esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Stability Analysis of New Scheme  
To test the absolute stability of the presented scheme (VRK3), we use the set of Equations (18) to derive the following:  
\[k_{1}=\lambda y_{n} \tag{23}\]  
\[k_{2}=\lambda y_{n}\bigg{(}1+\frac{h\lambda}{6}\bigg{)} \tag{24}\]  
\[k_{3}=\lambda y_{n}\bigg{(}1+\frac{11h\lambda}{4}+\frac{7h^{2}\lambda^{2}}{24} \bigg{)} \tag{25}\]  
By substituting Equations (23)-(25) into (17) and allowing \(z=h\lambda\), we obtain  
\[y_{n+1}=y_{n}\bigg{[}1+z+\frac{Z^{2}}{2}+\frac{z^{3}}{6}\bigg{]} \tag{26}\]  
Then, from Equation (26), the stability polynomial is  
\[R(z)=\frac{y_{n+1}}{y_{n}}=\bigg{[}1+z+\frac{Z^{2}}{2}+\frac{z^{3}}{6}\bigg{]} +o\Big{(}z^{4}\Big{)} \tag{27}\]  
Utilizing the MATLAB program, Figure 1 below graphically illustrates the absolute stability region of the Formula (27):
## 3 Results
### Sdn-Vanet QoS Framework Evaluation  
The performance evaluation of the SDN-VANET QoS Framework is carried out using Mininet-WiFi wireless network emulator [23], an OpenFlow-enabled network emulator forked from Mininet to add wireless channel emulation and mobility support. The SDN controller deployed is the open-source RYU for the justification given previously. In addition, the SUMO (Simulation of Urban MObility) [24] tool is used, which is an open-source, highly portable, microscopic and continuous multi-modal traffic simulation package--including road vehicles, public transport and pedestrians designed to handle large networks.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Training**} & \multicolumn{4}{c}{**Testing**} \\  & **Accuracy** & **Precision** & **Recall** & **F-Measure** & **Accuracy** & **Precision** & **Recall** & **F-Measure** \\ \hline Adaboost & 63.74\% & 81.47\% & 41.65\% & 55.12\% & 53.59\% & 67.19\% & 25.54\% & 37.01\% \\ Random Forest & 99.59\% & 99.58\% & 99.65\% & 99.61\% & 51.82\% & 56.28\% & 44.37\% & 49.62\% \\ SVC & 81.97\% & 86.55\% & 78.50\% & 82.33\% & 66.69\% & 61.21\% & 95.55\% & 74.62\% \\ Proposed Dense & 97.72\% & 97.82\% & 97.93\% & 97.87\% & 74.85\% & 72.92\% & 84.26\% & 78.18\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Generalized model results.  
Figure 5: Area under the curve and accuracy results of the proposed deep learning model.  
The performance evaluation is conducted using an urban congestion scenario having vehicular applications with heterogeneous bandwidth requirements. Our implementations consider three classes of application priority: 1, 2 and 3. Class 1 is devoted to safety applications with ultra-low delay requirements. For this class, we evaluate the impact of the usage of MEC on performance; one of the 5G features. Class 2 is devoted to infotainment application which is more delay tolerant. Class 3 is reserved for best-effort applications that don't have any priority or specific requirements. In our implementation environment, the controller has to delay the run of the adaptive component of the control application before recalculating the bandwidth assigned for each meter according to the association timeout occurring when the vehicle changes the RSUs.  
To assess the performance of the suggested SDN-VANET QoS Framework, the following performance metrics will be deployed: average throughput of data between vehicles and applications servers and average round trip time (RTT). The RTT can be defined as the time taken by the message sent by the source vehicle until receiving a response from the receiving application server.
## 4 Results of Dataset and Classification  
### Results of Constructed Dataset  
Figure 5 shows an example of the constructed dataset for this study. As in the principal component analysis (PCA), the statistical method did not work well because the total number of feature vectors was 43. Questionnaires for seven feature vectors give a subjective judgment. However, that provides preliminary evidence that the interview with self-care and regimen adherence is a reliable and valid instrument and efficiently assesses self-care behaviors associated with glycemic control. The used features for this study are related to physical activities in daily life. The remaining 36 feature vectors are related to physical fitness and gait characteristics under imposed challenged speed conditions in senior citizens with diabetes during walking. The dimension of the matrix is (human subjects \(\times\) feature vectors = 200 \(\times\) 43).  
Figure 6 shows the results of data distribution under the condition of three different walking speeds. Blue-colored data (mean \(\pm\) standard deviation (SD) = 0.898 \(\pm\) 0.155) represent the condition of slow speed, which becomes, on average, 22% slower walking speed than the preferred speed, and orange-colored data (1.157 \(\pm\) 0.219) represent the preferred condition. Finally, gray-colored data (1.441 \(\pm\) 0.275) represent the fast condition, which becomes the averagely 25% faster speed than the preferred, respectively.  
As a result, it was confirmed that there was no problem analyzing gait and physical fitness characteristics with the constructed dataset, although the experimental condition for walking speed was dependent on personal subjective criteria.
# Study on Seismic Isolation of Long Span Double Deck Steel Truss Continuous Grider Bridge
## 2 Structural Finite Element Model
### Seismic Dissipation and Isolation Device
#### 2.2.1 Friction Pendulum Bearing (FPB)  
A friction pendulum bearing (FPB) is composed of an upper base plate, base, ball pendulum, and ball crown lining plate. The upper base plate of the friction pendulum bearing is connected with the upper beam structure of the bridge, and the lower base is directly connected to the pier top. The sliding surface is a curved surface made of stainless steel, and the curvature radius of the stainless-steel curved surface is equal to that of the ball pendulum. Under a vertical force, the compressive stress of the curved surface is uniform. Isolation performance of the friction pendulum bearing (FPB) is related to the curvature radius and the friction coefficient of the sliding surface.  
Under an general external force, the friction pendulum bearing (FPB) does not produce an isolation performance. When a horizontal shear force is too large, the limit device of the bearing will be cut off, and the isolation performance of the friction pendulum bearing (FPB) will play out. After an earthquake, the friction pendulum bearing (FPB) can be reset automatically under a dead weight.  
The bilinear calculation model of friction pendulum bearing (FPB) is shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Order** & **Frequency Value** & **Vibration Characteristics** \\ \hline
1 & 0.455 & First-order vertical bending \\
2 & 0.635 & First-order transverse bending \\
3 & 0.704 & Second-order vertical bending \\
4 & 0.944 & Third-order vertical bending \\
5 & 1.006 & First-order torsional deformation \\
6 & 1.052 & Second-order transverse bending \\
7 & 1.308 & Fourth-order vertical bending \\
8 & 1.344 & Second-order torsional deformation \\
9 & 1.405 & Fifth-order vertical bending \\
10 & 1.521 & Third-order transverse bending \\ \hline \hline \end{tabular}
\end{table}
Table 1: The natural frequency value of each order.  
Figure 7: Front six modes. (**a**) First mode. (**b**) Second mode. (**c**) Third mode. (**d**) Fourth mode. (**e**) Fifth mode. (**f**) Sixth mode.  
In Figure 8, \(D_{y}\) is the yield displacement; \(D_{\text{d}}\) is the ultimate displacement; \(K_{1}\) is the initial stiffness of FPB; \(K_{2}\) is the swing stiffness of FPB.  
The equivalent stiffness and equivalent period of the system can be obtained by studying the nonlinear model of the FPB.  
\[K_{eff}=W/R+\mu*W/D \tag{1}\]  
\[T_{eff}=2\pi\sqrt{R*\text{d}/(dg+Rg\mu)} \tag{2}\]  
\[\beta_{eff}=\frac{2}{\pi}\frac{\mu}{D/R+\mu} \tag{3}\]  
where \(R\) is the curvature radius and \(u\) is the friction coefficient.
## 5 Discussions
### Open Issues  
In crises, effective power consumption management is crucial for IoT devices. Power and network transmission are key factors affecting such devices' functionalities and lifetimes. While these systems may function normally in pre-disaster phases, their effectiveness can be reduced during the disaster and recovery. To prevent such issues, IoT systems must be designed to function normally, even under low resources, such as bandwidth, power, or damaged sensor nodes. Adaptive task scheduling is a potential solution to extend power consumption. As highlighted in this research, 50% of power consumption comes from the sensing module and 40% from network transmission. Therefore, task scheduling should be dynamically adjusted based on the current situation. A low-power dynamic routing algorithm in wireless sensor networks can also help extend the sensor network's reach when the routing node fails. These concepts must be thoroughly studied and implemented to ensure practical application and sustainability in real-world scenarios.
# Steel Surface Defect Diagnostics Using Deep Convolutional Neural Network and Class Activation Map
## 2 Background
### Feature Extraction Methods
#### 2.1.2 Histogram of Oriented Gradients  
Histogram of oriented gradients (HOG) is the gradient-based feature descriptor for extracting the image features, generating the histograms based on gradient magnitude and direction [23]. The gradient magnitude and direction can be calculated via pixel values within an image, while edges and shapes in the image can be efficiently described by the gradient-based histograms. On account of being able to represent the image appearance, the HOG has been developed to outperform visual object detection analysis [24; 25]. The gradient magnitude \(g\) and the gradient direction \(\theta\) can be calculated using the gradients in two directions as follows.  
\[g=\sqrt{g_{x}^{2}+g_{y}^{2}} \tag{5}\]\[\theta=\arctan\frac{g_{y}}{g_{x}} \tag{6}\]  
The histogram of oriented gradients is formed as shown in Figure 1b. The gradient magnitudes and directions are calculated within the image, where the directions are the angles between \(0^{\circ}\) to \(180^{\circ}\). Those gradient directions compose user-specified bins of the histogram as a way of orientation binning, while the magnitudes are allocated to the bins. A series of the processes are conducted in all divided regions from the entire image, which are called cells. Accordingly, the gradient amplitudes of the bins locally provide feature representation for difference among the pixel intensity values and its orientation from the histograms.
###### Abstract  
The co-occurrence of cyanotoxins and taste-and-odor compounds are a growing concern for drinking water treatment plants (DWITPs) suffering cyanobacteria in water resources. The dissolved and cell-bound forms of three microccystin (MC) congeners (MC-LR, MC-RR and MC-YR) and four taste-and-odor compounds (geosmin, 2-methyl isoborneol, \(\beta\)-cyclocitral and \(\beta\)-ionone) were investigated monthly from August 2011 to July 2012 in the eastern drinking water source of Lake Chaohu. The total concentrations of microccystins and taste-and-odor compounds reached 8.86 \(\mathrm{\SIUnitSymbolMicro}\)/L and 250.7 \(\mathrm{\SIUnitSymbolMicro}\)/L, respectively. The seasonal trends of microccystins were not consistent with those of the taste-and-odor compounds, which were accompanied by dominant species _Microcystis_ and _Dolicosperm_. The fate of the cyanobacteria and metabolites were determined simultaneously after the processes of coagulation/flocculation, sedimentation, filtration and chlorination in the associated full-scale DWTP. The dissolved fractions with elevated concentrations were detected after some steps and the breakthrough of cyanobacteria and metabolites were even observed in finished water. Chlorophyll-\(a\) limits at intake were established for the drinking water source based on our investigation of multiple metabolites, seasonal variations and their elimination rates in the DWTP. Not only microccystins but also taste-and-odor compounds should be taken into account to guide the management in source water and in DWITPs.  
microcystis; taste-and-odor compounds; water source; drinking water treatment plant; cyanobacterial thresholds +
Footnote †: journal: Accepted: 28 December 2017; Published: 2 January 2018
## 3 Heterogeneous Systems: Electrochemical AOPs  
Electrochemical oxidation for wastewater treatment offers a range of advantages, being robust, reliable, and flexible [62; 63]. However, very few studies address the complete modeling of electrochemical oxidation in microreactors. [PERSON] et al. [46] modeled a coil-type electrocatalytic microreactor with a Ti-O-N anode and a titanium cathode, using the advection-convection equation and a detailed micro-kinetic model. Experimental results and numeric simulations for estimation of the kinetic parameters of phenol degradation were used to assess concentration gradients between the walls of the device at different radial gaps, showing that mass transfer limitations start to be significant for a gap larger than 200 \(\upmu\)m. [PERSON] et al. [50] presented an interesting comparison between detailed intrinsic kinetic modeling and pseudo-first order observed kinetics for the degradation of diuron, a toxic herbicide, in a microdevice. The microchannel had a rectangular cross-sectional area of 10 mm \(\times\) 250-750 \(\upmu\)m, depending on the experiment, and a length of 21 mm. The upper and lower walls consisted of two electrodes: a graphite anode and a stainless steel cathode. Degradation was mainly due to hydroxyl radical formation by water dissociation at the anode, under an applied current intensity of 1 mA. The Navier-Stokes equation, the continuity equation, and the advection-convection equation were implemented in the COMSOL platform and coupled with the intrinsic first-order kinetic rate law of the reaction occurring in the liquid phase. This model was used to fit experimental data for different residence times and the results were compared using a simple pseudo-first order model, ignoring mass transfer limitations. The rigorous model showed superior results, but the order of the two kinetic constants was comparable for a channel thickness of 250 \(\upmu\)m, demonstrating very little influence of mass transfer limitations in this configuration. As a result, the simplified model was used to estimate the influence of operating conditions such as pH and current intensity on the intrinsic kinetic constant. When increasing the channel thickness, the results showed poor predictions of the simplified model, demonstrating that in this case, diffusion of generated hydroxyl radicals into the bulk of the liquid phase has to be taken into account. Unfortunately, no validation of the prediction of the rigorous model was presented under these conditions.  
Further research in the field should address the use of microreactors for the investigation of electrochemical AOPs' intrinsic kinetics, validating the presented results in order to (_i_) define modeling protocols for kinetic estimation in the presence of mass transfer limitations and (_ii_) better define the design criteria to obtain mass transfer-independent parameters. Despite the great potentiality of deriving kinetic models for such systems, minimizing energy consumption and resource waste, electrochemical AOPs are still preferentially investigated using macroscopic lab-scale reactors.
## 3 Results
### Special Telecommunications Services  
In Figures 6-11 are shown the test results in the form of reproduced images from registered revealing emissions. The images contain characters of traditional and secure fonts from 12 points up to a size of 36 points. The recording of revealing emissions was made using two receivers, enabling the measurement of very weak electromagnetic emissions correlated with data processed electronically.  
It can also be observed that all the characters displayed are intelligible within the restored images, regardless of the font size (Figures 6, 7, 10 and 11). We can consider that the clarity of the recovered images from the compromising radiation corresponding to the Arial font is slightly better than that of the Times New Roman font. We continued with the display of secure asymmetrical and secure symmetrical fonts, choosing the same font size. The image recovery corresponding to these fonts can be seen in Figures 8 and 9.  
Figure 5: Reconstruction of the displayed image based on sensitive emissions measurement on frequency 840 MHz (\(BW\) = 50 MHz, the size of characters—24 points): (**a**) from the top—secure symmetrical font, secure asymmetrical font, Arial font, (**b**) from the top—secure symmetrical font, secure asymmetrical font, Times New Roman font.  
Figure 8: Image recovered when secure asymmetrical font was displayed—R&S FSET receiver.  
Figure 6: Image recovered when Arial font was displayed—R&S FSET receiver.  
Figure 7: Image recovered when Times New Roman font was displayed—R&S FSET receiver.  
Figure 9: Image recovered when secure symmetrical font was displayed—R&S FSET receiver.  
In Figures 10 and 11, we have illustrated the recovery image results obtained by using the FSWT receiver produced by the Rohde&Schwarz Company, which is the latest TEMPEST receiver model released on the market by the German company. This equipment comes with the raster module built in. It is true that, when using the FSET22 receiver, we allocated about 30 minutes to find the optimal video recovery parameters, while when using the FSWT receiver, we solved the same objective with five clicks. This is the feature of the German manufacturer, as well as the level of integration for the pre-selection filters. The only difference compared to the first part of the tests performed is that when using the FSWT receiver, we used a gain of 20 dB amplification. However, no significant improvement in the quality of the recovered image is observed and, thus, we can say that the effects of using TEMPEST fonts are the same, regardless of the reception equipment used in our tests.
# Research on a Wi-Fi RSSI Calibration Algorithm Based on WOA-BPNN for Indoor Positioning
## 3 Experiments and Results
### Hidden Layer Structure Setting
#### 3.3.2 Influence of Hidden Layer Structure on Average Positioning Error  
In this section, based on the five results of each BPNN model structure obtained in Section 3.3.1, we discuss the influence of the different BPNN model structures on the average positioning error results obtained after the calibration of the two test mobile terminals. The best positioning result of each BPNN model structure is shown in Figures 12 and 13.  
Figure 9: Distribution of test fingerprint points.  
Figure 11: Comparison of the average RSSI value after calibration of test mobile terminal 2.  
Figure 12: Comparison of average positioning error before and after calibration of test mobile terminal 1.  
Figure 10: Comparison of the average RSSI value after calibration of test mobile terminal 1.  
According to Figures 12 and 13, for the two test mobile terminals, under the condition of the same number of neurons in each hidden layer, whether the hidden layer of the BPNN model is a single hidden layer or double hidden layers, the average positioning error reaches the minimum value when the number of neurons in the hidden layer is six and five, respectively.  
It can be seen from the above results that more neurons in the hidden layer do not mean better WOA-BPNN calibration algorithm results. Therefore, based on the above results, and considering the problem of algorithm efficiency, we adopt the BPNN model with double hidden layers and six neurons in each hidden layer for the subsequent experiments.
# Evolution of Hybrid LiFi-WiFi Networks: A Survey
## 5 Handover and Load Balancing in HWLNets
### Hho  
Because of the physical property of light and technological ability of LiFi transmitters, AP covers up to three metres in diameter, thus making LiFi susceptible to frequent handovers even if the users move slowly. The data rate of LiFi is also affected by the PD in the user devices who can change orientation and affect the connectivity and in turn lead to handovers. In VLC, especially LiFi networks handover is a considerable factor in terms of QoS and user fairness. There are two major restrictions in distances between APs:  
* short distance leads to high spectral efficiency;
* longer distance decreases the number of handovers.  
A study was made by [9], in which the researcher proves that an ideal coverage for a single AP is two to eight m\({}^{2}\), taking into account the number of users and handover overhead. In this model, the overlap of light was pronounced due to the relatively close proximity of LiFi APs, and further study was carried out [57] that simulated the handover for non-overlapping APs. In this research, authors proposed the use of two handover schemes:  
* soft handover when distances between AP are great, so there is little or no overlapping. User equipment starts to communicate and establishes connection with the AP that has better QoS requirements before breaking connection with the old AP;
* hard handover for short AP distances. User equipment breaks connection with AP and only than establishes connection with the new AP.  
In another study [26], user equipment rotation and speed were analysed for handover, resulting in frequent handovers when equipment was angled between 60 and 80 degrees.  
The above-mentioned models represent stationary or slow-moving users. Handover rates of fast-moving users are extremely high, so a new model had to be made. One such is handover skipping [58] for unlinked APs. This novel topology-aware scheme proposes that a user should skip adjacent APs while moving in a predetermined vector. This can only be accomplished if a network controller has knowledge of the user's movement in such way that it can predict behaviour to an extent that a user will be fed by specific APs to diminish frequent handovers and to maintain a certain level of QoS. Such schemes relay on static network topology, which LiFi is anything but. A problem with positioning methods of users via GPS is that it is not pin point accurate, especially in buildings, which would in turn need a forecast model of sort that would rely on previous user movements combined with machine learning technology such as fuzzy logic. Further, sending this information back to APs is required, thus an RSS handover model was researched [59] based on the rate of change in RSS to determine a user's movement in relation to AP. This model uses average change rates in RSS to make handover possible since this method has no extra feedbacks and does not need to know network topology. This research showed that RSS handover can improve network data rates by about 30% over trajectory-based handover methods.