esa-sceva/satcom-chunk-collection huggingface dataset:
# Using the Guided Fireworks Algorithm for Local Backlight Dimming
## 2 Optimization Model of the Local Dimming Problem
### The New Model of the Local Dimming Problem  
PSNR is a classical image quality evaluation method; it evaluates image quality mainly based on the errors between pixels of the evaluated image and the reference image. This evaluation method, however, doesn't consider the characteristics of the human visual system. After the PSNR method, another image quality evaluation method, the SSIM index, was proposed. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, the SSIMindex uses an alternative complementary framework for quality assessment, based on the degradation of structural information [19]. According to the experimental results shown in [19], the SSIM index usually has a better performance than PSNR in terms of image quality evaluation. The SSIM index between two image signals x and y can be calculated by:  
\[\text{SSIM(x,y)}=\frac{(2\mu_{x}\mu_{y}+C_{1})(2\sigma_{xy}+C_{2})}{(\mu_{x}^{2} +\mu_{y}^{2}+C_{1})(\sigma_{x}^{2}+\sigma_{y}^{2}+C_{2})} \tag{5}\]  
where \(\mu_{x}\) and \(\mu_{y}\) are the mean intensities of x and y respectively, \(\sigma_{x}\) is the variance of x, \(\sigma_{y}\) is the variance of y, \(\sigma_{xy}\) is the variance of x and y, and \(C_{1}\) and \(C_{2}\) are two constants to stabilize the division with weak denominator. In [19], the mean SSIM (MSSIM) index was used to evaluate overall image quality:  
\[\text{MSSIM(X,Y)}=\frac{1}{M}\sum_{j=1}^{M}SSIM(x_{j},y_{j}) \tag{6}\]  
where X is the reference image and Y is the evaluated image, \(x_{j}\) and \(y_{j}\) are the image contents at the \(j\)th local window respectively, and \(M\) is the number of local windows of the image.  
On the basis of our previous work [12], this paper continues to focus on improving image quality. The MSSIM index was taken as the indicator of the displayed image quality. We used \(f(R)=\frac{1}{\text{MSSIM(X,Y(R))}}\) as the objective function, where \(R\) is the solution of the problem which represents a group of backlight block luminance. \(R=[r_{1},r_{2},\cdots r_{i},\cdots r_{K}]\), where \(K\) is the number of backlight blocks, \(r_{i}\) is an integer between 0 and 255 representing the luminance level of the \(i\)th block, and Y(\(R\)) represents the displayed image processed by solution \(R\). The model of the local dimming problem is:  
\[\left\{\begin{array}{l}\min:\ f(R)=\frac{1}{\text{MSSIM(X,Y(R))}}\\ \text{s.t.}\ \frac{\frac{1}{K}\sum\limits_{i=1}^{K}r_{i}}{r_{full}}\leq PC_{ limit}\end{array}\right. \tag{7}\]
## 3 Ontology Modeling and Quality Measures
### Key Issues in Ontology Design  
Regarding the approaches proposed in the literature, it can be seen that the proposed metrics focus mainly on the structure of class relationships, and to an extent cover the distribution of the instances. In [20], it is stated that ontology design usually starts and stops with designing taxonomy. Our motivation here is to show that the properties or attributes are important factors of ontology quality. The attributes play an important role in data and knowledge modeling, as they convey the concrete data values, while classes can be considered as containers of the related attributes. The correctness of a data model is based primarily on the correct structure of the attributes. The proposed model to enhance the role of properties in ontologies is based on the adoption of a conceptual description following Formal Concept Analysis (FCA) for ontology modeling.
# An Efficient Location Verification Scheme for Static Wireless Sensor Networks
## 5 Security and Performance Analysis
### Performance Analysis
In terms of computation cost, the lightweight scheme does not demand normal sensors to perform complicated calculations. Instead, the verifier alone performs all necessary verification calculations. The computation cost to the verifier then can be represented as \(O(N)\), since it has to perform computations for four matrices involving \(N\) sensors, all of which can be computed in the same loop. By comparison to the previous two schemes, the MSRLV scheme communication cost is only two. Each verifier sends one verification request packet and receives one packet in response with the results of the process. In terms of computation cost, the verifier and the claimant should each perform XORs and distance calculations for their neighbors, so the total is \(O(m)\simeq O(N_{ne})\) in complexity notation. Figure 12a,b show the communication and computation costs of each scheme according to the number of sensors in the network. For the comparison, it is further assumed that sensors are uniformly distributed in a 300 m \(\times\) 300 m area and that the transmission range of a sensor is assumed to be 20 m. Thus, there are approximately 0-10 neighbors for any given sensor when there are a total of 100-750 sensors in the network. Figure 12b describes the result in log-scale because of the huge difference in the cost between methods. Note that the phantom scenario is depicted for the \(iter=7\) case. When considering the case of moderate density among scenarios, i.e., 600 sensors in the network, the scheme proposed in this paper has a 77% better communication cost and a 92% better computational cost on average than both the lightweight and phantom schemes. Although the lightweight scheme cuts the computation cost of individual sensors down by employing the central verifier, its computation cost for the entire system is very high because the central verifier has to perform all of the computations. In light of these results, the proposed scheme is more effective at single sensor verification than competitive schemes.
# Discovery and Flavor Characterization of High-Grade Markers in Baked Green Tea
## 3 Materials and Methods
### Taste Components' Detection  
#### 3.6.1 HPLC-MS Detection of Phenolic Compounds  
The qualitative and quantitative analysis of phenolic compounds was performed using high-performance liquid chromatography (Agilent 1200, Agilent Technologies, Palo Alto, CA, USA) coupled with QqQ mass spectrometer (Agilent G6410B, Agilent Technologies, Santa Clara, CA, USA). Preliminary detection was performed in full-scan mode and product ion mode of mass spectrometry for precursor ions and product ions; collision energy (CE) and fragment voltage were optimized using Optimizer software (Agilent Technology, (Santa Clara, CA, USA).  
HPLC parameters were as follows: Mobile phase A was acetonitrile and mobile phase B was 0.1% formic acid water. A Zorbax Eclipse SB-C18 column (4.6 mm \(\times\) 150 mm, 5 \(\upmu\)m; Agilent Technologies, Santa Clara, CA, USA) was used. The injection volume was 10 \(\upmu\)L, and the column temperature was set at 25 \({}^{\circ}\)C. The flow rate was set at 1 mL/min and the DAD detection wavelength was 280 nm. The following gradient was used (mobile phase A in B): 10% isocratic elution for 10 min from 10 to 40% (10-15 min), 40% isocratic elution (15-18 min) from 40 to 10% (18-20 min), and 10% reconditioning (20-25 min).  
MS parameters were as follows: polarity, negative; capillary temperature, 300 \({}^{\circ}\)C; capillary voltage, 4000 V; drying gas temperature, 300 \({}^{\circ}\)C; drying gas flow rate, 6 L/min; nebulizer gas pressure, 55 psi.  
The relative concentrations of all phenolic compounds were obtained using semi-quantitative method with ethyl gallate used as the internal standard.
## 5 Implementation of the Flow Rate Control System
### Model Parameter Identification  
The model parameters of the drive systems of the \(X\)-, \(Z\)-, and \(\Theta\)-axes in the automatic pouring machine, as depicted in Figure 1b, were identified by fitting the simulations of the drive system models to the experimental data. The identified model parameters are presented in Table 2.  
In the model parameters of the pouring process, the parameters \(A(\theta)\), \(V_{s}(\theta)\), and \(q(h)\) are related to the shape of the ladle, as depicted in Figure 2, and can be obtained using CAD calculations. The obtained parameters are presented in Figure 8. Figure 8a,b denote the liquid plane area, which is horizontal to the pouring mouth, with respect to the tilt angle and its partial derivative with respect to the angle, respectively. Figure 8c,d show the liquid volume under the tip of the pouring mouth and its partial derivative, with respect to the tilt angle, and Figure 8e shows the ideal flow rate of the outflow liquid, with respect to the liquid height at the pouring mouth. The ideal flow rate is indicated as the flow rate coefficient \(c=1\). Furthermore, the length between the center of the rotational axis of the tilting ladle motor and the tip of the pouring mouth, as depicted in Figure 7b, is obtained as \(r_{l}=0.206\) m, by CAD calculation.  
\begin{table}
\begin{tabular}{c c c c} \hline  & \(X\)**-Axis** & \(Z\)**-Axis** & \(\Theta\)**-Axis** \\ \hline Gain, \(K\) & 1.00 & 1.00 & 1.00 \\ Time constant, \(T\) & 0.050 s & 0.050 s & 0.022 s \\ \hline \end{tabular}
\end{table}
Table 2: Identified model parameters for the drive systems of the \(X\)-, \(Z\)-, and \(\Theta\)-axes.  
Figure 8: Model parameters related to the ladle shape. (**a**) Area of liquid plane which is horizontally located on the pouring mouth, \(A(\theta)\). (**b**) Partial derivative of the area \(A(\theta)\), with respect to the angle \(\theta\). (**c**) Liquid volume under the tip of the pouring mouth, \(V_{s}(\theta)\). (**d**) Partial derivative of area \(V_{s}(\theta)\), with respect to the angle \(\theta\). (**e**) Ideal flow rate \(q_{id}\) of the outflow liquid, with respect to the liquid height \(h\) at the pouring mouth.  
The flow rate coefficient, \(c\), in the pouring process can be identified by fitting the simulation to the experimental data using the pouring model. In this study, the flow rate coefficient was obtained as \(c=0.75\). The density of the liquid is given as \(\rho=10^{3}\) kg/m\({}^{3}\), as water is considered to be the target liquid in this study.  
In the load cell model, the weight of the part transferred along the \(Z\)-axis is \(w_{z}=18.6\) kg. Additionally, the time constant in the load cell was identified as \(T_{l}=0.16\) s.
# Empirical Performance Models of MAC Protocols for Cooperative Platooning Applications
## 3 Motivation and Methodology for Empirical Models of MAC Protocol Performance  
In previous work [19], we reported network performance in a highway platooning scenario (such as that of Figure 2) when only coordinated beaconing traffic exists, i.e., no other data or control flows existed apart from periodic beacons transmitted by platoon members. Periodic beaconing is a network functionality that is often explored to enable safety and infotainment applications, and it is provided by design by the two regional protocol stacks, with beacons being called cooperative awareness messages (CAM) in ETSI ITS-G5 [9] and basic safety messages (BSM) in WAVE [8]. Beacons are sent at fixed intervals in the range of 0.1 to 1 s for cooperative applications. A number of works propose mechanisms to adapt the beaconing rate to the channel load, of which we highlight [20];the authors of [21] and [22] apply the idea specifically to the platooning context, in which beacon transmissions of the platoon members can be coordinated.  
In our analysis in [19], we observed that relationships between network performance metrics and the number of vehicles in a platoon could be captured and described by empirical models. Such results motivated our interest in searching for a wider range of empirical relationships between network topology and performance metrics, to provide information to application designers. Our preliminary conclusions held for three MAC protocols: CSMA/CA (contention-based), and PLEXE-slotted and RA-TDMAp (overlay TDMA-based). Note that, among all protocols reviewed in the previous section, these are the only ones that are readily available or implementable on commercial off-the-shelf (COTS) IEEE 802.11p network interfaces, without need for any hardware or device- modifications. We review briefly the operation of these protocols in the following section, and then review the conclusions reported in [19] that sustain our approach of producing empirical models.
# Dynamic Modeling and Optimization of Tension Distribution for a Cable-Driven Parallel Robot
## 2 Design of Parallel Robot
### Principle of Working for the Robot  
As shown in Figure 3, this is the working principle of the cable-driven parallel robot. The servo motor starts to work when it receives the signal sent by an upper computer, which is in the control box, and the cable drum will rotate with the servo motor at the same speed. Therefore, the cable could be released and pulled by the rotation of the cable drum. The attitude of this cable is fixed by Limit Pulley 1 first, and then the cable is tensioned by a tension pulley. Moreover, the other pulley, named Limit Pulley 2, is used to fix the attitude of the cable. In order to control the direction of the cable much better, an adaptive pulley is mounted behind Limit Pulley 2. Finally, the cable is fixed to the end-effector of the robot after the cable comes around the adaptive pulley. And the movement of the end-effector can be implemented by releasing and pulling cables.
## 4 Experiment and Discussion
### Experiment on Environmental Sound Recognition  
#### 4.4.1 Results  
The experimental conditions and steps in the environmental sound assessment are nearly the same as those of speech recognition experiment. The results are shown in Figure 9. Compared with the speech test, the fitting effect of the proposed method is reduced, with an average recognition rate of 69.9%, which is a 4.2% improvement compared with the GP algorithm and a 2.9% improvement compared with the IEC algorithm. However, compared with the baseline algorithm, the recognition rates of three algorithms all increase. From the figure, for the proposed algorithm, the recognition rate of patient S7 is the highest, reaching 84%, and that of patient S6 is the lowest, with a value of 55.2%.  
For the above data, two-factor variance analysis is also conducted. The results are shown in Table 3. From the table, compared with the IEC algorithm, the proposed algorithm shows statistical significance except for patient S2. For the GP algorithm, the exceptions are S1 and S3. In addition, compared the GP algorithm with the IEC algorithm, patient S6 with the GP algorithm is decreased significantly, patient S1 and S3 are enhanced significantly, and the other patients show no significant difference.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method Comparisons**} & \multicolumn{8}{c}{**Subjects ID**} \\ \cline{2-9}  & **S1** & **S2** & **S3** & **S4** & **S5** & **S6** & **S7** \\ \hline I vs. G & \(\Delta\) & \(\Delta\) & \(\Delta\) & *** & \(\Delta\) & *** & \(\Delta\) \\ I vs. P & * & *** & * & \(\Delta\) & *** & *** & *** \\ G vs. P & \(\Delta\) & * & \(\Delta\) & *** & *** & \(\Delta\) & ** \\ \hline \hline \end{tabular} Note: Statistical significance is shown by asterisk symbols: \(\Delta\)\(p\)\(>\) 0.05; * 0.01 \(<\)\(p\)\(<\) 0.05; ** 0.001 \(<\)\(p\)\(<\) 0.01; *** \(p\)\(<\) 0.001.  
\end{table}
Table 2: Statistical analysis of the recognition rate for speech.  
In addition, the fitting time for the environmental audio test is similar to the speech test.
# Multi-Antenna Jammer-Assisted Secure Short Packet Communications in IoT Networks
## 3 Secrecy Performance Analysis
### Secrecy Throughput  
The secrecy throughput is the average secrecy rate where confidential messages from [PERSON] are reliably transmitted to [PERSON] under a certain secrecy constraint. Mathematically, it can be expressed as:  
\[T=\mathbb{E}_{\gamma_{b},\gamma_{c}}\bigg{(}\frac{B}{N}(1-\epsilon)\bigg{)}= \frac{B}{N}(1-\tilde{\epsilon}) \tag{10}\]  
where \(B\) is the information bits delivered over \(N\) channel uses and \(\tilde{\epsilon}=\mathbb{E}_{\gamma_{b},\gamma_{c}}(\epsilon)\) is the average decoding error probability.
# Shear Wave Velocity Estimation Based on Deep-Q Network
## 5 Numerical Experiments
### Case 2  
Case 2 considers another geoacoustic model from the inversion results in the North Sea [2]. The geoacoustic model consists of a sediment layer with a linear velocity gradient over a continuous half-space (namely, LC introduced in [1]). The model parameters listed in Table 7 are used to generate phase velocity dispersion curves in the frequency range of 3 to 18 Hz. Five modes are selected as the ground truth. \(Vs_{t}\) and \(Vs_{b}\) refer to the shear wave velocities at the top and bottom of the sediment layer, respectively.  
The search bounds of the sediment parameters \(H\) (m), \(Vs_{t}\) (m/s), and \(Vs_{b}\) (m/s) are \([0,150]\), \([10,100]\), and \([100,500]\), respectively.  
The estimated dispersion curves and shear wave velocity profiles are shown in Figure 7 and Figure 8, respectively. Their legends are the same as in Figure 3 and Figure 4, respectively.  
The performance analysis for Case 2 is shown in Table 8, in which the bold fonts refer to the lowest relative error, misfit value, or running time over each row.  
From Figures 7 and 8, and Table 8, the following points can be found:  
* The low-to-high ranking for the misfit values is the DQN framework, DE, ASSA, and GA. The ranking is consistent with the low-to-high ranking for the overall relative error. This trend is already found in Table 6.
* The DQN framework attains the lowest misfit, the shortest running time, and the lowest overall relative error compared to other candidate methods.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Layer** & \(h\) **(m)** & \(\rho\) **(kg/m\({}^{3}\))** & \(Vp\) **(m/s)** & \(Vs_{t}\) **(m/s)** & \(Vs_{b}\) **(m/s)** \\ \hline Ocean & 364 & 1.0 & 1490 & 0 & 0 \\ Sediment & 49 & 1.8 & 1700 & 28 & 385 \\ Half-space & 200 & 1.8 & 2000 & 385 & 385 \\ \hline \hline \end{tabular}
\end{table}
Table 7: LC model for Case 2.  
Figure 7: The estimated dispersion curves with the ground truth in Case 2.
# A Secure Link-Layer Connectivity Platform for Multi-Site NFV Services
## 4 Implementation and Validation
### Functional Validation  
To validate the functionality of the L2S platform, we considered a realistic use case, where an IP television (IPTV) service is to be provided to a set of subscribers at their residential environments. The IPTV service has a head end, which receives the video signals from different TV content providers, and processes them to produce the compressed video formats that will be delivered to IPTV subscribers. Following a common approach for large-scale IPTV services, the head end transmits the video content of the TV channels to a set of video-serving offices. Each service office can then distribute the content to IPTV subscribers on a specific local area (e.g., a municipality). The IPTV service resorts to IP multicast technologies, to support the efficient distribution of video content from the head end to IPTV subscribers.
## 4 Experiments and Results
### Results Using Mel-Cepstral Distortion (MCD)
Figure 11 shows the results of the average MCD between the natural signals (blue color), the average distortions of the synthetic signals generated with the proposed method (red color), the average distortions of the synthetic signals generated with the proposed method without applying an EWT algorithm (dark blue color), and the average distortion of the synthetic signals generated with the model in [21] (green color) using the MCD method. Figure 11: Result of mel–cepstral distortions (MCDs). Figure 10: General diagram of the procedure to calculate the signal distortion. To verify that the generator in the GAN model was not just copying some of the training examples, we computed the MCD distortion of a synthetic signal against each of the signals used in the training dataset. Figure 12 shows the resulting MCD values, with and without the EWT postprocessing. It can be seen that in none of the cases is there an MCD value equal to approaching zero; therefore, the generated signal is not a copy of any of the training inputs. It can be seen in Figure 11 that the distortions of the natural signals and the signals generated using the proposed method are in the same range, unlike the distortion obtained with the signals generated using the model [21].
## 1 Introduction
### Research Process  
As shown in Figure 1, this study first analyzed the situations of the target products and the AOI terminal. We collected the images produced by the AOI terminal and used them as the training samples for the model. Next, we named the folders, stored the standard images respectively, and used LabelImg-master software to label the categories and locations of the defects. We imported the images into the ResNeXt model and the Inception v3 model respectively to tell whether they were defect or standard images, and then imported the images into the YOLO v3 model to identify their defect categories and locations. We integrated the ResNeXt+YOLO v3 model and the ResNeXt+YOLO v3 model and ran the tests respectively, and then used a confusion matrix to compare the accuracy and false alarm rates. Finally, we chose the one with a higher accuracy rate and a lower false alarm rate as the base model to set up the defect detection system.
## 4 Analysis and Suppression Method of Short Circuit Current in MMC-Based MTDC Power Grid
### DC Short Circuit Current Suppression Method in MMC-Based MTDC Power Grid
#### 4.2.1 MMC Topologies with DC Fault Ride Through Capability  
The HB-SM is the most widely applied topologies of MMC for DC power girds due to the simple structure and low cost. However, MMC that is based on HB-SM cannot block the fault currents fed from the AC grid [44], as shown in Figure 10a. Without DC CBs, it can only clear DC fault line by tripping the AC circuit breakers [45]. Various SMs have been proposed and investigated in order to improve fault-blocking performance of the MMC-based MTDC power grids [7]. Figure 10b-e show the short circuit current discharge circuit (in red line) for various SM topologies.  
* FB circuit: after a DC short circuit current occurs, all of the IGBTs in the SMs are blocked and the capacitor voltages can generate reverse voltages to block the AC currents, as shown in Figure 10b. Thus, the FB-SMs can provide the DC fault ride through capability [17].
* CD circuit: in the CD based MMC-HVDC system, although the SMs can block the DC fault current [2], they can only generate a reverse voltage \(U_{\text{dc}}\) per bridge arm, which is, half of the reverse voltage produced by the FB-SMs per arm, as shown in Figure 10c. Thus, it takes more time for the CD-MMC to suppress the fault current to zero [16].  
Figure 9: DC side modified AVM of the MMC.  
* 5 LCC circuit: during a DC short circuit fault, the short-circuit current can be blocked by the two capacitors of the 5 LCC-SM, as shown in Figure 10d. The reverse voltage generated by 5 LCC-SM per arm is the same as the voltage produced by the FB-SM [17].
* 3 LCC circuit: similar to the HB-SM, the three-level NPC and FC SMs cannot handle any DC fault, as shown in Figure 10e,f. Furthermore, from a manufacturing perspective, this solution is not very attractive [46].  
The power transmission will be interrupted and the entire DC power grid will be shut down because SMs with DC fault ride through capability cut off the fault line through blocking all of the converters. Meanwhile, changing SM topologies will also create extra investment cost and additional loss of the project [2].