esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Vehicle-Bridge Coupling Vibration Characteristics of Curved Girder Bridges
## 3 Verification of Programs  
This module is mainly used to analyze vehicle-bridge coupling vibration, which can be utilized to analyze vehicle-bridge coupling vibration when a single type of vehicle passes through a bridge. It can also calculate the vehicle-bridge coupling vibration response of multiple routes and fleets with different speeds under different bridge surface irregularities. It can also perform vehicle-bridge coupling vibration analysis for one-way and two-way driving.  
A simple-supported curved box girder bridge was taken as the research object; the main parameters were as follows: calculated span \(L=24.0\) m, girder radius \(R=100.0\) m, elastic modulus \(E=3.250\)\(\times\)\(1010\) N/m\({}^{2}\), shear modulus \(G=1.350\)\(\times\)\(1010\) N/m\({}^{2}\), and density \(\rho=2600\) kg/m\({}^{3}\). The main girder's cross-section was a single box single chamber with a cross-sectional area of \(A=4.392\) m\({}^{2}\), a cross-sectional moment of inertia \(I_{y}=2.443\) m\({}^{4}\), \(I_{z}=20.721\) m\({}^{4}\), and a free torsional moment of inertia \(J=5.042\) m\({}^{4}\). The main girder section is shown in Figure 2.  
Assuming the spring-mass system as shown in Figure 3, the suspended mass of the moving mass block was \(M=102,041\) kg, the spring stiffness supporting the mass block was \(K=1.0\times 10^{10}\) kN\(\cdot\)m, and the spring-mass block travelled through the simple-supported girder bridge with a constant speed, the system speed was the \(V=36\) km/h. The displacement and acceleration time histories at the simple-supported bridge mid-span were calculated using this program's integral method, separation method, and finite element analysis software, as shown in Figures 4 and 5.  
Comparing the results in Figures 4 and 5, the overall and separate methods in the program Cmck in this article agreed well with the results calculated via the common FEA (Ansys 17.0) software, indicating that the calculation program based on the two methods of overall and separate v coupled vibration analysis can be used for vibration analysis of time-varying vehicle-bridge coupled systems.  
Figure 4: Comparison of the displacement time-history curves at mid-span.  
Figure 3: Uniform moving spring-mass system acting on a simply supported girder.  
Figure 2: (a) Cross section of the girder (unit: cm), (b) plan view of the curve bridge (unit: cm).
## 7 Open Issues and Opportunities for Future Research
### Data Collection in Future Networks  
Data-driven AI/ML-based cognition techniques form the foundation of intelligent management in future networks, which requires instrumentation in order to provide the necessary telemetry data to fuel the ML engines. Future networks will involve high data rates and large scale, which imply that unprecedented volumes of network telemetry data will be generated, collected, and processed. In addition, the high-performance and often mission-critical applications supported by future networks often require telemetry data to be highly precise and comprehensive. Such requirements may limit the ability to apply sampling as a technique on a large scale, compounding the scalability challenge as the telemetry data volume explodes.  
In order to address these challenges, it becomes imperative for the management architecture in future networks to support \"smart\" data collection that is able to obtain necessary data for analytics without generating vast volumes of raw data. This involves the development of functions that automatically adjust the resolution of data measurement based on the context and current conditions and allow data pre-processing in the managed network systems. Another possible research direction for tackling this challenging problem is to explore data analytics techniques that are able to support intelligent management without consuming a massive amount of raw data.
# A Survey of Detection and Mitigation for Fake Images on Social Media Platforms
## 6 Evaluation Parameters and Datasets
### Datasets
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**S. No.** & **Parameter Name** & **Formula** & **Description** \\ \hline
1 & Precision (P) & P = TP/(TP + FP) & Measure shown to correct model was in classifying positives. \\ \hline
2 & Recall (R) & R = TP/(TP + FN) & Measures how many positives are missed by model. Also called sensitivity or Total Positive Rate (TPR). \\ \hline
3 & Accuracy (A) & A = (TP + TN)/ & Measures how accurately model classifies correctly. \\ \hline
4 & F1 (F1) & F1 = 2(PXR)/(P + R) & Measures the harmonic mean of Precision and Recall. \\ \hline
5 & False-Positive Rate & FPR = FP/FP + TN & Measure how many negatives are classified as positive. \\ \hline
6 & False-Negative Rate & FNR = FN/TP + FN & Measures miss rate. \\ \hline
7 & Half-Total Error Rate (HTER) & HTER = FPR + FNR/2 & Average of FPR and FNR. \\ \hline
8 & ROC & Receiver Operating Characteristic plot is used to visualize the performance of a classifier. \\  & & It is a two-dimensional curve for depicting the system’s characteristics. \\ \hline
9 & AUROC & The area under ROC measures the entire area under the ROC curve. It is a total measure \\  & & of performance across all possible classification thresholds. \\ \hline
10 & mPA & Mean Average Precision is the average of AP. AP is the area under the \\  & & Precision-Recall curve. \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Evaluation parameters. are associated with 9404 fake and 6225 real tweets posted by 9025 and 5895 unique users, respectively;
* NIST Nimble: The Nimble 16 dataset has approximately 10,000 images with various types of tampering, including the images where anti-forensic algorithms were used to hide minor alterations;
* PGGAN: Consisting of 100K GAN-generated fake celebrity images at 1024 \(\times\) 1024 resolution;
* PHEME: The rumors and hard facts made on Twitter amid breaking news are collected in this dataset. It contains rumors related to nine events; each is annotated with its veracity value, true, false, or unverified. * Weibo: Comprises Sina Weibo data collected between 2012 and 2016 from the web and mobile platforms. The collection has domestic and international news.
# FAIDM for Medical Privacy Protection in 5G Telemedicine Systems
## 3 Proposed Scheme
\begin{table}
\begin{tabular}{c c} \hline
**Notations** & **Definitions** \\ \hline \(s_{0}\) & The secrete value of remote server node \(S\) \\ \(s_{i}\) & The secrete value of \(i\)th gateway node (\(GW_{i}\)) \\ \(s_{ij}\) & The secrete value of \(i\)th constrained node (\(CN_{ij}\)) \\ \(S_{i}\) & Private key of \(GW_{i}\) after registering to remote server node \\ \(S_{ij}\) & Private key of \(CN_{ij}\) after registering to \(GW_{i}\) \\ \(aS_{ij}\) & \(CN_{ij}\)/’s anonymous private key issued by \(GW_{i}\) \\ \(ID_{ij}\), \(ID_{ij}\), \(aID_{ij}\) & Identity of \(GW_{i}\), \(CN_{ij}\), and \(CN_{ij}\)/’s anonymous identity \\ \(Q_{0}\), \(Q_{i}\), \(Q_{ij}\), \(Q_{aID_{ij}}\) & Public parameters of generated by secrete values \\ \(sk_{GW_{i}\leftrightarrow CN_{ij}}\) & The session key of \(CN_{ij}\) and \(GW_{i}\) \\ \(H_{1}(.)\), \(H_{2}(.)\), \(H_{3}(.)\) & Collision-resistance one-way hash functions \\ \(h_{k}(.)\) & Collision-resistance secure one-way chaotic hash function using \(K\) as the key \\ \(E_{k}(.)\), \(D_{k}(.)\) & The symmetric encryption and decryption using \(k\) as the key \\ \(MAC_{GW_{i}}\), \(MAC_{CN_{\overline{q}}}\) & The message authentication code algorithm of \(GW_{i}\) and \(CN_{ij}\) \\ \(\mathit{Cert}_{HCA\to S}\) & The certification issued by healthcare certification authority to remote server node \(S\). \\ \(\mathit{Cert}_{S\to GW_{i}}\) & The certification issued by remote server node \(S\) to \(GW_{i}\) which is generate from \(\mathit{Cert}_{HCA\to S}\). \\ \(\mathit{Cert}_{GW_{i}\rightarrow CN_{ij}}\) & The certification issued by \(GW_{i}\) to \(CN_{ij}\) which is generate from \(\mathit{Cert}_{S\to GW_{i}}\). \\ \hline \end{tabular}
\end{table} 
Table 1: Notations of the proposed scheme.
## 2 Foundations on RISs  
This section introduces the fundamental concepts of RISs, such as their hardware architecture and operating principles. It also provides a basic description of an RIS-assisted communication system, including the end-to-end configuration and the channel model.
## 4 Experimental Results
### Experimental Results on CIFAR-10  
Table 1 illustrates the test results on the CIFAR-10 dataset. Specifically, using the ResNet-20 model, we achieved superior performance compared to the traditional Tucker decomposition technique, with a 3.53% increase in accuracy, while maintaining the same compression ratio of 2.6\(\times\). In addition, our method demonstrates a considerable performance improvement (0.93% higher accuracy) when compared to the Tensor Ring tensor technique [46], while maintaining the same compression ratio of 6.8\(\times\). Regarding ResNet-32, our approach outperforms the existing methods. When compared to PSTR-S, our method's trained model yields a 0.6% increase in accuracy while maintaining a better compression ratio of 2.8\(\times\). Furthermore, with the same compression ratio of 5.8\(\times\), we achieved a 0.58% increase in accuracy.
## 4 Proposed Implementation Techniques
### Optimizing GPU Memory Usage
#### 4.2.3 Memory Optimization Technique in CHAM and LEA  
In the case of CHAM, unlike AES, there is no table to copy; thus, when copying from CPU to GPU, only round key was copied to the GPU, and encryption was started. Because nonce is a random constant, only the constant value is transferred, not copied to the GPU. AES requires 44 32-bit round keys to encrypt, but CHAM has only 8 32-bit round keys required when using 128-bit plaintext and 128-bit key length. Eight round keys are used up to 112 rounds repeatedly, thus we can use them efficiently by assigning them to registers. By using the small round key size of CHAM, registers can be used as efficiently as possible.  
Since LEA does not use tables, efficient memory optimization is possible. Similar to CHAM, plaintext is stored in a register and used. However, unlike CHAM, the round key of LEA requires 144 32-bit values, so the round key is stored and used in shared memory. When copying the round key, as in AES, each thread in the warp is implemented to efficiently copy a contiguous memory area. LEA also synchronized after completing the round key copy.
# A Novel Approach to Short-Term Stock Price Movement Prediction using Transfer Learning
## 3 Preliminaries  
### Long Short Term Memory  
Long short-term memory (LSTM) is a specific recurrent neural network (RNN) architecture. It can learn temporal patterns from sequential data, and overcomes the vanishing gradient problem in a standard RNN [8]. Moreover, LSTMs have gating mechanisms to regulate the flow of information. The amount of information that will be retained is systemically determined at each time step. Therefore, LSTMs can memorize temporal patterns over a long time series. The architecture of an LSTM cell is illustrated in Figure 1, in which \(x_{t}\), \(h_{t}\), and \(c_{t}\) are defined as the input, the hidden state, and the cell state, respectively, at time \(t\). An LSTM cell has three gates: \(f_{t}\), \(i_{t}\), and \(o_{t}\), which are called the forget gate, the input gate, and the output gate, respectively. Note that \(\otimes\) is point-wise multiplication. _Sigmoid_ and _tanh_ activation functions are marked as _sigmoid_ and _tanh_, respectively. Here, the _tanh_ activation function is defined as \(\tanh(z)=\frac{2}{1+e^{-2z}}-1\). The _tanh_ function is used in order to update the hidden state. Moreover, the _sigmoid_ activation function is defined as \(\sigma(z)=(1+e^{-z})^{-1}\). The _sigmoid_ function helps form the output value in the range of \([0,1]\). If the output is close to 0, most information is lost. If the output value is close to 1, it means that more information is allowed.  
The calculations for the forget gate (\(f_{t}\)), the input gate (\(i_{t}\)), the output gate (\(o_{t}\)), the cell state (\(c_{t}\)) and the hidden state (\(h_{t}\)) are performed using the following formulas:  
\[f_{t}=\sigma(W_{f}*x_{t}+U_{f}*h_{t-1}+b_{f}) \tag{1}\]  
\[i_{t}=\sigma(W_{i}*x_{t}+U_{i}*h_{t-1}+b_{i}) \tag{2}\]  
\[o_{t}=\sigma(W_{o}*x_{t}+U_{o}*h_{t-1}+b_{o}) \tag{3}\]  
\[c_{t}=f_{t}*c_{t-1}+i_{t}*\tanh(W_{c}*x_{t}+U_{c}*h_{t-1}+b_{c}) \tag{4}\]  
\[h_{t}=o_{t}*\tanh(c_{t}) \tag{5}\]  
where \(W_{f}\), \(W_{i}\), \(W_{o}\), \(W_{c}\), \(U_{f}\), \(U_{i}\), \(U_{o}\), and \(U_{c}\) are weight matrices, and \(b_{f}\), \(b_{i}\), \(b_{o}\), and \(b_{c}\) are bias vectors.
_Applied_  
_sciences_  
_Article_  
**The Effect of the Precursor Solution's Pretreatment on the Properties and Microstructure of the SCS**  
**Final Nanomaterials**  
**[PERSON] 1,2, [PERSON] 1,3,4, [PERSON] 1 and [PERSON] 2,*(c)**  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Footnote 2: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Footnote 3: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Footnote 4: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Footnote  
+++ ==WARNING: Truncated because of repetitions==
6: [https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)  
++  
Solution combustion synthesis (SCS) is categorized as a soft-chemical method, and is a simple and cost-efficient approach yielding fine powders [8; 9]. It involves the self-sustained redox reaction of an oxidant with a fuel in the presence of metal cations [10; 11]. Generally, the oxidants are metal precursors (e.g., metal nitrates), and the fuel is any organic compound (e.g., urea, glycine, and citric acid) that is capable of forming complexes with metal ions [12]. The released heat from the exothermic reaction between the oxidants and the fuel fulfills the energy requirements for the formation of the final compounds [13]. The most significant advantage of the SCS method is its time and energy efficiency. Once the mixture reaches the necessary temperature, it ignites and the high self-generated energy converts the precursors into oxides and other compounds without demanding an external energy source. Conversely, other approaches based on metal hydroxide and/or alkoxide conversion are endothermic and thus require continuous external energy input [14].  
There are many parameters that influence the properties of the produced materials, such as the preheating temperature, the initial composition, the fuel nature, the fuel concentration, etc. [15; 16; 17]. One of the parameters that have been considered to have low or no effect on the composition of the SCS-derived products is the quantity of water added in the initial solution. [PERSON] et al. [18] conducted a thorough investigation concerning this parameter, and it was concluded that the quantity of water in the precursor SCS solution does affect the composition and the microstructure of the final nanopowders. As a continuation of that work, in this research we demonstrate the influence of pretreatment of the precursor solution, performing heating on a plate up to 70 \({}^{\circ}\)C with mild magnetic stirring.
# Use of a Weighted ICP Algorithm to Precisely Determine USV Movement Parameters
###### Abstract  
The purpose of this article is to present a study aimed at developing a method for the precise determination of unmanned surface vehicle (USV) movement parameters (heading (HDG), speed over ground (SOG) and rate of turn (ROT)) through appropriate processing. The technique employs a modified weighted ICP (Iterative Closest Point) algorithm and a 2D points layer arranged in the horizon plane obtained from measurements. This is performed with the help of Light Detection and Ranging (LIDAR). A new method of weighting is presented. It is based on a mean error in a given direction and the results of modified weighted ICP tests carried out on the basis of field measurement data. The first part of the paper characterizes LIDAR measuring errors and indicates the possibilities for their use in matching point clouds. The second part of the article deals with a method for determining the SOG and course over ground (COG), based on a modified weighted ICP algorithm. The main part of the paper reviews a test method aimed at evaluating the accuracy of determining the SOG and COG by the scan-matching method using a modified weighted ICP algorithm. The final part presents an analysis comparing the obtained SOG and COG results with reference results of GNSS RTK measurements and the resulting generalised conclusions.  
ICP; mean error ellipse; precise maneuvering; movement parameters +
Footnote †: journal: Applied Sciences
# Doppler Shift Tolerance of Typical Pseudorandom Binary Sequences in PMCW Radar
## 4 Comparative Performance Analysis
### Evaluation of Doppler Shift Distortion of Range Profiles
#### 4.2.4 Additional Remarks  
Finally, it is worth highlighting that, although the PSLR and ISLR enable an objective comparison among the considered PRBSs, which is the aim of this article, they must be analyzed with care when assessing the overall quality of obtained range profiles. Among the reasons for this are (i) the fact that the nearest sidelobe to the mainlobe dominates the PSLR performance for most considered PRBSs and sequence lengths \(N_{\text{PRBS}}\) as discussed in Section 4.2.2, and (ii) the fact that most of the integrated sidelobe level used for ISLR calculation is concentrated around the mainlobe. The aforementioned factors may result in the masking of a significant dynamic range reduction at some regions of the PACF by the PSLR and ISLR metrics. This can be clearly seen, e.g., when observing the Doppler-shift degraded oversampled PACF for Gold sequences in Figure 6d compared to its original form in Figure 6c, which is not reflected by the nearly constant PSLR in Figures 9 and 10 nor by the ISLR degradation of around 3.5 dB in Figures 11 and 12. With the use of range windowing functions, which are commonly used in practice but not considered in this article for the sake of conciseness, both PSLR and ISLR metrics tend to better reflect changes in the dynamic range.
# IDEINFO: An Improved Vector-Weighted Optimization Algorithm
## 4 Discussion
### Wilcoxon Rank Sum Test
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**F** & **Index** & **INFO** & **SSA** & **GWO** & **WOA** & **INFO1** & **INFO2** \\ \hline \multirow{2}{*}{F1} & P & 6.3864\(\times 10^{-5}\) & 0.00023125 & 6.3864\(\times 10^{-5}\) & 6.3864\(\times 10^{-5}\) & 6.3864\(\times 10^{-5}\) & NaN \\ \cline{2-9}  & R & + & + & + & + & + & = \\ \hline \multirow{2}{*}{F2} & P & 6.3864\(\times 10^{-5}\) & 0.00075118 & 6.3864\(\times 10^{-5}\) & 6.3864\(\times 10^{-5}\) & 6.3864\(\times 10^{-5}\) & NaN \\ \cline{2-9}  & R & + & + & + & + & + & = \\ \hline \multirow{2}{*}{F3} & P & 6.3864\(\times 10^{-5}\) & 6.3864\(\times 10^{-5}\) & 6.3864\(\times 10^{-5}\) & 6.3864\(\times 10^{-5}\) & 6.3864\(\times 10^{-5}\) & NaN \\ \cline{2-9}  & R & + & + & + & + & + & + & = \\ \hline \multirow{2}{*}{F4} & P & 0.00018267 & 0.00018267 & 0.00018267 & 0.00018267 & 0.00018267 & 0.73373 \\ \cline{2-9}  & R & + & + & + & + & + & + & = \\ \hline \multirow{2}{*}{F5} & P & 0.00018165 & 0.0018165 & 0.00018165 & 0.00018165 & 0.00018165 & 1 \\ \cline{2-9}  & R & + & + & + & + & + & + & = \\ \hline \multirow{2}{*}{F6} & P & 0.00018267 & 0.001008 & 0.00018267 & 0.00018267 & 0.00018267 & 0.57075 \\ \cline{2-9}  & R & + & + & + & + & + & + & = \\ \hline \multirow{2}{*}{F7} & P & 0.00018165 & 0.14047 & 0.00018267 & 0.79134 & 0.00018165 & 0.47268 \\ \cline{2-9}  & R & + & - & + & - & + & + & - \\ \hline \multirow{2}{*}{F8} & P & 0.00018165 & NaN & \(4.0402\times 10^{-5}\) & 0.00018923 & 0.00018165 & NaN \\ \cline{2-9}  & R & + & = & + & + & + & + & = \\ \hline \multirow{2}{*}{F9} & P & 0.00018267 & 0.00018267 & 0.00018267 & 0.0018267 & 0.00018267 & 0.0018267 & 0.42736 \\ \cline{2-9}  & R & + & + & + & + & + & + & = \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Wilcoxon rank sum test results. As shown in Table 7, most of the \(p\)-values are less than 5%, and the overall performance of IDEINFO is statistically significantly different from the other six algorithms, thus indicating that IDEINFO has better performance than the other algorithms.
## 5 Analysis Results
### Effect of Drag Spring Pre-Tension Force  
Another parameter of drag springs is the pre-tensioned force P. The models with different pre-tensioned force P of drag springs as 3.5 N, 3.0 N, 2.5 N, 2.0 N, and 0.0 N are analyzed to study the influence on the structural dynamics of this reflector. The natural frequencies of each model are listed in Table 7.  
The decrease of natural frequencies is comparatively small with reducing pre-tensioned force. When the pre-tensioned force is set as P = 0.0 N in Model 10, the first frequency 0.3647 Hz is almost close to the first frequency 0.3331 Hz of Model 2 without the drag springs. But the frequency domain of Model 10 is much narrower than Model 1. It is indicated from another perspective that the existence of drag springs can develop the integrity of structural dynamics with extra drag spring stiffness. Moreover, the pre-tensioned force supplies the structure with the stress stiffness to further improve the whole structural stiffness.  
Hence, the function of drag spring stiffness is mainly to develop the structure integrity and narrow down the frequency domain. The function of pre-tensioned force is to totally improve the structure stiffness by extra stress stiffness. However, the pre-tensioned force in drag springs also acts as a driving force during the deployment [13]. Thus, the pre-tensioned force should be controlled at a feasible value as P = 3.5 N. The excessive pre-tensioned force may destroy the stability of deploying process.
## 2 Hybrid-Energy Micro Gird Modeling
### Storage Battery Modeling  
The battery is the common form of energy storage in a micro network, and battery energy has the characteristics of bidirectional flow and fast power response, which make it a good frequency adjustment source in a micro grid to provide real-time dynamic balance. It has a positive significance for improving the power quality of the micro grid and ensuring stable operation.  
This paper uses a lead-acid battery as the energy storage system. The battery charging state is an important technical parameter to reflect the remaining power. The value of SOC at time \(t+\Delta t\) is related to the value of SOC at time \(t\), and the charge and discharge power of the battery can be expressed as [21]:  
\[S_{t+\Delta t}=S_{t}+\frac{P_{bat-t}\Delta t}{C_{bat}} \tag{6}\]  
where \(S_{t}\) and \(S_{t}+\Delta t\) are the values of SOC at the moment of \(t\) and \(t+\Delta t\); \(P_{bat}-t\) is the battery power between \(t\) and \(t+\Delta t\); \(C_{bat}\) is the accumulator capacity.  
The battery duration will be consumed in the process of charging and discharging. Thus, the battery duration loss costs ought to be considered in addition to charging and discharging costs. Different depths of charging and discharging have different effects on battery duration. When considering the battery type, the battery duration loss cost in a charge and discharge process can be expressed as:  
\[C_{storage}=\frac{C_{init}}{N} \tag{7}\]  
The cycle times are related to the discharge depth of the lead-acid battery, which can be expressed as:  
\[N=a_{1}e^{a_{2}D_{N}}+a_{3}e^{a_{4}D_{4}} \tag{8}\]  
where \(N\) is the equivalent cycle number when the depth of discharge is \(D_{N}\); \(a_{1}\), \(a_{2}\), \(a_{3}\), \(a_{4}\) are correlation coefficient factors and are parameterized as \(-16.27\), \(2.679\), \(4110\) and \(-1.85\), respectively.