value of MSW was around 2638.9 kcal/kg or 11.1 MJ/kg [10]. Calorific values were found to vary from 10 to 17 MJ/kg because of the variation of the composition and moisture content. Furthermore, from the total heat energy, steam energy and net electrical energy were determined, which are illustrated in Table 8. The total potential power output from MSW was found to be 0.8604 kWh/kg. Total waste generation per day was estimated as 358.19 t. As a moisture content of 48.28% has been observed in MSW, the amount of dry waste per day is 185.26 t. Therefore, the energy potential from MSW in RCC is 6.64 MW, which is close to the results obtained in previous studies [11; 17]. Therefore, from the empirical equations, it is observed that, approximately, 159.40 MWh electrical energy can be produced per day from MSW generated in RCC. [PERSON] et al., in 2014, estimated that about 5.3 MW of electricity can be generated from the generated MSW in RCC, which is slightly less than the result of this research because of a smaller population and, thus, waste generation in 2014 compared to the present conditions [11]. Again, in 2019, authors estimated the energy recovery potential from MSW as 10.2 MW [17]. This estimation is higher than the finding of this research, as the authors did not consider the moisture content in MSW and, therefore, calculated the energy potential considering total generated MSW per day, including moisture.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \begin{tabular}{c} **Heat** \\ **Energy** \\ **(MJ/kg)** \\ \end{tabular} & \begin{tabular}{c} **Steam Energy** \\ **(MJ/kg)** \\ \end{tabular} & \begin{tabular}{c} **Electrical Power, E\({}_{\text{F}}\)** \\ **(kWh/kg)** \\ \end{tabular} & \
\begin{tabular}{c} **Station Service Allowance, S\({}_{\text{A}}\)** \\ **(kWh/kg)** \\ \end{tabular} &
\begin{tabular}{c} **Net electrical Power E\({}_{\text{NP}}\)** \\ **(kWh/kg)** \\ \end{tabular} \\ \hline
14.90 & 10.43 & 0.9153 & 0.0549 & 0.8604 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Determination of net electrical power from MSW in RCC.  
Figure 10: Mean and standard deviation of weight percentages of different elements present in MSW.
## 3 Experimental Data Processing  
The frames obtained from the high-speed video camera were saved as photographic images for further processing. Since the discharge in WEDM is carried out sequentially, as shown in Figure 5, each discharge was found in the corresponding frame. Moreover, the bubble oscillation process after each discharge was observed. It was found that from formation to dissipation, the bubble went through several cycles of oscillation, and each cycle includes expansion and shrinkage. Figure 6 shows one cycle of bubble oscillation.  
In addition, a group of signals obtained from the oscilloscope is shown in Figure 7. With the voltage and current signals, the discharge delay time can be obtained. A combination of the framing signal and the current waveforms was used to locate the corresponding discharges in the images. Finally, the corresponding images and discharge delay time of 713 discharges were obtained for further investigation.  
In this study, MATLAB Version R2023a was utilized to analyze images and determine the discharge location. The first step involved capturing a baseline image without any discharge under constant illumination conditions, which served as the reference for subsequent comparisons. Using MATLAB, the grayscale values of all pixels in the reference image were computed and stored. Moreover, as shown in Figure 7, the framing and current signal recorded by the oscilloscope were synchronized to precisely identify the time frames in which discharge events occurred. This synchronization allowed the MATLAB analysis  
Figure 5: Image of discharge obtained from experimental observation.  
Figure 6: One cycle of bubble oscillation.  
to focus on the relevant frames, enhancing the efficiency and accuracy of the process. Then, for each captured discharge image, the grayscale values of each pixel were calculated and compared to those in the baseline image. By analyzing pixel-by-pixel differences, the pixel showing the most significant deviation in grayscale values was identified as the discharge location.  
This approach is grounded in the assumption that the discharge causes light intensity changes and results in noticeable grayscale variations in the affected region. To validate the reliability of this detection method, the discharge locations detected through grayscale analysis were checked by comparing them with the actual positions observed in the images. As illustrated in Figure 8, the overlap between the detected discharge locations and those that were visually observed verified the feasibility and effectiveness of this method in detecting discharge locations.
# Automatic Test Data Generation Using the Activity Diagram and Search-Based Technique
## 4 Experiment Description
### Experiment Planning
#### 4.2.1 Research Questions  
The goal of this experiment is to assess and compare AutoTDGen with the two alternative approaches (Section 3). Hence, its evaluation against alternative approaches depends on several variables. By taking these variables into account for testing the proposed technique against the defined goal, referring to the guideline on how to from RQs [42], the experimental investigation aims to empirically address the following RQs:  
* RQ1: How do the tests generated by the proposed AutoTDGen perform, compared with alternative approaches, in terms of statement and branch coverage?
* RQ2: What is the difference in fault detection effectiveness between the tests generated by the proposed AutoTDGen and alternative approaches?
* RQ3: Is there any variation in the types of faults detected by the proposed AutoTDGen and alternative approaches?
* RQ4: What is the interaction relationship between the test coverage and fault detection effectiveness of the adopted techniques and subject properties?  
RQ1, RQ2, and RQ3 compare the proposed AutoTDGen with alternative approaches in terms of the percentage of coverage attained and the test effectiveness, indicated by the proportion of artificially seeded faults (mutants) detected and variations in the types of faults detected (e.g., various mutation operators). RQ4 investigates whether the performance of the applied testing techniques is affected by the properties (e.g., complexity, concurrency, or real-time behavior) of the subject systems.
## 3 Results
### Performance of v-ddPCR on Routine and Retail Samples
#### 3.7.2 Retail Samples  
Twenty-five raw chicken samples (RS) were collected from retail shops. As illustrated in Figure 6, 14 out of 25 RS, which build up 50% of the entire samples, were detected positive for _Campylobacter_ with at least one method. Three of them were quantified above PHC (RS_03 and RS_15 in both PCR methods, RS_04 in a solely v-ddPCR method), indicating that the ISO standard method failed in the quantification of samples above PHC. The other 11 RS were detected below PHC limit based on at least one of the three methods.  
Figure 5: Analysis of LGL routine samples: chicken breast meat (BM) from retail. Analysis in culture, v-ddPCR and v-qPCR. The Process Hygiene Criterion (PHC: \(\log_{10}\) 3.0) is marked with a black line.
## 2 Methods
### Path Connection
#### 2.3.1 Two-Stage Connection Parameter Settings  
In the path connection process of FMT, the connection radius \(r_{n}\) is a constant that remains unchanged throughout, which limits the flexibility of path exploration. For instance, when the distance between the start and goal points is large, a small connection radius would require a significant number of iterations. Conversely, if the distance between the start and goal points is small, using a larger connection radius would result in overly linear optimal paths. Figure 5 depicts the search tree and optimal paths generated by FMT with different connection radii.  
To enhance the flexibility and efficiency of path expansion, different connection radii are set for each stage using Equation (4):  
\[r_{n}=\frac{\lambda V_{space}}{N_{sample}} \tag{4}\]  
\(N_{sample}\) represents the number of sampled points, \(V_{space}\) represents the volume of the sampling space, and \(\lambda\) represents the connection coefficient. After the initial sampling, in order to find more feasible paths from the start to the goal point within a smaller number of iterations, \(\lambda\) is set to 5. This allows for larger connection step sizes in the search tree, resulting in fewer iterations needed to find feasible paths. After the secondary sampling, a set of path points is formed around the feasible paths. To explore more path points in the search tree, \(\lambda\) is set to 2.5. This restricts the search range for each step, leading to increased iterations, but it also brings diversity to the path search.