esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Optimization of CL Conditions  
#### 3.1.1 Effect of Ni(IV) Concentration on CL Intensities  
The CL intensities, as functions of [Ni(IV)], [Lu], and [OH\({}^{-}\)] in both Ni(IV) and luminol solutions, flow rate, and characters of the carrier flow, have been determined and summarized. CL intensity  
Figure 3: Schematic of the flow injection analysis CL system for SAL/TEB determination, where P is the peristaltic pumps, V is the six-way injection valve, M is the manifold, CE is reaction cell, W is waste, and D is a photomultiplier tube detector.  
increased with the increasing flow rate, and a 2.5 mL/min flow rate was selected for all solutions in the measurements. Pure water was used as carrier flow.  
At constant [Lu], [OH\({}^{-}\)] in both Ni(IV) and luminol solutions, the influence of Ni(IV) concentration on CL emission intensities have been determined in the region of 2.0 \(\times\) 10\({}^{-8}\) M to 2.0 \(\times\) 10\({}^{-5}\) M. The CL relative intensities of TEB and SAL increased with the increasing Ni(IV) concentration, but higher concentrations of [Ni(IV)] lowered the CL intensity probably because of self-absorption by Ni(IV) complex anion. Maximum CL intensities were obtained for TEB and SAL. The optimum concentrations of Ni(IV) were thus selected as 6.0 \(\times\) 10\({}^{-7}\) M for TEB and 2.0 \(\times\) 10\({}^{-6}\) M for SAL.
# Use of Covariance Analysis in Electroencephalogram Reveals Abnormalities in Parkinson's Disease
###### Abstract  
Covariance analysis from wavelet data in electroencephalographic records (EEG) was, for the first time, applied in this study to unravel information contained in the standard EEG, which was previously not taken into consideration due to the mathematical models used. The methodology discussed here could be applied to any neurological condition, including the important early stages of neurodegenerative diseases. In this study, we analyzed EEG from control (CL) participants and participants with diagnosed Parkinson's disease (PD), who were age-matched women in an eyes-closed resting state, to test the model. PD is predicted to rise over the next decades as the population ages. Furthermore, women are more likely to undergo PD-related complications and worse disability than men. Two groups based on age were considered: under and over 60 years (PD patients <60 and >60; CL <60 and >60). Continuous Wavelet Transform and Cross Wavelet Transform were applied to determine patterns of global wavelet curves, main frequencies, and power analyses. Our results indicate that both CL age groups and PD patients <60 share a main \(\alpha\) brainwave and PD patients >60 showed a main \(\delta\) brainwave. Interestingly, power anomalies analyses show a decreasing anteroposterior gradient in CL, whereas it is increasing in PD patients, which was not previously observed. The brainwave power in PD patients <60 was higher in \(\theta\), \(\alpha\) and \(\beta\) waves and in >60 group, the \(\delta\), \(\theta\) and \(\beta\) brainwaves were predominant. This methodology offers a tool to reveal abnormal electrical brain activity unseen by a regular EEG analysis. The advent of new models that process EEG, such as the model proposed in this study, promotes renewed interest in electrophysiology of the brain to study the early stages of PD and improve understanding of the origin and progress of the disease.  
covariance; wavelet analyses; Parkinson's disease; electroencephalographic record (EEG); non-motor symptoms +
Footnote †: journal: Applied  
0021
# Outdoor Visible Light Communication in Intelligent Transportation Systems: Impact of Snow and Rain
## 2 System Model
### Atmospheric Turbulence Analysis
#### 2.4.3 Snow Attenuation  
The attenuation due to snow is larger than that due to rain for the same precipitation rate since the cross-sectional area of snowflakes is larger than raindrops. The attenuation, \(\gamma_{\text{snow}}\) (dB/km), as a function of snow precipitation intensity, S (mm/h), is given by [21].  
\[\gamma_{\text{snow}}=\mathbf{a}\cdot\mathbf{S^{b}} \tag{18}\]  
The parameters a and b are functions of the operating wavelength, \(\lambda\) (nm), and the estimated values for wet and dry snow are listed in Table 3[21].
# Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks
## 5 Performance Evaluation
### Comparison with State-of-the-Art Methods
#### 5.3.1 The Lifetime of the Network  
Figure 10(a) illustrates the lifetime comparison of the proposed model against the state-of-the-art clustering protocols like SEED, ABC, CEEC, and ZBR. Here, we discuss the lifetime of the network, which is defined as the time interval in which all the sensor nodes in the network drain their batteries. All the sensor nodes in the network of MOCHs, SEED, ABC, ZBR, and CEEC run out of batteries at about 4999, 3934, 2369, 1400, and 2954 rounds, respectively. The lifetime of the proposed model is approximately 1095, 2630, 3599, and 2045 greater than SEED, ABC, ZBR, and CEEC, respectively. The lifetime of the proposed model is approximately 22%, 53%, 72%, and 41% rounds greater than SEED, ABC, ZBR, and CEEC, respectively. The lifetime of MOCHs is 22% greater than SEED because the CH selection criterion is marginally better in SEED, which restricts the number of the cluster to a certain limit due to that no extra CHs are selected. In the clustering protocols, the good selection criterion for the CHs selection saves a lot of energy in the network. The cluster formation mechanism is distributed in three different zones, no extra time and energy are consumed for repeated CH selection and cluster formation. In MOCHs, the optimal CH selection is done by the BS which contains unlimited resources and helps to save the energy of the network. The cluster formation involves a lot of processing and consumes the energy of the network. In SEED this selection is completed by CH and consumes almost 20% of the network resources, which decreases the lifetime of SEED. The lifetime of ZBR is 72% lesser than the proposed model because in ZBR the multi-hop communication is used to forward the data from the CH to the BS. The BS is placed outside the sensing field in any arbitrary  
Figure 10: Analysis of random and optimal CHs selection on the lifetime of ZBR, SEED, and the proposed algorithm. (**a**) The effect of random and optimal CHs selection on the lifetime of MOCHs; (**b**) The effect of optimal CHs selection on the lifetime of ZBR, and SEED.  
place. The CHs closer to the BS relay the data of almost 3 back clusters. Consequently, the CHs closer to the BS run out of battery very quickly compared to the distant CHs which make the network unstable. Figure 11b depicts the network lifetime of MOCHs, SEED, ABC, CEEC, and ZBR with different network energies like 0.25 J, 0.75 J, and 1 J. We can see that the proposed models' performance also remains very good at different network energies. We also compare the lifetime of the network with varying the node distribution. The Tables 2 and 3 demonstrate the comparison of the lifetime of the network with \(N=200\) and \(N=300\). From the tables, we can see that the proposed model outperforms as compared to SEED, ABC, CEEC, and ZBR in all the distribution scenarios.
# Scene Description for Visually Impaired People with Multi-Label Convolutional SVM Networks
###### Abstract  
In this paper, we present a portable camera-based method for helping visually impaired (VI) people to recognize multiple objects in images. This method relies on a novel multi-label convolutional support vector machine (CSVM) network for coarse description of images. The core idea of CSVM is to use a set of linear SVMs as filter banks for feature map generation. During the training phase, the weights of the SVM filters are obtained using a forward-supervised learning strategy unlike the backpropagation algorithm used in standard convolutional neural networks (CNNs). To handle multi-label detection, we introduce a multi-branch CSVM architecture, where each branch will be used for detecting one object in the image. This architecture exploits the correlation between the objects present in the image by means of an opportune fusion mechanism of the intermediate outputs provided by the convolution layers of each branch. The high-level reasoning of the network is done through binary classification SVMs for predicting the presence/absence of objects in the image. The experiments obtained on two indoor datasets and one outdoor dataset acquired from a portable camera mounted on a lightweight shield worn by the user, and connected via a USB wire to a laptop processing unit are reported and discussed.  
Keywords:visually impaired (VI); computer vision; deep learning; multi-label convolutional support vector machine (M-CSVM) +
Footnote †: journal: Applied Sciences
## 4 Results and Analyses
### Pedestrian Data in Urban Canyon
The availability of the integrity information for the three filters discussed above is tabulated in Table 3. Since there are only a few epochs with PDOP higher than 10, the exclusion of those epochs did not change the availability values by more than 0.1%. Thus the tabulated availability values were computed without discarding those epochs. It can be observed that there is a slight improvement in terms of availability of GNSS integrity information with the VB filters as compared to the standard filter. The improvement could very well be magnified for a user navigating through GNSS challenged environments for a longer duration of time. Figure 11: Reliability of position solution (OEM6+IMU)—urban canyon. \begin{table}
\begin{tabular}{c c c c} \hline  & **Standard** & **VB** & **VB Adaptive** \\ \hline
**\% of Epochs with Integrity** & & & \\
**Information available** & & & \\ \hline \end{tabular}
\end{table} 
Table 3: Availability of GNSS integrity information (OEM6)—urban canyon.
## 3 Modeling of Laser Echo Signals Under Multi-Channel Continuous-Wave Lasers
Therefore, the photon collision probability is affected by the number of collisions, particle size, and relative spatial position of the smoke particles. Neglecting the relative  
Figure 4: Overlapping areas of 3D collision cross-section for the collision of two smoke particles. Figure 5: Multi-channel parallel laser coherent emission systems and target detection processes: (**a**) smoke-filled scene, (**b**) smoke-free scene. time delay of the emitted laser in each channel, the laser received power signal based on the particle collision angles can be summarized as  
\[S_{R}(t)=\sum_{i=1}^{N_{laser}}Amp_{i}(\theta_{ps1},\theta_{ps2},\theta_{ps}) \times P_{T}S_{T}(t-\tau_{Ni}) \tag{16}\]  
where \(Amp_{i}(\theta_{ps1},\theta_{ps2},\theta_{ps})\) is the amplitude coefficient of the laser received power signal. \(\tau_{Ni}\) is the delay time corresponding to each laser. Through the mixing of \(S_{T}(t)\) and \(S_{R}(t)\) signals to obtain the beat signal \(S_{B}(t)\), the frequency domain characteristics of the target detection echo signal and its pattern of variation can be extracted. On the basis of the triangular wave linear FM method, the instantaneous frequency \(f_{T}(t)\) of the laser power signal \(S_{T}(t)\) can be expressed as  
\[f_{T}(t)= \begin{cases}f_{0}-\dfrac{2B}{T_{m}}t+\dfrac{B}{2},t\in\left[0,\dfrac {T_{m}}{2}\right]\\ f_{0}+\dfrac{2B}{T_{m}}t+\dfrac{B}{2},t\in\left[-\dfrac{T_{m}}{2},0\right]\end{cases}  \tag{17}\]  
where \(f_{0}\) is the initial frequency; \(T_{m}\) is the modulation period; and \(B\) is the sweep bandwidth. \(f_{T}(t)\) is shown as a solid line in Figure 6.  
Beat frequency \(f_{B}(t)\) is expressed as  
\[f_{B}(t)=\left|f_{R}(t)-f_{T}(t)\right|= \begin{cases}-\dfrac{4B}{T_{m}}t+\dfrac {2B}{T_{m}}\tau,0\leqslant t\leqslant\dfrac{\tau}{2}\\ \dfrac{4B}{T_{m}}t-\dfrac{2B}{T_{m}}\tau,0\leqslant t\leqslant\dfrac{\tau}{2} \\ \dfrac{2B}{T_{m}}\tau,\tau\leqslant t\leqslant\dfrac{T_{m}}{2}\end{cases}  \tag{18}\]  
where \(\tau\) is the time delay of the laser echo signal compared to the emission signal, as shown by the red line in Figure 6.  
For laser proximity fuzes, it can be concluded that \(\tau\ll T_{m}\) and the beat frequency can be approximated as a constant. Thus, the relationship between the target distance \(R\) and beat frequency \(f_{B}\) is summarized as  
\[R=\dfrac{cT_{m}}{4B}f_{B} \tag{19}\]  
Figure 6: Transmission frequency, receiving frequency, and beat frequency based on triangular wave linear FM. By extracting the frequency domain characteristics and variation patterns of the target echo signal and the smoke echo signal, the target characteristics of the laser fuse not only reflect the 3D and spatial attributes inside the smoke cloud, but also provide a basis for researching the effects of parameter variations on target characteristics in Section 4.
# Vibration and Reliability Analysis of Non-Uniform Composite Beam under Random Load
## 3 Numerical Studies, Results and Discussion
### Vibration Responses and Reliability Analysis in Different Periods of Time  
The uncertainty of frequency and amplitude of random base motion excitation leads to different vibration responses and reliability results within different time periods. The maximum amplitude and strain of beam with \(b_{0}\) = 0.005 m, \(h_{0}\) = 0.001 m, \(n\) = 2, \(m\) = 2.5 and \(h\) = 1.5 in the time periods of 0-20 s, 20-40 s, 40-60 s, 60-80 s, 80-100 s, 100-120 s, 120-140 s, 140-160 s and 160-180 s are calculated, and vibration reliability in the time points of 20 s, 40 s, 60 s, 80 s, 100 s, 120 s, 140 s, 160 s and 180 s under the same random load with (\(A_{a}\sim N\big{(}50,10^{2}\)\(\mathrm{m/s^{2}}\) and \(\varphi\sim N\big{(}200,\big{(}200/15\big{)}^{2}\)\(\mathrm{rad/s}\)) were analyzed, respectively. The calculation results are shown in Figure 5.  
As can be seen from Figure 5, the maximum amplitude and strain of beam in different periods of time is different; because the reliability is not only related to the maximum strain in this time period but also to the number of loading cycles. The result shows that in the relatively short period of time (<120 s), the reliability may increase or decrease with the increment of the time period. However, when the period of time of analysis increases above a certain value (>120 s), the reliability shows a downward trend reaching to a converged value as a whole. The results of reliability under different time lengths are different, and the calculation time duration for reliability analysis should be according to the actual operation and measurement conditions.  
Figure 4: Influences of mean and variance of base motion excitation amplitude on vibration responses (maximum amplitude and strain) and reliability: (**a**) Influences of mean of base motion excitation amplitude on vibration responses and reliability; (**b**) Influences of variance of base motion excitation amplitude on vibration responses and reliability.
# Accurate Indoor Localization with IoT Devices and Advanced Fingerprinting Methods
## 4 Discussion
### Future Research Directions  
While our study has made considerable progress, there are several areas for future research. One potential direction is the integration of mobile phone motion sensors to further refine the localization process. These sensors can provide additional data to predict user movement, thereby increasing the accuracy of location estimates.  
Moreover, continuous improvement of the training data through user interaction can enhance the system's performance over time. By incorporating user-generated data into the training process, the system can adapt to changing environments and maintain high accuracy levels.  
Future research could also explore the scalability of our approach in larger and more diverse environments. Testing the algorithms in different types of buildings, such as industrial facilities and multi-story complexes, would provide valuable insights into the versatility and limitations of our methods.  
In conclusion, this study presents a significant step forward in the field of indoor localization using IoT and advanced fingerprinting methods. The improved accuracy and robustness of our proposed algorithms offer promising potential for a wide range of applications, setting the stage for further advancements in this critical area of research.
## 3 Quantitative Analysis of Support Effect of the Tunnel Face
### Strengthening Effect of Reserved Core Soil  
Figure 10 shows the 3D relationship between the stability coefficient (\(K\)), bottom width (\(b_{1}\)) and top height (\(h\)) of the reserved core soil. It can be seen that the stability coefficient presents typical spatial distribution characteristics when the reserved core soil is used to strengthen the tunnel face, and the projection of the stability coefficient on the horizontal plane is triangular with the variation range of 0.85\(\sim\)1.225. The stability coefficient can effectively be stabilized at 1.225 when the bottom width is 7.85 m and top height is 6.60 m, which is slightly larger than the critical stability coefficient.  
Figure 9: Relationships between stability coefficient (\(K\)) and cohesion (\(c_{g}\)) and grouting filling rate (\(\zeta\)): (a) 3D distribution cloud map, (b) Relationship between \(K\) and \(\zeta\), (c) Relationship between \(K\) and \(c_{g}\).  
Equation (25) shows that the supporting stress provided by reserved core soil has a linear relationship with bottom width and a cubic nonlinear relationship with top height, indicating that the influence of top height on the stability coefficient is significantly greater than that of bottom width. Relevant specifications reveal that the reserved area of core soil is generally not less than 50% of tunnel face [38; 39], but the reserved core soil size of the actual construction is often affected by the limitations of tunnel dimensions, cycle footage, self-stability and construction convenience. Therefore, a larger excavation height will reduce the ratio of the reserved core soil area to the excavated cross-sectional area when the tunnel is excavated by the full-section method, resulting in a limited strengthening effect. A larger height of core soil can be reserved for a better supporting effect when excavating with the step method, and this is consistent with the fact that reserved core soil is mostly used in step method construction [30; 38]. It can be seen from the above analysis that, on the premise of ensuring the stability of the reserved core soil and construction space, appropriately increasing the top height and bottom width can improve the stability of the tunnel face, and blindly increasing the reserved core soil dimensions will adversely affect the convenience of construction.
## 2 Related Works  
The practice of memory forensics technology started in 2005, and was launched by DFRWS for the Windows system memory forensics analysis challenge [7]. Afterward, memory forensics technology began to develop rapidly. [PERSON] proposed a pool tag search method to extract processes and threads from pool memory [8]; [PERSON] developed a method to reconstruct the VAD tree [9]; [PERSON] analyzed the PTE structure and proposed a virtual address to physical address translation method [10]. These studies lay the foundation for Windows memory forensics.  
In terms of the research on memory reconstruction, [PERSON] et al. proposed a method to reconstruct WinXP system memory based on the KPCR structure [11]; [PERSON] et al. improved the KPCR method and applied it to the Win7 system [12]. However, with the update of Windows versions, the methods based on pool tag scanning and KPCR scanning are difficult to generalize. To address this issue, [PERSON] et al. proposed a general Windows memory reconstruction method based on PDB [13], and the memory forensics framework Rekall implemented this method [14].  
After being able to reconstruct the basic Windows structure, researchers continue to expand data sources for memory forensics. [PERSON] proposed a method to extract network connections from the Windows heap [15]; [PERSON] et al. proposed a memory fragment file carving algorithm based on the reverse of the structure chain [16]; [PERSON] et al. proposed a stack trace method that does not rely on process debug symbols [17]. These examples of research on Windows memory structure reconstruction, including heap, stack, and other objects, analyze the relationship between Windows memory management objects, which is the foundation of detecting malware in memory.  
Currently, the injection page detection method based on memory forensics mainly focuses on VAD objects (e.g., the malfind plugin of Volatility and Rekall) to detect the label, private state, and protection attribute of VAD. However, the detection conditions of this method are too rough, and it cannot detect malicious code that modifies the VAD protection attributes. [PERSON] proposed a method to detect thread injection based on the call stack and developed the malthfind plugin [18]. [PERSON] et al. proposed a similar injection detection method based on call stack analysis, which can detect the injection code [5] that modifies the VAD protection attribute, but the hidden injection page that has not been executed cannot be detected based on the stack call.  
Considering the limitations of existing VAD and call stack-based detection methods, researchers turned to exploit the characteristics of physical memory pages. [PERSON] presented a method to match YARA signatures in logically discontinuous physical memory [19], but this method requires input target signature of malware; [PERSON] et al. [6] proposed to use the executable attribute of PTE as a feature of executable physical memory pages, but this method will report all modified memory maps, including many benign memory pages, so it cannot accurately locate malicious memory pages.
## 6 Discussion and Conclusions
### Conclusions  
In this work, a mainshock-integrated aftershock fragility function model is proposed to calculate the probability of exceeding a damage state of a structure subjected to M-A sequences. A pile foundation-supported bridge system is used to test the proposed fragility model numerically. The findings show that the traditional mainshock fragility model is no longer suitable to fit the fragility curves of a structure under M-A sequences if the fragility curve is conditional upon a specific mainshock intensity and varying aftershock intensities. The developed mainshock-integrated aftershock fragility function model fits the fragility curves well in this circumstance by adding a parameter \(\gamma_{m}\), which is a function of the mainshock of concern. With the developed fragility function model, the probability of exceeding a damage state of the bridge structures under the earthquake sequences can be calculated accurately. The findings also indicate that the M-A sequences have a great influence on the probability of collapse of the bridges modeled in this study. The probability of collapse has an initial value, which is a function of the mainshock intensity. It is observed that the initial values of the probability of collapse increase with the mainshock intensity. The proposed model can also be adapted to compute the reliability of other engineering structures under M-A sequences if the seismic hazards of both mainshocks and aftershocks at a given site are known.  
Conceptualization, X.G.; methodology, X.G., and Z.C.; investigation, X.G., and Z.Z.; resources, X.G.; data curation, X.G.; writing--original draft preparation, Z.Z., and X.G.; writing--review and editing, Z.C.; supervision, X.G.; project administration, X.G.; funding acquisition, X.G., and Z.C. All authors have read and agreed to the published version of the manuscript.  
The first author acknowledges the supports from NSFC with Grant Number 51708484, NSFJP with Grant Number BK20170511, and NSFJHEIC with Grant Number 17 KJB580010. Furthermore, the third author acknowledges the support of a University of Missouri Research Board Grant (\"Design-oriented Scoured Foundation Modelling for Bridge Performance Analysis\").  
The authors declare no conflict of interest.