esa-sceva/satcom-chunk-collection huggingface dataset:
# Source Symbol Purging-Based Distributed Conditional Arithmetic Coding
## 4 Simulation Results
### Coding Complexity  
The decoding complexity of DAC increases with the maximum width \(M\) of the decoding tree, and the encoding complexity of DAC is linear, like a classical AC [9]. In the SPDCAC encoding process, \(1/n\) of the total number of the source symbols are purged from the source sequence, and the remaining source symbols are encoded using a conditional AC. Therefore, the encoding complexity of SPDCAC is proportional to \(O(\frac{n-1}{n}N)\), which is slightly lower than that of DAC and DJSCAC. The decoding tree construction method of SPDCAC is the same as that of DAC, so the computational complexity of the decoding algorithm is the same as that of DAC and DJSCAC, which is proportional to \(O(MN)\). However, in the actual decoding process, decoding ambiguity will simultaneously occur in all decoding paths in the proposed scheme, while the decoding ambiguity of each decoding path in the DAC and DJSCAC appears randomly. Therefore, the computational complexity  
Figure 8: BER performances of different DSC schemes when encoding a Markov source (\(P_{t}=0.88\)) with \(N=1024\) at various code rates.  
of the decoding algorithm in the proposed scheme will be slightly higher than those of the DAC and DJSCAC.  
Table 3 shows the average encoding and decoding times (seconds) for SPDCAC and DJSCAC under different lengths and different maximum decoding tree widths (code rate = 0.5\(b/s\), \(H(X|Y)=0.25\), \(P_{t}=0.8\)). SPDCAC and DJSCAC are running on a personal computer equipped with an Intel Core i7-6700 HQ 2.60 GHz processor.  
From Table 3, it can be seen that the actual decoding times of SPDCAC and DJSCAC will accordingly grow as the maximum width of the decoding tree and the sequence length are increased. The actual decoding time of SPDCAC is about 1.2 times that of DJSCAC, which means that the decoding complexity of SPDCAC and DJSCAC is the same order of magnitude. Although the actual decoding time of SPDCAC is longer than that of DJSCAC, the actual encoding time of SPDCAC is shorter than that of DJSCAC. This is because SPDCAC reduces the number of source symbols that need to be encoded by purging the source sequence. This can reflect the characteristics that SPDCAC meets the low energy consumption requirements of the DSC coding scheme for the encoder.
## 2 Materials and Methods
### Pulsed Electric Field Pretreatment  
The PEF pretreatment of apples was performed utilising a batch system (PEF Pilot(tm) Dual System, Elea Vertriebs- und Vermarktungsgesellschaft mbH, Quakenbruck, Germany). To accomplish that, in the beginning, a single apple was placed within a chamber, the capacity of which equalled 2-L. The build of the chamber included a set of two parallel stainless-steel electrodes, which were distanced from each other by 24 cm. Once the apple was in place, the treatment chamber was filled with water (21 \(\pm\) 1 \({}^{\circ}\)C), which served the function of a conductive medium. When weighed, the total input of the chamber added up to approximately 1.5 kg, consistent for each experiment. The parameters of the pulsed electric field were set at the following values: electrode voltage equalled 24 kV, electric field strength stood at 1 kV/cm, pulse frequency was at 20 Hz, while the pulse width took the value of 7 \(\upmu\)s. The number of rectangular pulses varied concerning the amount of supplied energy (1, 3.5, 6 kJ/kg).  
The values of the specific energy intake \(W_{spec}\) [kJ/kg] and electric field strength \(E\) [kV/cm] were calculated in accordance with the following formulas:  
\[W_{spec}\ =\ \frac{I\cdot U\cdot t\cdot n}{1000\cdot m} \tag{1}\]  
\[E\ =\ \frac{U}{d} \tag{2}\]  
where: \(I\)--current [A]; \(U\)--voltage [V]; \(t\)--duration of a pulse [s]; \(n\)--number of pulses [-]; \(m\)--mass of the total input [kg]; \(d\)--distance between electrodes [cm].  
Using a conductance (pH/conductivity meter CPC-401, Elmerton, Zabrze, Poland) for determining apples' conductivity before and after the PEF treatment allowed for calculation of the CDI (cell disintegration index), which was executed in accordance with the literature [27]:  
\[CDI\ =\ \frac{\sigma-\ \sigma_{i}}{\sigma_{d}-\ \sigma_{i}} \tag{3}\]  
where: \(\sigma\)--conductivity of tissue after applying a pulsed electric field [\(\upmu\)S]; \(\sigma_{i}\)--conductivity of intact tissue [\(\upmu\)S]; \(\sigma_{d}\)--conductivity of maximally ruptured tissue [\(\upmu\)S].
# Deciphering Autonomous Vehicle Regulations with Machine Learning
## 3 Methodology
### Machine Learning
#### 3.3.1 Measure of Predictive Performance  
The workflow used the AUC score as the primary measure of a model's predictive performance. The AUC score, which stands for \"area under the curve\", refers specifically to the area under the receiver operating characteristic (ROC) curve. The ROC curve is a graphical representation that illustrates the diagnostic ability of a binary classifier as a function of its discrimination threshold. The horizontal and vertical axes of the ROC plot the false positive and true positive rates, respectively. The false positive rate (\(1-\)specificity) is the ratio of false positives to the sum of false positives and true negatives. The true positive rate (sensitivity) is the ratio of the true positives to the sum of the true positives and the false negatives. Hence, the ROC captures the trade-off between the sensitivity and specificity of a model. The AUC score ranges from 0 to 1, providing an aggregate measure of the model's performance across all classification thresholds. A model that perfectly discriminates between the positive and negative class will have an ROC curve that passes through the upper left corner of the plot, which represents 100% sensitivity and 100% specificity with AUC = 1. An AUC score of 0.5 signifies that the model has no class separation capacity, which is no better than random guessing. The AUC score is particularly useful when dealing with imbalanced datasets, as it does not bias toward the majority class.
## 4 Conclusions  
(1) The composite interface of the clad rebar is well bonded and free of holes and inclusions. The entire composite interface can be divided into the following four zones: the carbon steel matrix zone, decarburised zone, composite zone, and stainless steel matrix zone. A large amount of martensite was found in the composite zone, and CrC precipitates were detected at the grain boundaries. The minimum value and the maximum value of the hardness of clad rebar was found in the decarburization zone at 138 HV and the recombination zone at 218 HV, respectively.  
(2) The diffusion distance of chromium was \(\approx\)35 \(\upmu\)m, and that of nickel was \(\approx\)10 \(\upmu\)m, as determined using EPMA. In addition, the uphill phenomenon occurred during the diffusion of carbon;  
(3) The tensile strength, bond strength, and minimum thickness of the clad rebar all exceeded the specified values. The reinforcement did not detach from the composite interface after forwards and reverse bending;  
(4) After welding and sealing, the clad rebar ends exhibited good corrosion resistance. In terms of corrosion rate, carbon rebar > galvanised rebar > epoxy rebar > clad rebar \(\approx\) stainless steel round bar. The corrosion rate of the clad rebar was approximately one-tenth that of the carbon reinforcement.  
(5) A layered multipass-type welding process is proposed. The welded joints produced using this process were determined to have a tensile strength greater than the standard value of the parent material, as well as good corrosion resistance.  
(6) The drawback of this paper is that it fails to investigate the service life and rolling parameters of clad steel bars, which is expected to be improved in subsequent studies.  
Conceptualization, Z.L.; methodology, Z.L. and J.T.; software, Z.L.; validation, Z.L., L.Z. and J.T.; formal analysis, Z.L.; investigation, Z.L.; resources, Z.L. and J.T.; data curation, Z.L. and Y.Z.; writing--original draft preparation, Z.L.; writing--review and editing, Z.L. and J.T.; visualization, Z.L.; supervision, Z.L.; project administration, Z.L., J.T., Y.X. and X.Q.; funding acquisition, J.T., Y.X. and X.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the key R & D project of Guangxi Zhuang Autonomous Region (2021 AB16016) and the science and technology plan project of Liazhou City (2021 AA0101B001).  
Figure 23: Schematic of tensile fracture of welded-joint specimens.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Yield Strength** & **Tensile Strength** & **Elongation** \\ \hline Welded joint specimens & 485 MPa & 660 MPa & 29\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Tensile test results of welded-joint specimens.  
Figure 24: Surface profile of the specimen after rust removal.
# GNSS Signal Distortion Estimation: A Comparative Analysis of L5 Signal from GPS II and GPS III
## 2 Models and Methods
### Joint Estimation Algorithm  
The continuous baseband signal is obtained after preprocessing by the software receiver. Accumulating and averaging continuous baseband signals can effectively reduce noise interference [14]. The baseband signal \(\mathbf{y}\) after processing is recorded as  
\[\mathbf{y}=\big{[}y_{1},y_{2},\ldots,y_{p}\big{]}^{T}, \tag{6}\]  
\[\mathbf{h}=[h_{1},h_{2},\ldots,h_{m}]^{T}, \tag{7}\]  
where \(p\) is the sampling point, \(\mathbf{h}\) is the analog impulse response matrix, and \(m\) is the order of the filter. The digital distortion of the single-branch signal is  
\[\eta=[\eta_{1},\eta_{2},\ldots,\eta_{N}]^{\mathrm{T}}\eta_{n}\in(-1,1), \tag{8}\]  
where  
\(N\) indicates the number of branches, and \(\eta\) is the digital distortion matrix. Thus, the \(n\)th signal is  
\[\mathbf{x}_{\eta_{n}}=[x_{\eta_{n}}(1),x_{\eta_{n}}(2),\ldots,x_{\eta_{n}}(p) ]^{T}. \tag{9}\]  
Figure 2: Signal distortion model on navigation payload.  
\(\mathbf{X}(\eta_{n})\) can be denoted as the \(n\)th branch signal convolution matrix.  
\[\mathbf{X}(\eta_{n})=\left[\begin{array}{cccc}x_{\eta_{n}}(1)&x_{\eta_{n}}(p)& \ldots&x_{\eta_{n}}(p-m+2)\\ x_{\eta_{n}}(2)&x_{\eta_{n}}(1)&\ldots&x_{\eta_{n}}(m+1)\\ \ldots&\ldots&\ldots&\ldots\\ x_{\eta_{n}}(p)&x_{\eta_{n}}(p-1)&\ldots&x_{\eta_{n}}(p-m+1)\end{array}\right]^ {T}. \tag{10}\]  
The navigation payload transmission channel characteristic can be transformed into a maximum likelihood estimation (MLE) joint estimate for the digital distortions and analog impulse response [10].  
\[(\mathbf{\tilde{\eta}},\mathbf{\tilde{h}})=\underset{\eta,\mathbf{h}}{\mathrm{ argmax}}\Big{\{}\|\mathbf{y}-\mathbf{X}(\eta)\mathbf{h}\|_{2}^{2}\Big{\}}, \tag{11}\]  
where \(\mathbf{X}(\eta)\) indicates the multiplexed signal convolution matrix, and \(\mathbf{\tilde{\eta}}\) indicates the digital distortion estimation matrix.  
Furthermore, the \(n\)th branch digital distortion \(\widetilde{\eta}_{n}\) is an analytical equation describing the measurement signal and the input convolution matrix.  
\[\widetilde{\eta}_{n}=\underset{\eta_{n}}{\mathrm{argmax}}\{\mathbf{y}^{H} \mathbf{X}(\eta_{n})(\mathbf{X}(\eta_{n})^{H}\mathbf{X}(\eta_{n}))^{-1} \mathbf{X}(\eta_{n})^{H}\  
Firstly, the software receiver is used to preprocess the large antenna measurement data to obtain the initial baseband signal. The MLE is then used to obtain the digital distortion for each branch. Next, a new multiplexed signal is synthesized using the digital distortion of each branch. Finally, the impulse response of the analog channel is obtained using the LS method. In the whole process, the software receiver part can use different classical algorithms according to the requirements.
# Performance Assessment of a Renovated Precast Concrete Bridge Using Static and Dynamic Tests
## 6 Evaluation of Dynamic Displacements of the Bridge  
The dynamic tests with the truck passage at different velocities were performed to obtain the dynamic response of the structure. Only traffic load was assumed in the numerical calculations. The wind effects during the tests were negligible and therefore not incorporated into the calculations. Effects of the structural dead weight were not included in the presented displacements.  
A two-axle truck was used (see Table 3, Truck 1) in the tests. An uneven surface was created by an obstacle placed in the middle of span no. 2 with a length of 0.5 m and a height of 0.06 m (Figure 14). As the axles of the truck hit the uneven surface, significant dynamic effects occurred (Figure 15).  
Vertical displacements in the middle of span no. 2 were recorded by the IBIS-S radar. Both recorded and numerical results are presented in Figure 15. The comparison of the obtained results in Figure 15 shows that the overall responses, assuming different vehicle velocities (57, 36 and 21 km/h), of both test and analysis are similar. The most significant dynamic response is caused by the vehicle passing over the obstacle in the middle of span no. 2. The vehicle used during these tests (two-axle truck--Truck 1 from Table 3) had a substantial effect on the overall response. If there are other cars in the traffic flow, they can be also assumed, and the partial effects can be added.  
Figure 14: Artificial uneven surface placed in the middle of the span no. 2 (**a**) photo; (**b**) dimensions in millimeters (\(R\)—radius).  
Figure 13: Displacement record by IBIS-S radar in the middle of span no. 2—the 3 rd frequency.  
The calculated time histories have slightly smaller dynamic amplitudes than the displacement measurement records. The reason for this could be caused by an inappropriate assumption of the stiffness of the tires (see Table 4). In this case, a rough estimation of tire stiffness was carried out by measuring changes in tire dimensions in the vertical and horizontal directions caused by gravitational forces. Tire pressure identification using sensors could be an option to obtain this data more accurately, as in [28]. This effect should be studied further in the future.  
In general, IBIS-S radar allows for the measurement of hundreds of points along the bridge with a sampling rate of up to 200 samples/s. In the case study, results from only two points are presented. Two reflectors were installed (Figure 5b) to achieve better radar signal. The two selected points are those where the displacement measurements needed to be carried out.
# Influence of Multiple Openings on Reinforced Concrete Outrigger Walls in a Tall Building
###### Abstract  
Outrigger systems have been used to control the lateral displacement of tall buildings. Reinforced concrete (R.C.) outrigger walls with openings can be used to replace conventional steel outrigger trusses. In this paper, a structural model for an R.C. outrigger wall with multiple openings was proposed, and the effects of the multiple openings on the stiffness and strength of the outrigger walls were evaluated. The equivalent bending stiffness of the outrigger wall was derived to predict the lateral displacement at the top of tall buildings and internal shear force developed in the wall. The openings for the passageway in the wall were designed by the strut-and-tie model. The stiffness and strength of the outrigger wall with multiple openings was analyzed by the nonlinear finite element analysis. Taking into consideration the degradation in stiffness and strength, the ratio of the opening area to the outrigger wall area is recommended to be less than 20%. The degradation of stiffness due to openings does not affect the structural performance of the outrigger system when the outrigger has already large stiffness as the case of reinforced concrete outrigger walls.  
outrigger wall; multiple openings; deep beam; stiffness; shear strength; tall building Article
## 4 Networked ATM-CPS Model and Its Threat Model
### Networked ATM-CPS Model
The adjacency matrix and association matrix reflecting the networked ATM cyber system and the physical system are another form of representation of the complex network; therefore, starting with the analysis of the adjacency matrix or association matrix of the network, the adjacency matrix of ATM graphs and the association matrix of the hypergraphs are represented by graph theory. The complex network is constructed by matrix operation of the adjacency matrix based on graph and hierarchical graph and association matrix of hypergraph and hierarchical hypergraph. At the same time, the degree distribution polynomial is introduced to analyze the degree distribution characteristics and other characteristics of the networked ATM. Specifically, five steps are included. Figure 5: Networked ATM-CPS model. First, the classical theories, such as graph theory, fractal theory, and stability theory, are used to analyze the ATM network model on the basis of matrix operations. Second, the average path length of the ATM network is measured as a basis for determining the actual ATM reachability. Third, the cluster structure (association structure) of the complex network is used to analyze the robustness of the network when the nodes or edges of the ATM network are subject to accidental and deliberate attacks (fault tolerance). Fourth, on the basis of the flow of the actual ATM service and the characteristics of the network traffic, the meshing number of the complex network is used to determine the distribution state of its service traffic to avoid cascade failure (avalanche phenomenon) in the ATM-CPS. Fifth, the clustering points in the ATM-CPS are identified by combining the efficiency parameters in the complex network. By identifying the agglomeration points, prevention strategies are proposed to ensure the stability and risk resistance of the networked ATM-CPS.
## 3 Results
### Effects of Ultrasonic Vibration on the Fluidity of Cement Paste after CO\({}_{2}\) Absorption
#### 3.3.1 The Most Available Geometric Diameter of Pore Distribution  
The differential curve of the pore diameter of hardened cement paste under CO\({}_{2}\) AA was tested by the mercury intrusion method (Figure 6). For CO\({}_{2}\) AA of 0%, 0.44%, 0.88%, 1.32%, 1.76%, and 2.20%, the most available geometric diameters in the pore distribution differential curve of hardened cement paste are 112.5, 118.5, 92.5, 83.1, 80.7, and 72.7 nm under mechanical agitation. Under ultrasonic vibrating agitation, the most available geometric diameters in the pore distribution differential curve of hardened cement paste are 78.7, 83.3, 70.5, 60.6, 49.4, and 48.2 nm, respectively, which are decreased by 30%, 29.7%, 23.8%, 27.1%, 38.8%, and 33.7% compared to those under mechanical agitation.  
Figure 5: Effects of CO\({}_{2}\) AA on the fluidity of cement paste.  
Figure 6: Differential curve of pore distributions of hardened cement paste under different CO\({}_{2}\) AA: (a) 0%; (b) 0.44%; (c) 0.88%; (d) 1.32%; (e) 1.76%; (f) 2.20%.  
It can be obtained according to Figure 6 that the most available geometric diameter of pore distribution of the hardened carbonized cement paste with ultrasonic agitation is lower than that with mechanical agitation. When the CO2 AA is respectively 0.44%, 0.88%, 1.32%, 1.76%, and 2.20%, the most available geometric diameter of pore distribution of the hardened carbonized cement paste with ultrasonic agitation gradually decreases by 15.4%, 14%, 18.5%, and 2.4% compared to that with mechanical agitation, which indicates that the most available geometric diameter of pore distribution of the hardened carbonized cement paste with ultrasonic agitation gradually decreases with an increase in CO2 AA. Therefore, the most available geometric diameter of pore distribution of the hardened carbonized cement paste can effectively be reduced with ultrasonic agitation.
# Lightweight Physical Layer Aided Key Agreement and Authentication for the Internet of Things +
## 4 Performance Analysis
### Experimental Results  
Figure 7 is the histogram of the RMSD results of the experiment. For ease of comprehension, we assume that the profiles can be fitted as a log-normal distribution, i.e., \(\ln(X)\sim(v_{i},\sigma_{i}^{2})\) that PDF and CDF are defined by the following:  
\[f_{\eta|\mathcal{H}_{i}}(x)=\frac{1}{x\sigma_{i}\sqrt{2\pi}}e^{-\frac{(\ln x- v_{i})^{2}}{2v_{i}^{2}}},\quad x>0\quad\text{and}\quad i=0,1 \tag{18}\]  
\[F_{\eta|\mathcal{H}_{i}}(x)=\frac{1}{2}+\frac{1}{2}\text{erf}\bigg{(}\frac{ \ln x-v_{i}}{\sqrt{2}\sigma_{i}}\bigg{)} \tag{19}\]  
where \(v_{i}=\mathbb{E}[\eta|\mathcal{H}_{i}]\), \(\sigma_{i}^{2}=\text{Var}[\eta|\mathcal{H}_{i}]\) and \(\text{erf}(\text{x})\) is error function [28] respectively. From the above equations, \(P_{M}^{\circ}\), \(P_{F}^{\circ}\) and \(\lambda\) can be induced by applying the concept in Equations (8)-(10), conversely. Then, in the case of RMSD, the thresholds are determined as follows:  
\[\alpha_{1}=\underset{\alpha}{\text{argmax}}(1-F_{\eta|\mathcal{H}_{1}}(\alpha ))\leq P_{M}^{\circ} \tag{20}\]  
\[\alpha_{0}=\underset{\alpha}{\text{argmax}}\,F_{\eta|\mathcal{H}_{0}}(\alpha )\leq P_{F}^{\circ} \tag{21}\]  
As shown in Figure 8, \"Black\", \"Gray\" and \"White\" are determined as follows:  
\[\Theta=\begin{cases}\text{Black}&\text{if}\quad\eta>\alpha_{1}\\ \text{Gray}&\text{if}\quad\alpha_{0}\leq\eta\leq\alpha_{1}\\ \text{White}&\text{if}\quad\eta<\alpha_{0}\end{cases} \tag{22}\]  
Figure 6: Raw and irregularly sampled channel phase profiles of (a) independently simulated and (b) USRP experimented correlated channels.  
Figure 9 shows that the signaling overhead of conventional EPS-AKA and the proposed PL-AKA in different numbers of subcarriers and target false alarm rates, where \(P=30\), \(U=20\), \(N=200\), \(P_{M}^{\circ}=10^{-6}\). In addition, we investigated not only the signaling overhead of total protocol, but also that of MME as described in (16). In terms of entire protocol, it alleviates the burden of MME, which can be desirable for the future in distributed networks. In addition, the proposed scheme achieves a computational predominance by sacrificing the target false alarm rate. Thus, the number of subcarrier and false alarms and the miss detection probability should be coordinated moderately as its application.  
Figure 8: PDFs of RMSD for PL-AKA.  
Figure 7: Histogram of sanitized phase RMSD \(\eta\).