esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Fire Damage Diagnosis System Using Fuzzy Theory
### Process for CPD I  
As previously mentioned, in the CPD I, the visual inspection data on the concrete discoloration (\(CD\)), the crack width (\(CR\)) and the spalling depth (\(SP\)) were utilized as the input variables. [PERSON]'s fuzzy inference method [16; 17] is used to estimate the grade of each variable expressed as A-E, and the final fire damage grade of an individual structural member is then determined by combining the grades of all of the variables. In the process of the CPD I, the fuzzifications of the input variables are performed by substituting each visual inspection datum into the corresponding fuzzy set shown in Figure 4a-c, and the fuzzy  
Figure 4: Fuzzy sets of input data. Figure 3: Triangular and trapezoidal fuzzy sets [15]. rules are then defined as shown in Table 4, where the output fuzzy sets belong to all of the fire damage Grades A-E. For example, as shown in Figure 5, if the concrete color is observed from the visual inspection as pink, slightly affected by light-gray, and the heating temperature is estimated to be 525 \({}^{\circ}\)C, the membership degree of the pink is estimated to be 0.75, and that of the light gray is estimated as 0.25. The fire damage grades of the concrete discoloration can then be estimated using the predefined fuzzy rules \(R_{CA}\) 2 and \(R_{CA}\) 3, for instance, as shown in Figure 6 and Table 4. Therefore, the membership degree of the concrete discoloration belongs to 'SH (slightly-high)' of Grade A by as much as 0.75 according to the fuzzy rule \(R_{CA}\) 2 and to 'M (medium)' by as much as 0.25 according to the fuzzy rule \(R_{CA}\) 3. As shown in the last step of Figure 6, the possibility (\(\phi\)) of each fuzzy rule can be combined, and the possibility (\(\phi\)) of the concrete discoloration belonging to Grade A can then be estimated as 0.614 from the defuzzification process introduced in Equation (3). In addition, as shown in Figure 7, the possibility of Grades B, C, D and E for the concrete discoloration can be estimated as 0.819, 0.716, 0.546 and 0.386, respectively. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Input Parameter** & **Rule No.
# A Machine Learning Model for Predicting Noise Limits of Motor Vehicles in UNECE R51 Regulations
## 4 Prediction of Acceleration Noise Limits by Using the Proposed BPNN Model
### A Case Study of Noise Control for a Light Commercial Truck  
The noise levels of two light commercial trucks made in 2013 and 2016 were measured by Applus+ IDIADA according to the ECE R51 regulations, respectively. There two tested vehicles are the two-generation products from the same motor manufacturer-Jiangling Motors Co., Ltd. (JMC, Nanchang, China). The maximum engine power of the two tested vehicles is 80 kW, and the masses are 4.5 and 4.3 tons, respectively. Therefore, they can be classified to a light commercial truck (vehicle category N2). The sound pressures of the vehicle during accelerating were collected. The sound pressure of the truck made in 2013 (The 2013 model) is 77.3 dB (A), while that of its descendent made in 2016 (The 2016 model) is 76 dB(A) as shown Figure 4. The noise level of the 2013 model is lower than the noise limit specified in UNECE regulations at the time (2005) with a value of 0.7 dB(A), in which the regulations maintained unchanged until 2016. The noise level of the 2016 model is lower than that of specified in the regulations in 2016 with a larger value of 1 dB(A). Apparently, both two tested vehicles meet the requirements of the UNECE regulations. Note that the noise limits in the regulations increase, and become much stricter than before, and the noise level of the Category N2 vehicles during accelerating decreases by 1.3 dB(A). To make sure the fulfilment of the UNECE regulation requirements, the automotive industry strictly follows the regulations on their next-generation product development. The 2020 model could be able to meet the requirements of the current regulations in 2020 and even 2024, if the manufacturer has its product improvements achieved. Unfortunately, the 2020 model was not measured, due to the impacts of COVID-19. However, the next-generation models will face challenges to satisfy the predicted limit of the next revised regulations in future years for the change may be 4.43 dB(A), by using the proposed BPNN model in this research. The predicted value of 69.57 can be used as a reference noise limit for the motor manufacturer to use for their next-generation vehicle design.  
Figure 4: Comparisons on limits and test values for light commercial trucks (Category N2).
## 2 Methodology
### Application Context  
The proposed approach was employed to assess five histological image datasets, namely colorectal cancer (CR), oral epithelial dysplasia (OED), non-Hodgkin's lymphoma (NHL), and liver tissues, covering liver gender (LG) and liver aging (LA).  
The CR dataset consists of histological images extracted from 16 sections stained with H&E, specifically focusing on T3 or T4 stage colorectal cancer. An expert in pathology labeled the images, categorizing them into benign or malignant groups. The dataset includes 165 images, with 74 images representing benign cases and 91 images representing malignant cases [99].  
The OED dataset was created using 30 H&E histological sections from the tongues of mice that had been previously exposed to a carcinogen [100]. This study was approved by the Ethics Committee, protocol 038/39 of the Federal University of Uberlandia. A total of 148 ROIs divided into two classes, healthy (74) and severe dysplasia (74), were considered in this work.  
The NHL dataset consists of 374 H&E images of malignant [PERSON]'s lymphoma subdivided into three classes: Chronic Lymphocytic Leukemia (CLL) with 113 images; Follicular Lymphoma (FL) with 139 images; and Mantle Cell Lymphoma (MCL) with 122 images. The dataset is a collection of samples prepared by different histologists at various hospitals [101].  
The last two datasets comprise liver tissues from 48 male and female mice obtained through the National Institute on Aging, Atlas of Gene Expression in Mouse Aging Project (AGEMAP). Images were acquired manually by a Carl Zeiss Axiovert 200 microscope at 40\(\times\) magnification. The liver gender (LG) dataset consists of 265 images, which were categorized into male (150) and female (115) classes of mice under an ad libitum diet. The liver aging (LA) dataset, on the other hand, contains 528 images of female mice at four ages: 1 (100), 6 (115), 16 (162) and 24 (151) months, also on an ad libitum diet [102].  
An overview of the datasets is given in Table 4, indicating the names and their respective types, number of images, and number of classes. Some examples of images with their respective classes are presented in Figure 4.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Dataset** & **Image** & **Number of Images** & **Number of Classes** & **Resolution** \\ \hline CR & Colorectal Tumor & 165 & 2 & \(567\times 430\) to \(775\times 522\) \\ OED & Oral Epithelial & 148 & 2 & \(450\times 250\) \\  & Dysplasia & & \\ LA & Liver Tissue & 528 & 4 & \(417\times 312\) \\ LG & Liver Tissue & 265 & 2 & \(417\times 312\) \\ NHL & Non-Hodgkin’s & & & \\ Lymphoma & 374 & 3 & \(1388\times 1040\) \\ \hline \end{tabular}
\end{table}
Table 4: General information about the datasets used in this investigation.  
Figure 3: Diagram of the ensemble approach: boxes with dashed lines define that only one of its components was used to compose a solution.
# V2G Strategies to Flatten the Daily Load Curve in Seoul, South Korea
## 2 Daily Load Curve Evaluation Method
### Charging and Discharging Modeling of EVs by Location
#### 2.1.1 Probability Density Function of EV Discharge Start Time at the Workplace  
First, it was assumed that the discharge probability density function during EV work was an uncontrolled mode calculated by subtracting the difference between the hourly outflow traffic volume and the inflow traffic volume in downtown Seoul, considering a driving time of 1 h in the morning (Equation (1)).  
Second, in the case of the controlled mode, in order to minimize the effect on the system, it was calculated as in Equation (2) by considering a driving time of 1 h and applying the difference between the hourly inflow and outflow traffic in the city center and the SMP rate system. Here, the probability density function of the discharge start time at the workplace was calculated based on Equation (1), since it was assumed that the EV entering the city was used for driving to the workplace.  
Therefore, it was assumed that these EVs started discharging after a certain time (e.g., 1 h) after entering the city. The probability density function at the start time of attendance in the controlled mode was calculated based on Equation (2) because the higher the SMP rate, the higher the probability that the EV would be discharged after going to work. In Equations (1) and (2), whole vehicle(t) means the entire running vehicle at time \(t\), and \(EVs_{outflow}(t)\) and \(EVs_{inflow}(t)\) are, respectively, the outflow and the inflow vehicles in the city center by time zone at the actual operation time t. SMP(t) represents the SMP rate at time t.  
**Equation (1)**: Probability of discharge start time at work (uncontrolled mode):  
\[P_{uw}(t)=\frac{\left(EVs_{inflow}(t)-EVs_{outflow}(t)\right)}{whole\ EVs} \tag{1}\]  
**Equation (2)**: Probability of discharge start time at work (controlled mode):  
\[P_{cm}(t)=\frac{\left(EVs_{inflow}(t)-EVs_{outflow}(t)\right)}{whole\ EVs}\times SMP(t) \tag{2}\]  
Figure 2 shows the estimated emission probability density in the uncontrolled mode at the workplace, considering the difference in traffic volume going to and from the city center. In addition, Figure 2 shows the discharge probability density results, including SMP rates for comparison with probability density using simple traffic modeling. Unlike Figures 2 and 3 presents the discharge probability density in the controlled mode in which the SMP rate is greatly adjusted during peak times. That is, as the SMP rate increases in the afternoon grid peak time period from 9:00 a.m., it can be seen that the discharge probability density increases relatively during the same time period. As shown in Figure 2, when the SMP\({}_{2}\) rate is applied rather than the SMP\({}_{1}\) rate during the same time period, the discharge probability density is high during the grid peak time period, and it can be seen that the grid peak is reduced the most.
## Framework for Proposed TER
### Trust Judging and Update Rules  
In our scheme, we choose the threshold level to be 0.5, and intermediate nodes actions with respect to this threshold are given in Table 2. We can use a high threshold value if the system is sensitive to security requirements.  
* If the belief of neighbor vehicle \(x\) in \(z\) is greater than the given threshold, then it will trust vehicle \(z\) and vise versa.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Standard On-Demand Routing** & **Trust Enhanced Routing** \\ \hline Sending node IP address & Receiving node IP address \\ Sending node seq number & Receiving node seq number \\... &... \\ hop count & same \\... &... \\ Expine time & Expine time \\... & Positive evidence (trust field) \\... & Negative evidence (trust field) \\... & Opinion metric \\... & Trust update \\ \hline \hline \end{tabular}
\end{table}
Table 1: Trust field extension added in proposed TER.  
* If the uncertainty of vehicle \(x\) in vehicle \(z\) is greater than 0.5, \(x\) ask for a digital signature verification from vehicle \(z\) and waits for it. If \(x\) successfully verifies \(z\)'s signature then \(x\) will start communicating with vehicle \(z\).
* If two neighbor vehicles had positive interaction, then the trust repository is updated by increment the trust in the correspondent vehicle, and the inverse is true also.
* If two vehicles have any uncertainty between each other, then we use \(\mathbf{w}_{z}^{x}=(0,0,0,u)\). Here, \(u\) is the maximum uncertainty from vehicle \(x\) to vehicle \(z\). It is because the two vehicles never interacted by the given expiry time of 5 s.
## 3 Problem Description and Computational Setup  
Figure 4(a) shows the geometry of the thread-like hole with a helical angle of 45\({}^{\circ}\), and the jet including the swirl component is referred to as SIJ. The swirling impinging jet issuing from the 45\({}^{\circ}\) spiral hole is a weak swirling flow with a swirling number of about 0.18 according to the computed result in ref. [13]. When the groove angle is 0\({}^{\circ}\), the flow grooves are straight, and the jet is called a multi-channel impinging jet (MCIJ) with a non-swirling case. In ref. [13], our team found that all the values of \(Nu(r)\) for SIJ (\(\theta\) > 0) with a high heat transfer rate are bigger than those for MCIJ (\(\theta\) = 0) in the region of \(r/dj\) \(\leq\) 0.5  
Figure 4: Radial distribution of \(Nu\) at the flat target wall.  
Figure 3: The ratio of the sub-grid viscosity to the molecular viscosity at \(r/D\) = 0 and \(r/D\) = 1; (**a**) \(r/D\) = 0; (**b**) \(r/D\) = 1.  
at the same cases. The new proposed nozzle to excite the swirling flow can enhance heat transfer radial uniformity compared to the MCIJ. In ref. [26], the experimental data shows that heat transfer on the flat target surface for the new nozzle with \(\theta=45^{\circ}\) can be enhanced on the whole target surface compared with the MCIJ and CIJ. For the MCIJ, the flow feature is different for that of the CIJ, and the heat transfer on the flat target surface is comparable to that of the SIJ [25; 26]. When the target surface becomes concave, the jet flow field for the MCIJ and SIJ is more complex, and heat transfer on a semi-cylinder concave target for these new nozzles may be more interesting. Herein, the groove angles of \(0^{\circ}\)and \(45^{\circ}\)are chosen to study the flow and heat transfer characteristics of the non-swirling and swirling impinging jets on a concave target.  
Figure 5(b) shows the geometry of the computational domains. The inner diameter of the semi-cylinder concave target is 27 mm with a radial length of 84 mm. Figure 5(c) shows the multi-block structured grid model, and to ensure high quality and consistency, the grid setting is the same as that of Figure 1(b). The size of the first layer near the wall is 0.075 mm with a grid growth rate of 1.07, and the value of the \(y+\) function is also about 0.5. The result  
Figure 5: Geometrical structure and computational domains.  
of the grid-independent tests shows that the suitable grid density is about 12.86 million cells with a maximum cell feature length of 0.3 mm.  
The computational setup for the jets on the concave target remained fairly consistent with the above verified case for the jets on the flat target mentioned in Section 2.2. The difference is just the mean velocity profile at the nozzle inflow, which is specified based on the given \(Re=16\),000 with an equivalent diameter of 6 mm. Related to the inflow velocity and the equivalent diameter, the non-dimensional time step is changed to 5.76 \(\times\) 10\({}^{-4}\) to keep the Courant number less than 1. Similarly, the statistics are calculated as the same as the above verified case.
## 1 Introduction  
Over the years, as the concept of Smart Grids slowly became one of the highly discussed topics, certain needs and expectations on the whole meaning of such networks emerged. The whole idea of the concept is built up on a communication network that would allow a large number of connected nodes in various topologies to communicate and transmit important data about the power grid. This, via usage of devices on all structures of the power grid, should lead to a substantial improvement in stability, effectivity and higher safety of power generation, transmission and distribution processes [1; 2; 3]. The concept also counts on the involvement of renewable energy sources in the power grid and higher level of energy demand management. Thus, the concept of Smart Grids is tightly associated with smart metering and the possibility of controlling different parts of the power grid as well [4].  
For enabling the possibility of measuring and controlling key points, a reliable bidirectional communication infrastructure has to be built along the important devices deployed on the power grid. Controlling the key points would allow the network operational centers to operate circuit breakers, indicate fault detection and could lead to better detection of unequal phase loads. Besides well-known,highly reliable technologies such as optical networks or microwave point-to-point links [5], other types of technologies are being deployed. Therefore, the communication network has a heterogeneous character [6; 7]. In remote areas, where the whole process of building up optical networks might not be cost-effective, alternative communication technologies are being used. Usually, cellular network communication technologies, or enabling communication on already established power lines by the technology called PLC (Powerline Communication), are used [8; 9].  
Powerline communication technologies are usually divided into two groups based on their operating frequencies [10]:  
* Narrowband PLC (NB-PLC) operating in the frequency range 3-500 kHz, using ARIB, FCC or CENELEC bands.
* Broadband PLC (BB-PLC) or Broadband over Power Lines (BPL) operating in the frequency range from 1.8 MHz up to 80 MHz. Bands are not strictly divided and are usually based on recommendations of respective standards and specifications--IEEE 1901, Homeplug specifications or G.hn.  
Especially with demands of a sufficient level of cybersecurity, extra overhead caused by the securing mechanism should be expected. Thus, implementing technologies with sufficient throughput is vital [11]. Adding features such as tunneling also causes significant overhead, which should be taken into account when planning Smart Grid infrastructures [12].
## 5 Experimental Results
### Evaluation of the CGFace, IF-CGFace Model on the PCGAN Dataset
#### 5.4.1 Balanced Scenario  
Firstly, the output feature map was extracted from the last hidden layer of the model of the fold 4 (PCGAN) from previous experiment. Next, the softmax classifier was replaced by the AdaBoost and XGB classifiers. The performance results of our CGFace model compared with ADA-CGFace, XGB-CGFace, and VGG16 (different classifiers and feature types) are shown in Table 3. We quickly notice that the CGFace model achieves 98% accuracy, whereas the AUC is only 81%. In contrast, although the classification accuracy of IF-CGFace models is similar to that of CGFace, XGB-CGFace yields AUC at 84.2%, while ADA-CGFace achieves 89.4%, outperforming CGFace. The result suggests that batch normalization and dropout layers which were placed after each convolution layer can reduce not only overfitting data during training, but also increase the performance of computer-generated face detection.
# ICRP: Internet-Friendly Cryptographic Relay-Detection Protocol
## 5 Performances
### Analysis
#### 5.2.3 Volume  
We quantify the volume of overhead data added through a \((n,p,k)\)-sending in Active Mode in comparison with the amount of raw information transmitted \((n\cdot p\cdot k\) bytes).  
1. Each message (resp. response) is marked with a bit \(s_{i}\) (resp. \(r_{i}\)). This gives an additional \(2\cdot n\cdot k\) bits of information traveling through the network.
2. Each message (resp. response) is complemented with a sequence number encoded onto a 4 bytes header. This adds another \(64\cdot n\cdot k\) bits of additional data traveling.
3. During the verification part of our prototype, a \(RSA2048\) signature is sent for each session, with an additional 2 bytes tag indicating the current session number. This adds another \((2048+16)\cdot k\) additional bits.  
Overall, the total overhead of our protocol is \((66n+2064)k\) bits. The proportion of additional data traveling through the network is :  
\[\frac{(66n+2064)k}{8 npk}=\frac{66n+2064}{8 np}\]  
This proportion is unrelated to the number of sessions \(k\). Table 4 displays the overhead proportion for a few practical values of \(n\) and \(p\).  
It appears that increasing the values of \(n\) and \(p\) lowers the overhead proportion, but, as stated before, it is preferable to keep the number of round \(n\) below 512. Otherwise, the decision process would be too rarely applied. It is also advised to restrain the size \(p\) of individual packets to avoid too frequent packet loss.
## 2 Overall System  
To improve the curative effectiveness of treatments using non-invasive brain stimulation, the target locations should be stimulated accurately and repetitively. Furthermore, the determined relative posture between the stimulator and the head should be maintained during the treatment which usually lasts about 40 min [3]. Unpredictable motions of patients should be compensated for in real time to maintain the stimulation accuracy during treatment. In addition, it is important to save disease- and patient-specific target locations because these areas vary across diseases and individuals. The automatic relocation function helps achieving consistent performance in every treatment and reduces the preparation time for treatments.  
To satisfy these requirements, the proposed system was designed with a spherical robotic positioning device and a neuro-navigation system, as shown in Figure 1. Compared to the conventional manual positioning method, a robotic positioning device offers various advantages, such as precise positioning, good repeatability, and compensation of patient movements. The neuro-navigation system not only shows the current location of the stimulator with respect to the patient's head but also works as a graphical user interface (GUI) for controlling the positioning robot. For examples, the target stimulation spot is set on this GUI.
## 2 System and Channel Model
### Joint Impact of Pathloss and Generalized Pointing Errors with Nonzero Boresight  
By using Equations (2), (3) and (9) and applying a random variable (RV) transformation [33], the joint PDF of the random variable \(hh_{m}\) is obtained as:  
\[f_{h_{m}}(h_{m})=\xi_{m}^{2}(A_{0,m}g_{m})^{-\xi_{m}^{2}}h_{l,m}^{-2\xi_{m}^ {2}}h_{m}^{\xi_{m}^{2}-1},\ 0\leq h_{m}\leq A_{0,m}g_{m}h_{l,m}, \tag{11}\]  
Furthermore, taking into consideration Equations (1)-(3), the instantaneous electrical SNR, \(\gamma_{m}\), can be obtained as:  
\[\gamma_{m}=\frac{\eta_{m}^{2}h_{m}^{2}P_{s}}{N_{0}}=\frac{\eta_{m}^{2}h_{l,m} ^{4}h_{p,m}^{2}P_{s}}{N_{0}}, \tag{12}\]  
where \(P_{s}\) and \(N_{0}\) stand for the signal and noise optical PSD, respectively, [25].  
Thus, by considering Equation (12), the average electrical SNR, \(\rho_{m}=E[\gamma_{m}]\), is obtained as [19]:  
\[\rho_{m}=\frac{\eta_{m}^{2}E^{2}[h_{m}]P_{S}}{N_{0}}=\frac{\eta_{m}^{2}\xi_{ m}^{2}A_{0,m}^{2}g_{m}^{2}h_{l,m}^{4}P_{S}}{(\xi_{m}^{2}+2)N_{0}}. \tag{13}\]