esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Control Scenarios
### Total Flow Chart of Service
#### 4.2.2 **[Service Package #2]** Context Awareness and Actuator Control-Based Fire Detection
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Service** & \multicolumn{2}{c}{**Details of the Scenario**} \\ \hline \hline Fire occurrence—Control the Damper & \multirow{4}{*}{\begin{tabular}{c} Fire occurrence—Control the Damper \\ Breaker \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} **Related application technologies**: Smart sensor based fire situation \\ recognition technology, actuator control technology through smart sensor \\ intelligent connection \\ \end{tabular} } \\  & & **Service delivery method**: Control of damper breaker based on recognition of fire situation \\ \cline{2-3}  & & **Service effect**: By controlling the damper breaker through the recognition of the fire situation, it quickly prevents the spread of fire and protects the safety of the rescuers \\ \hline \hline  & \multirow{4}{*}{\begin{tabular}{c} Gas explosion situation—Control the Gas \\ Valve Breaker \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} **Service details**: IoT sensors (gas leakage sensor, temperature, flame \\ detector) detect fire situation \(\textgreater{}\textgreater{}\) Gas is blocked through gas leakage detector depending on the gas leak situation \\ \cline{2-3}  & **Service main target**: Actuator control by sensor detection \\ \cline{2-3}  & **Service domain**: Smart home/building/etc. Applicable to all fields where smart sensors and actuator control technology are applied \\ \cline{2-3}  & **Related application technologies**: Fire sensor recognition based on smart sensor, actuator control technology through smart sensor \\ \cline{2-3}  & **Service delivery method**: Gas breaker control based on fire \\ \cline{2-3}  & **Service effect**: Prevent secondary fire due to gas by controlling gas breaker based on recognition of fire situation \\ \hline \hline  & \multirow{4}{*}{\
\begin{tabular}{c} Power overload situation—Control the \\ Electric Breaker \\ \end{tabular} } & \multirow{4}{*}{
\begin{tabular}{c} **Service details**: IoT sensor (Electric Leak Detector, Integrated Gas Sensor, \\ Flame Detector) detects fire situation \(\textgreater{}\)Shuts off electricity through an electric breaker \\ \cline{2-3}  & **Service main target**: Actuator control by sensor detection \\ \cline{2-3}  & **Service domain**: Smart home/building/etc. Applicable to all fields where smart sensor and actuator control technology is applied \\ \cline{2-3}  & **Related application technologies**: Intelligent connection of sensors based on ICT/IoT technology, real-time situation monitoring technology, actuator control technology through smart sensor intelligent connection \\ \cline{2-3}  & **Service delivery method**: Control of electric breaker based on recognition of fire situation \\ \cline{2-3}  & **Service effect**: Prevent secondary fire due to electricity by controlling electric breaker based on recognition of fire situation \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Service Package #2 details of the scenario.
# Verification of Control System Runtime Using an Executable Semantic Model +
## 3 Runtime Formal Model
### Instruction Decoding  
As detailed in Section 2, the identifier vmcode of a machine instruction comprises the identifier of the group ig and the specific type of the instruction it. The processing of each instruction in the virtual machine can be divided into two parts: a generic function (\(\mathcal{U}\)), which involves fetching the instruction group and its variant, and a dependent function (\(\mathcal{C}\)), which is specific to each instruction executed by the machine. Processing both the generic and dependent parts results in returning a new state. So, the generic function decoding any instruction can be defined as follows:  
\[\mathcal{U}[\text{any\_instruction}]\ =State\to State \tag{15}\]  
After an instruction is decoded by \(\mathcal{U}\), the dependent function \(\mathcal{C}\) is called:  
\[\mathcal{C}[\text{instruction}]\ =State\to State \tag{16}\]  
The semantic model of the function \(\mathcal{U}\) is expressed by Formula (17):  
\[\begin{split}&\mathcal{U}[\text{any\_instruction}]\ =\lambda s.\\ &(cm,dm,cs,ds,cr,dr,ex,flg):=\ s\\ & ig:=\ Get1 BMem(cr,cm)\\ & cr_{1}:=\ cr\oplus 1\\ & it:=\ Get1 BMem(cr_{1},cm)\\ & cr_{2}:=\ cr_{1}\oplus 1\\ & s_{1}:=\ \textbf{match}\ ig\ \textbf{with}\\ &\  
Two examples of instruction groups are given in Formula (17). The ig=01 denotes addition instructions, while ig=04 denotes the division ones. In the first case, it=22 means the addition of two INT operands performed by \(\mathcal{C}[\texttt{ADD:INT:r:op1:op2}]\). For it=32, three operands are added by the function \(\mathcal{C}[\texttt{ADD:INT:r:op1:op2:op3}]\). Similarly, ig=04 and it=02 call the \(DIV\) function for two INT operands, while it=09 means the division of two REALs. The result of the function \(\mathcal{C}\) is a new state of the machine stored in the variable \(s1\).
## 3 Research on Image Acquisition System of Lining Cracks
### Shooting System Hardware Analysis
#### 3.1.2 Shooting Range Calculation  
For images of the tunnel lining, stitching is required to form a panoramic distribution map of the tunnel for display. Therefore, the actual size of a single picture should not be too small. Concerning the actual detection experience in the tunnel in the past, and considering the overlap area required for splicing, the side length of the field of view to be selected is about 1.0 m, then the minimum number of pixels required for one side of the camera n can be calculated by Equation (1):  
\[n=\frac{r}{p}=\frac{1.0\times 1000}{1.0}=1000 \tag{1}\]  
Figure 4: The test piece and corresponding crack width (the number in the figure is the width of the crack there in mm).  
where \(n\) is the minimum number of pixels, \(r\) is the side length of shoot range, and \(p\) is the pixel resolution.  
After considering the cost and camera sensor model, the Baumer TXG13 area scan camera with a pixel size of 1392 \(\times\) 1040 is finally selected, corresponding to the actual shooting range 1.392 m \(\times\) 1.04 m.
## 2 Materials and Methods
### Transmission and Scanning Electron Microscopy  
_L. infantum_ promastigotes were exposed to essential oils at concentrations that inhibit viability by 50% (IC\({}_{50}\)) and the morphological alterations were investigated by electronic microscopy. For ultrastructural studies with transmission electronic microscopy, samples were treated as reported previously ([PERSON] et al., 2001). Briefly, the cell was fixed with glutaraldehyde in sodium cacodylate buffer, post fixed in osmium tetroxide and uranyl acetate, dehydrated in ethanol and in propylene oxide, and embedded in Epon 812 (TAAB 812 resin). Ultrathin sections were stained with lead citrate and uranyl acetate. For scanning electronic microscopy, the samples were fixed and post-fixed as described for transmission, dehydrated in ethanol, critical point dried using CO\({}_{2}\), and sputter-coated with gold. The specimens were examined in JEOL JEM-100 SX transmission electron microscopy (TEM) at 80 kV and in JEOL JSM-5400 scanning electron microscope (SEM) at 15 kV.
## 2 Materials and Methods
### Methods  
#### 2.2.1 Technical Workflow  
In this paper, the extraction method for fishing grounds was investigated using AIS data from mainland China's purse seine fishing vessels operating in the Arabian Sea. As shown in Figure 2, the process is outlined in the following steps:  
* The AIS data were compared with the relevant catch data and identity information of mainland China's purse seine fishing vessels, and the AIS data corresponding to the MMSI numbers of the relevant fishing vessels were screened out.
* Data cleaning was conducted on the original AIS data following the method outlined in Section 2.1.2.
* The cleaned data were analyzed to identify AIS records indicating the fishing behavior of the fishing vessels and the trajectories of fishing vessel operations were extracted, which were visually represented as fishing vessel operation trajectories.
* Three methods, namely, fishing effort (FE), KDE, and kernel density hotspot analysis (KDHSA), were utilized to extract fishing grounds and subsequently, the results were compared and analyzed.
* The extraction results of the three methods were compared with the catch data, and a spatial similarity analysis was performed.
* The most effective method was determined and statistical analyses were conducted on the extracted fishery information, including global autocorrelation and other relevant parameters.
## 4 Results
### Verification
#### 4.1.4 Overlapping of Turbulence Wedges  
The position of the turbulence wedges on the rotor blade depends on the location of surface defects triggering the premature flow transitions. Therefore, multiple turbulence wedges can be positioned closely to each other and overlap. In order to illustrate the capability of the introduced image processing methods to detect premature flow transitions even if the turbulence wedges are overlapping, simulated thermographic image similar to Figure 10 are created. Figure 12 shows the thermographic flow visualization with (a) two separated turbulence wedges, (b) two turbulence wedges with a medium overlap, and (c) two turbulence wedges greatly overlapping. All turbulence wedges have a relative contrast of \(c_{\text{w}ed}=50\%\).  
Figure 11: Mean euclidean distance between the true and the evaluated transition lines of the state-of-the-art image processing (red), method A (blue), and method B (green) over the CNR between the turbulence wedge and the surrounding laminar flow regime. The relative contrast of the turbulence wedge is \(c_{\text{w}ed}=50\%\). The mean euclidean distance of method A and B increases with increasing noise but remains below the state-of-the-art method. This proves that the introduced image processing is robust towards even low CNR.  
Figure 10: Simulated thermographic flow visualization image with premature flow transitions along the flanks of a turbulence wedge with a relative contrast of \(c_{\text{w}ed}=50\%\). The relative noise amplitude is (a) 0.1, (b) 1.0, and (c) 1.5 times the measured image noise in a real, reference image. The corresponding CNR are 496, 73, and 33, respectively. Increasing noise results in more local gradient maxima falsely interpreted as premature flow transitions. The consequence is an increased uncertainty of the flow transition line for method A (blue) and B (green).  
The image processing methods A (blue) and B (green) are both able to cope with the overlapping of turbulence wedges and location of the flow transition line. For the test case of closely overlapping turbulence wedges, see Figure 12c, the two turbulence wedges merge into on area. This is due to the image filtering for smoothing purposes of the image processing in order to reduce the influence of image noise and increase the robustness. Nonetheless the surface area of both turbulence wedges are detected by the introduced methods. This shows that the angle of the flow transition segments is used as a priori knowledge, but not the forming of individual turbulence wedges separated from each other. The state-of-the-art method (red) is shown as a reference but is not able to detect the premature flow transitions due to the low contrast of the turbulence wedge, see Section 4.1.2.
## 3 Experiment and Results  
In order to verify the effectiveness of the proposed scheme, a proof-of-concept experiment is conducted according to the principle framework in Figure 1. As shown in Figure 5, the optical signal from the laser (DFB, EMCORE 1772), with the wavelength of the C-band and a power of 10 dBm, was sent to the Dpol-DDMZM (FTM7980 EDA, 21.5 GHz). The modulator had a half-wave voltage of 3.5 V. Moreover, it was biased at the appropriate working point. An 8 GHz RF signal generated from the single frequency source (Agilent E4438C) was first boosted by an electrical amplifier with a gain of 30 dB and was then inserted into a 3 dB divider. A PS was used to adjust the power and the phase difference of the RF between the two DDMZMs. The modulated optical signal at the output of the Dpol-DDMZM was then sent to the PD (Finisar, 50 GHz) for intensity detection. The responsivity of the PD was about 0.6 A/W, and the 3 dB bandwidth was 50 GHz. An electrical amplifier with a gain of 30 dB was employed after PD to boost the generated pulse train signal. In the end, the electric signal was divided by a 3 dB divider. One part was sent to the electrical spectrum analyzer (ESA, Keysight, N9030B) and the other to the oscilloscope (OSC, Keysight, DSOZ592A). The parameter setting of the experiment device was adjusted according to the simulation.  
Figure 6 shows the electrical power spectrums and generated waveforms of the sawtooth; in Figure 6a, the even harmonic power is 8.0 dB lower than the fundamental frequency power, and the third harmonic power is 16.0 dB lower than the fundamental frequency power, both of which are roughly similar to the values in the simulation (6.0 dB and 9.5 dB). Figure 6b is the output signal waveform of the PD in the time domain, which is consistent with the ideal waveform of the sawtooth signal. The RSME between the generated and ideal waveform is less than 0.190.  
Figure 7 shows the experiment results of the electrical power spectra and the waveforms, respectively. As marked in Figure 7, the ratio between the third harmonic power and the fundamental frequency power is also very close to the value obtained in the simulation. The RSME between the generated and ideal waveform is less than 0.168 for the triangular waveform and 0.135 for the square waveform. In the experiments, the waveform generated in the time domain is not perfect, and the frequency spectrum in the electrical domain is deviated from the ideal frequency spectrum. The main reason is that we used a small signal source and electric amplifier to replace the high-power signal source, which introduced unwanted harmonics.  
Figure 5: Physical map of the built experimental link.
# Evaluation of Online Teaching Quality Based on Facial Expression Recognition
## 1 Introduction
### The Current State of Face Detection  
With the breakthrough of Alexnet in the ImageNet competition, artificial intelligence technology represented by deep neural networks began to be applied in the field of image and object detection [2]. At present, the research on facial expression recognition (FER) technology mainly adopts the deep learning method because, compared with the traditional machine learning method [3], the convolutional neural network (CNN) can extract deeper and more abstract features. It has shown excellent performance in various image classification tasks [4]. Furthermore, the attention mechanism has been widely used in various computer vision tasks, such as saliency detection [5], crowd counting [6], and facial expression recognition. Benefiting from the extensive research and application of deep learning in the field of image and computer vision, as well as the disclosure of a large number of datasets, such as LFW [7] and Celeba [8], face detection and recognition algorithms based on deep learning are increasing day by day. For example, Deepface [9], proposed by Facebook at CVPR2014 in 2014, achieved 97.35% accuracy on LFW, and in 2015, the Face++ [10] adopted by [PERSON] et al. achieved 99.50% recognition accuracy on LFW, and, in 2015, FaceNet [11], launched by Google, achieved 99.63% accuracy on LFW and so on. Using deep learning for face detection can usually achieve higher accuracy. Introducing face detection and expression recognition based on deep learning into online teaching evaluation can provide teachers with more accurate classroom feedback in a timely manner.
## 2 Introduction to IPv6
### Transition Methods
#### 2.3.3 Tunnel
Hosts using ISATAP must be dual stacked while the connecting network can be entirely IPv4, with each ISATAP host connected to at least one ISATAP router. The use of ISATAP is recommended to be stopped as soon as IPv6 connectivity is established in the network. Operating systems, which have automatic tunneling enabled by default, should be configured to have it turned off if ISATAP is not needed. Tunnel brokers deploy dual stack tunnel servers with access to the Internet. Clients can connect to tunnel brokers that establish a tunnel in return. The tunnel broker is responsible for the complete management including DNS, authentication, and access control. The main use of tunnel brokers is for experimental reasons or single individuals not having native support by their ISP yet. Configurations vary depending on the tunnel broker. Information about tunnel brokers can be found in RFC 3053 [53]. There are two more \"tunnel techniques\" still in development. One is Dual Stack Transition Mechanism (DSTM) that is a transition mechanic using 6 over4 for IPv6 dominant networks [54]. The second is the Bi-Directional Mapping System (BDMS) which tries to avoid tunneling but focuses on translation mechanisms [55]. Both have not made it to a standard track by now and are considered experimental.
# Novel Beam Scan Method of Fabry-Perot Cavity (FPC) Antennas
## 3 Results
A comparison between previous works and own is described in Table 3. Even though [11; 12] can scan the main beam continuously, they are hardly applicable for real time-varying systems due to their phase-adjusting methods: mechanically rotating a PRS and injecting water into a PRS. Moreover, while [13; 14; 15] can electrically switch the main beam using PIN-diodes, they cannot realize continuous beam-scanning and a wide steering coverage. On the other hand, our work can electrically and continuously scan the main beam with high gain as well as a wide scan range by introducing the phase-controllable AMC ground plane, which is far favorable for commercial applications.
# Radiation Hardened Read-Stability and Speed Enhanced SRAM for Space Applications
## 3 Simulation Results and Analysis
### Hold Power  
Hold power (HPWR) is important in SRAM design because it represents the power consumed when the memory cells are in a stable state and not actively reading or writing. The measurement method for HPWR in SRAM consists of two components: one through leakage power via the pull-up and pull-down paths and the other through the bit-line. To measure these, a pulse is applied to all nodes storing a '1', ranging from 0 to VDD, and power is measured when VDD is reached. HPWR dissipation occurs due to leakage in the bit lines and within the SRAM's internal structure. In bit lines, NMOS transistors, with their higher carrier mobility, exhibit greater HPWR compared to PMOS transistors. Within the internal structure, the more paths from VDD to the ground, the higher the HPWR. The proposed design, which uses three pairs of NMOS access transistors and has the highest number of paths (six) from VDD to the ground, exhibits the highest HPWR among the compared cells. However, in designing a radiation-hardened SRAM, more emphasis is placed on the robustness of the design against radiation strikes and the stability of the cells. Due to its superior radiation resistance compared to conventional designs, the proposed design is more suitable for radiation-hardened applications. The HPWR values of all comparison cells are presented in Figure 11.  
Figure 10: Comparison results of the word line trip voltage of the proposed RHRSE-20T with previous works.
# Cross-Medium Photoacoustic Communications: Challenges, and State of the Art
## 3 Properties of Linear Photoacoustic  
When high energy light is absorbed by the water, depending on the energy density and irradiance of the light, the physical state of the water medium either changes or does not change. If the property of the water medium does not change, the optoacoustic process is called linear. In linear optoacoustic, absorption of high light energy creates local temperature fluctuation in the water medium which causes volume fluctuation. Eventually this volume fluctuation creates a propagating acoustic signal. The linear categorization is because the power of the generated acoustic signal is proportional to the input laser power. In contrast, when irradiance of light energy is higher than a certain threshold level, the property of the water medium changes, and the optoacoustic process is called nonlinear. As the name suggests, in nonlinear optoacoustic power of the generated acoustic signal is not proportional to the input laser power. This section focuses on the linear optoacoustic mechanism. Achievable communication ranges, laser modulation configurations, and data  
Figure 5: Optical breakdown generated shockwave and bubble expand–collapse shockwaves generated by a single laser pulse focused onto the water. The first transient is the acoustic shock wave generated by the optical breakdown of the water medium and the subsequent three transients are generated due to several bubble collapse and expansion cycles.  
rates are addressed. Usually, the generated acoustic signal by the optoacoustic effect is very broadband. Yet, when an acoustic wave travels a long distance in the water medium, the high frequency components attenuate faster which eventually causes a very weak signal detection at the receiver side. Hence, we will start our discussion by frequency domain analysis and how to generate narrow-band acoustic signals.
# Optically Transparent Honeycomb Mesh Antenna Integrated into OLED Light Source
## 2 Material Characteristic
### Sheet Resistance  
The two popular types of transparent antennas are meshed and based on transparent conductive oxide (TCO)-type antennas. In this work, the ITO film and the mesh structure were studied and compared. The \(R_{s}(\Omega/sq)\) resistance of the sheet and the transparency T (%) were compared to identify the most-suitable material of this design.
## 5 Experiments and Results
### Classification Precision Metric Test
Finally, the NB and SVM algorithms have always obtained lower-ranking performances than the other algorithms. Therefore, it was necessary to perform the [PERSON] statistical hypothesis test to verify if there is a statistical difference between the compared classification algorithms. This test determines whether one algorithm has better or worse rating performance than another, even if its rating accuracy metric percentage is higher or lower.
## 4 Analog Front-End
### Receiving Amplifier  
The receiving amplifier is designed to increase the voltage amplitude of the incoming RF signal and to bias the LVDS stage of the FPGA. It basically consists of three subsequent stages:  
* The common-emitter amplifier provides the main fraction of the voltage gain at a high input impedance. The collector feedback resistor \(R_{\text{a,1}}\) allows amplifier biasing with a small number of components and limited temperature drift [31].
* The emitter follower is applied to decouple the low-power common-emitter stage from the non-negligible input capacitance of the square-wave slicer. By appropriate biasing, it adds a DC offset of approx. 1 V to the RF signal, so that the requirement for a DC biasing of the LVDS comparator in the middle of the supply range is satisfied.
* The square-wave slicer is the comparator-based circuit translating the sinusoidal RF signal to a rectangular wave showing the same OOK or PSK modulation as the input signal. Here, the comparator is fed with a low-pass filtered average of the analog signal (basically representing the DC level) through the branch of \(R_{\text{f,1}}=1\) k\(\Omega\) and \(C_{\text{f,1}}=1\) nF and a signal with fast transient behavior through \(R_{\text{f,2}}=1\) k\(\Omega\) and \(C_{\text{f,2}}=6\) pF (parasitic input capacitance), which limit the impact of noise and interference.  
The circuits are optimized to by a parametric sweep of the biasing resistors \(R_{\text{a,i}}\) in a small-signal simulation to yield the optimum ratio of power consumption and gain. The simulation revealed a maximum gain of 25 at a power consumption of approx. 600 uW and a resistive input impedance of 700 \(\Omega\) between 1 and 20 MHz. The component values are listed in Table 2.