esa-sceva/satcom-chunk-collection huggingface dataset:
# A Robust Security Architecture for SDN-Based 5G Networks
## 5 Performance Evaluation
### IP Spoofing  
We first show the simulation results of our scheme under IP spoofing. For the sack of clarify, we term our scheme as _Robust_ in short. In our simulations, the attacker will eavesdrop the communication channel. Once he achieves a authentication request, it can get the IP of the devices and then generate his own attacks between two adjacent authentication requests from this devices. As we mentioned before, more attempts can give the attacker more opportunities to guess the right secret key. We vary the number of attacks from 0 to 500 and record the associated disconnect rate to study the feasibility of our scheme. The overall simulation result is shown in Figure 8, as we can see that, when the number of attacks reaches 500, the disconnect rate is lower than 0.1%. Then, we compare the performance of _Robust_ with two state-of-the-art ones. In Figure 9b, we can see that the disconnectrate of _Robust_ keeps increasing as the number of attacks increases. For the state of art \(TFSv1\) and \(SDSecurity\), the IP spoofing attack is generated by solely using the IP information while not generating the secret key used in our scheme. Taking the case of _moderate_ as the example in Figure 9b, we can see that the disconnect of our scheme is extremely low, in the worst case of 500 attacks, the maximum disconnect is still lower than 0.01%, which is much better than the disconnect rate from \(TFSv1\) and \(SDSecurity\). Taking a joint consideration of _sparse_, _moderate_ and _dense_, another important observation is that the amount of hosts will affect the performance of SDN architecture; \(SDSecurity\) experiences a very efficient loss as the number of hosts increases. On the contrary, the disconnect rate of _Robust_ does not vary a lot because the proposed scheme is a lightweight security architect, which fits the large scene very well. Both of the two observations indicate that the performance of the proposed scheme is much better than that from the state of the art under IP spoofing.  
Figure 8: Performance under IP spoofing.  
Figure 7: The simulation set up.  
Figure 9: Comparison under IP spoofing. (**a**) sparse; (**b**) moderate; (**c**) dense.
# A Real-Time Remote Respiration Measurement Method with Improved Robustness Based on a CNN Model
## 2 Related Works
### Photoplethysmography-Based Respiration Measurement Method
They proposed a method to measure respiration by applying frequency analysis technology and demonstrated notable performance in visible and infrared environments [47]. PPG-based noncontact respiration measurement methods were also studied. These promising methods can stabilize skin area tracking and continuously measure respiration through a combination of techniques for detecting the facial RoI as a representative skin area. Previous studies showed the usefulness of PPG-based respiration measurement techniques using camera systems. However, these methods still involve some limitations. First, they are difficult to apply in actualenvironments because most of the research results reported were recorded in environments such as laboratories and hospitals, where both movement and illumination are extremely limited. In fact, techniques such as BSS and frequency analysis methods used to purify respiration information from signals are extremely sensitive to noise; therefore, they cannot estimate respiration rate accurately in the presence of movement and optical noise unrelated to respiration [48]. In addition, [PERSON] et al. and [PERSON] et al. found that respiratory and heart rate signals were similar to PPG signals when respiratory rates were measured at high respiration rates greater than 20 bpm (bpm). They found that the accuracy was reduced because respiratory rate extraction was impossible owing to the overlapping periodicity [49,50]. Considering that these studies were conducted based on relatively stable PPG signals acquired with a finger in contact with a camera lens and that breathing rates as high as 40 bpm are generally considered normal, the probability of noise is very high. Thus, noncontact PPG-based respiration measurement methods still involve some notable limitations on their application as a general respiration measurement technology [47].
# Dynamic Quality Monitoring System to Assess the Quality of Asphal Concrete Payement
## 2 Development of the Dynamic Monitoring System for Asphalt Concrete
### _Composition of Digital Monitoring System_
#### 2.2.5 Quality Control System for Asphalt Concrete Pavement Rolling  
The main equipment for the asphalt mixture rolling quality control system includes mobile station, temperature sensor and vibration sensor. The subsystem is based on RTK technology. According to setting the reference station and vehicle mobile station, the centimeter level positioning of the roller is achieved by using differential positioning and triangle positioning principles. This enables a continuous collection of data on rolling times, rolling speed and travel direction. The system's positioning accuracy is 1-3 cm in the horizontal direction and 2-5 cm in the vertical direction. The infrared temperature sensor and acceleration sensor are used to collect the rolling temperature, roller vibration frequency and compaction meter value data. The data are transmitted to the remote central server in real time through TCP/IP protocol. Using the visual operating system in the cab  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Main Construction Machinery** & **Monitoring Indicators** \\ \hline \multirow{2}{*}{Mixing plant} & Asphalt aggregate ration, grading, asphalt weight, mixture temperature, mixing time, silo weightand discharge temperature. \\ \hline \multirow{2}{*}{Transport vehicle} & Loading time, loading place, transportation route, transportation time and transportation speed. \\ \hline \multirow{2}{*}{Paver} & Paving track, paving speed, paving time and asphalt temperature. \\ \hline \multirow{2}{*}{Roller} & Rolling speed, rolling track, rolling times, asphalt temperature and vibration frequency. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main monitoring indicators of digital monitoring system.  
of the roller, the rolling information is sent to the vehicle touch screen controller of the display and control terminal to feed back the rolling state information of asphalt mixture in real time. This approach helps to guide equipment operators to achieve the expected compaction state. The digitized asphalt pavement roller is shown in Figure 4.  
Figure 1: Structure diagram of the digital monitoring system for measuring asphalt pavement construction quality.  
Figure 2: Data acquisition equipment in control room of mixing plant.
## _Abstract_  
In this paper, a wireless sensor network (WSN) technology adapted to underground channel conditions is developed, which has important theoretical and practical value for safety monitoring in underground coal mines. According to the characteristics that the space, time and frequency resources of underground tunnel are open, it is proposed to constitute wireless sensor nodes based on multicarrier code division multiple access (MC-CDMA) to make full use of these resources. To improve the wireless transmission performance of source sensor nodes, it is also proposed to utilize cooperative sensors with good channel conditions from the sink node to assist source sensors with poor channel conditions. Moreover, the total power of the source sensor and its cooperative sensors is allocated on the basis of their channel conditions to increase the energy efficiency of the WSN. To solve the problem that multiple access interference (MAI) arises when multiple source sensors transmit monitoring information simultaneously, a kind of multi-sensor detection (MSD) algorithm with particle swarm optimization (PSO), namely D-PSO, is proposed for the time-frequency coded cooperative MC-CDMA WSN. Simulation results show that the average bit error rate (BER) performance of the proposed WSN in an underground coal mine is improved significantly by using wireless sensor nodes based on MC-CDMA, adopting time-frequency coded cooperative transmission and D-PSO algorithm with particle swarm optimization.  
keywords: underground coal mine; multicarrier code division multiple access (MC-CDMA); coded cooperation; multi-sensor detection; power allocation; particle swarm optimization
## 4 Discussion  
It is worth noting that, for the purpose of ensuring the visibility of all images in the experiments of this paper, the gain of the intensifier was adjusted according to the attenuation coefficient of the medium. The calibrated relative magnification of the image intensifier at different gains should be used to convert grayscale images into the energy of the photosensitive surface before comparison. On the other hand, since the weight of each photon packet was assigned to 1 at the initialization of the simulation, the corresponding energy could be obtained by counting the received effective photon packets and accumulating them. Comparing the simulation and experimental results of the two targets in different ALs, it can be seen in Figure 8 that as AL increased, the energy declined significantly. For convenience, the energy in each case was normalized to the value at AL of 0.42. Because the transceiver was separated to protect the image intensifier in the experimental setup, the overlap factor (O(z)) in the light detection and ranging (LiDAR) equation was less than 1, and considering the measurement error and systematic error in the experiment, theoretically the received energy in the simulation should be higher than the value in the experiment. In fact, this phenomenon is also observed in Figure 8, and it maintained a slight discrimination under different water conditions, which means that the results of IMC method are basically consistent with the experimental results.  
There are several studies on the underwater transmission MC model, among them [PERSON] et al. [42] developed a semi-analytic MC radiative transfer model to study laser propagation in sea water and obtained the chlorophyll \(\alpha\) concentration profile; the lookup table method has been shown to be appropriate for complex scattering phase function. Compared with the lidar experimental data of the Sanya Bay in 2018, the relative maximum error was about 40%, while the experiment proved that the relative maximum error of our IMC simulation results did not exceed 30%. In general, the results indicate that the IMC method is effective for researching laser transmission, target detection and imaging in complex underwater medium, and significantly improves the modeling accuracy.  
Figure 8: Normalized energy comparison of simulation and experimental images of (**a**) the ‘Diver’ target and (**b**) the ‘Double-Slit’ target.
# A Posteriori Detection of Numerical Locking in \(hpq\)-Adaptive Finite Element Analysis
## 5 Effectivity of the Method in Model Problems
### Numerical Examples
#### 5.2.3 A Quarter of a Bending-Dominated Shell
The locking has not been removed from the modified mesh, however the value of \(p=5\) has appeared sufficient for removal of the locking from the \(h\)-adapted (intermediate) mesh--the second section of the convergence curve is not horizontal. Figure 22: A quarter of a bending-dominated shell—\(hp\)-adapted (final) mesh. Note that the adaptations including the described mesh modifications, based on either the maximum (\(p=8\)) or optimized (\(p=5\)) values of the longitudinal order of approximation, may be performed automatically by the program, while the standard \(hp\) course has to be enforced by a user. Figure 23: A quarter of a bending-dominated shell—after simple detection and \(h\)-adaptation  
Figure 24: A quarter of a bending-dominated shell—mesh after optimized modification. As in the previous numerical examples, the absolute and relative error values, respectively, \(\log(U_{r}-U)\) and \((U_{r}-U)/U_{r}\), are presented in Table 3 versus the number \(N\) of degrees of freedom (dofs). For the points of three adaptive convergence curves, the corresponding mesh figure numbers are included in the table. Figure 26: A quarter of a bending-dominated shell—convergence for three adaptation cases. Figure 25: A quarter of a bending-dominated shell—after optimization and \(hp\)-adaptation. Discussion  
The anticipated shear-membrane locking has been detected in two adaptation modes including modification of the initial mesh. In the case of the standard _hp_-adaptation, the admissible error value has not been achieved. This is because the estimated error level and the discretization parameters in the adapted meshes have not been determined accurately due to the locking. In the adaptation with modification based on the maximum \(p\) possible, the admissible error value has not been achieved. This is because only further _h_-adaptation has been performed and further \(p\)-adaptation has not been possible as the maximum value of \(p=8\) has already been adopted. However, the admissible error value has been confidently achieved in the adaptation with the modification of the initial mesh based on the optimized value of \(p\).
## 3 Experimental Setup  
### Device Setup  
Figure 3 shows the experimental setup of the LS-based induction thermography system. The induction thermography system consists of the current excitation device, cooling device, copper coil, sliding guide rail, and PC. The IR camera was FLIR's SC645 model (FLIR Systems Inc., Wilsonville, OR, USA; un-cooled, 640 \(\times\) 480 pixels, 7.5\(\sim\)13 \(\mu\)m, 30 Hz). The distance between the IR camera and the specimen was set to 700 mm. The frequency of the excitation device is set to 27 kHz, and the intensity of the current is set to 135 A. The cooling device cools the heat generated by the coil. The coil is made of copper and is manufactured in a \"U\" shape. The movement range of the specimen is 1000 mm by the sliding guide rail, and it moves along the guide rail. The images acquired by the IR camera are transmitted to the PC, converted to Excel using the FLIR S/W (R&D suite 3.1), and then post-processed.
# Implementation of a Demand-Side Management Solution for South Korea's Demand Response Program
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Name** & \multicolumn{4}{c}{**Provincial Government**} \\ \hline
2017.06 & X & - & 3,858,630 & - & - \\ \hline
2017.07 & O & 3 h & 5,395,950 & 336,334 & 127.28\% \\ \hline
2017.08 & X & - & 4,930,560 & - & - \\ \hline
2017.09 & X & - & 3,694,440 & - & - \\ \hline
2017.10 & X & - & 1,142,180 & - & - \\ \hline
2017.11 & O & 1 h & 2,305,028 & 76,229 & 91.24\% \\ \hline
2017.12 & X & - & 4,697,140 & - & - \\ \hline
2018.01 & O & 9 h & 5,763,120 & 119,026 & 121.89\% \\ \hline
2018.02 & O & 1 h & 4,249,360 & 131,721 & 138.00\% \\ \hline
2018.03 & X & - & 3,519,930 & - & - \\ \hline
2018.04 & X & - & 1,245,460 & - & - \\ \hline
2018.05 & X & - & 1,036,460 & - & - \\ \hline
2018.06 & O & 2 h & 3,300,720 & 106,706 & 113.88\% \\ \hline
2018.07 & X & - & 5,803,640 & - & - \\ \hline
2018.08 & X & - & 5,354,860 & - & - \\ \hline
2018.09 & O & 1 h & 3,150,830 & 120,557 & 116.69\% \\ \hline
2018.10 & X & - & 1,003,480 & - & - \\ \hline
2018.11 & X & - & 2,717,800 & - & - \\ \hline  & Total & & 84,391,878 & 1,201,747 & - \\ \hline \multicolumn{4}{c}{**Name**} & \multicolumn{4}{c}{**G Store**} \\ \hline
2016.12 & O & 2 h & 610,614 & 21,309 & 95.73\% \\ \hline
2017.01 & X & - & 599,362 & - & - \\ \hline
2017.02 & X & - & 537,054 & - & - \\ \hline
2017.03 & O & 1 h & 446,284 & 10,547 & 93.41\% \\ \hline
2017.04 & X & - & 175,469 & - & - \\ \hline
2017.05 & X & - & 160,303 & - & - \\ \hline
2017.06 & X & - & 463,036 & - & - \\ \hline
2017.07 & O & 3 h & 647,514 & 36,711 & 170.65\% \\ \hline
2017.08 & X & - & 591,667 & - & - \\ \hline
2017.09 & X & - & 443,333 & - & - \\ \hline
2017.10 & X & - & 137,062 & - & - \\ \hline
2017.11 & O & 1 h & 262,012 & 8,323 & 82.96\% \\ \hline
2017.12 & X & - & 610,614 & - & - \\ \hline
2018.01 & O & 9 h & 599,362 & 11,935 & 122.22\% \\ \hline
2018.02 & O & 1 h & 537,054 & 10,158 & 106.42\% \\ \hline
2018.03 & X & - & 446,284 & - & - \\ \hline
2018.04 & X & - & 175,469 & - & - \\ \hline
2018.05 & X & - & 160,303 & - & - \\ \hline
2018.06 & O & 2 h & 463,036 & 10,515 & 112.22\% \\ \hline
2018.07 & X & - & 647,514 & - & - \\ \hline
2018.08 & X & - & 591,667 & - & - \\ \hline
2018.09 & O & 1 h & 443,333 & 10,113 & 97.89\% \\ \hline
2018.10 & X & - & 137,062 & - & - \\ \hline
2018.11 & X & - & 262,012 & - & - \\ \hline  & Total & & 10,147,42 & 119,611 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._To verify the capacity of the demand response from the customer, the request from KPX on 20 July 2017, 14:00-17:00, is displayed in Table 7. The large customer, N company, a big factory which has a contracted capacity of 10,000 kW delivered 97%, 93%, and 96% for each period and the average delivery rate was 95%. Figure 9 Illustrates the CBL load, the DR reduction result, and the delivery rate of each period. For example, Figure 9a shows the CBL is 18,059 kW, the real load is 8378 kW, the DR reduction is 9,681 kW, and the contracted capacity is 10,000 kW at 14:00-15:00.
# Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study
## 4 Evaluation
### Centralized Learning  
In a first experiment, we learned an autoencoder model with a configuration described in Section 3.1 on a single node, both with a Keras implementation on top of Tensorflow and with our Spring Boot application that uses Deeplearning4j[42]. Figure 6 gives an overview of the training process for 50 epochs, using 20% of the benign samples for validation to avoid overfitting. The duration of training on a single node was 57 min, or a little more than 1 min per epoch on average. The results show an accuracy of around 97% for both the training and validation phase, with a loss value that stabilizes around 5.08 \(\times\) 10\({}^{-3}\). Table 2 provides an overview of the reconstruction error distribution.  
Figure 5: Hybrid deployment of high-end devices and low-end device. From left to right: desktop, laptop, ODROID U3 mini-board, and NVIDIA Jetson TX2 embedded board.  
Figure 6: Centralized training of the autoencoder for 50 epochs on benign data only using 20% of the data for validation to avoid overfitting (X-axis depicts epochs, and Y-axis depicts accuracy and loss value on training and validation set).  
In principle, we use the maximum value in the above table to define a threshold (e.g., 1.8 \(\times\) 10\({}^{-1}\) for the reconstruction error to distinguish benign from anomalous behavior, with the assumption that benign traffic has a lower reconstruction error and the malicious traffic a higher reconstruction error. However, setting this value too high may lead to some malicious traffic going unnoticed, while setting it too low may flag benign traffic as anomalous. Setting a proper value for the threshold depends on whether one wants to minimize the false positives or the false negatives. This optimization concern is application specific and ideally the result of a broader hyperparameter search experiment to find the optimal neural network model and/or thresholds. However, this assessment falls beyond the scope of this work.
## 3 Results and Analysis
### Infiltration of AgW Materials  
The results of infiltrating W skeletons with different additives at 1050 \({}^{\circ}\)C for 150 min are presented in Figure 8.  
Figure 8a presents the infiltration with a 1% Cu addition (relative to the W powder mass). As shown, the surface contour of the W skeleton disc was clear, whereas the Ag compact on the W skeleton melted and formed liquid droplets during the infiltration  
Figure 8: Infiltration of Ag into the W skeleton: (**a**) 1% Cu; (**b**) Ni:Cu = 1:9, Ni:Cu = 1:1; (**c**) 1% Ni.  
process. However, the liquid Ag did not penetrate the W skeleton. Subsequently, upon cooling in the furnace, the liquid Ag solidified into spheres. This suggested that Ag did not infiltrate the W skeleton with the Cu addition. Figure 8b presents the infiltration with a 1% (Cu+Ni) addition (relative to the W powder mass). The results indicate that for the sample with Ni:Cu = 1:9, the surface Ag layer was relatively thick and convex, suggesting a relatively large wetting angle. This observation implied that during the infiltration process, Ag only partially infiltrated the W skeleton. In comparison, the sample with Ni:Cu = 1:1 had a convex surface with a smaller contact angle, indicating that Ag partially infiltrated the W skeleton, with a higher degree of infiltration compared with the Ni:Cu = 1:9 sample. Figure 8c presents the infiltration with 1% Ni addition (relative to the W powder mass). As shown, the surface of the sample was relatively flat, with no Ag on the side surface. However, both the upper and lower surfaces had solidified Ag structures. This indicated that Ag fully infiltrated the W skeleton.  
In summary, as the Ni content in the W skeleton increased, the Ag infiltration exhibited the following transition: no infiltration \(\rightarrow\) partial infiltration \(\rightarrow\) complete infiltration.
## 3 Problem Definition and Formulation
### Constraints
#### 3.2.2 Storage Resource Constraint  
Each node's storage resources are also subject to constraints.  
**Assumption 1**.: _Storage resources are limited and dynamically influenced by the total number of tasks being processed._  
The storage resource constraint for node \(i\) is given as  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Variable** & **Description** \\ \hline \(\mathbf{r}_{i}\) & Resource vector of satellite node \(i\). \\ \(c_{i}\) & Computational capacity of satellite node \(i\). \\ \(s_{i}\) & Storage capacity of satellite node \(i\). \\ \(b_{i}\) & Bandwidth of satellite node \(i\). \\ \(p_{i}\) & Processing capability coefficient of satellite node \(i\). \\ \(y_{i}\) & State of satellite node \(i\). \\ \(t_{j}^{c}\) & Computational requirement of task \(j\). \\ \(t_{j}^{s}\) & Storage requirement of task \(j\). \\ \(t_{j}^{b}\) & Bandwidth requirement of task \(j\), measured in Gbps. \\ \(\mathbf{X}\) & Task assignment matrix in the network, \(X_{ij}\in\{0,1\}\). \\ \(X_{ij}\) & Boolean variable indicating whether task \(j\) is offloaded to satellite node \(i\). \\ \(d_{\delta k}^{j}\) & Data transmitted from satellite node \(i\) to user terminal \(k\) for task \(j\). \\ \(\delta_{ik}^{j}\) & Transmission delay from satellite node \(i\) to user terminal \(k\) for task \(j\). \\ \(\mathbf{D}_{t}\) & Data flow, including transmitted data amount \(d_{ik}^{j}\) and task assignment matrix \(X_{ij}\). \\ \(L_{\max}\) & Maximum allowable transmission delay for user terminal. \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of variables and descriptions.  
\[\sum_{j=1}^{M}X_{ij}t_{j}^{s}\leq s_{i},\ \forall i \tag{7}\]  
This constraint ensures that the total storage resource usage at node \(i\) for task \(j\) does not exceed the storage capacity \(s_{i}\) of node \(i\), where \(t_{j}^{s}\) is the storage resource requirement of task \(j\), and \(s_{i}\) is the storage capacity of node \(i\).