esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 \"Multistage Anchorage System + CFST\" Combined Support Technology
### Establishment of the New Support Method
#### 4.1.2 Crest  
CFST consisting of steel pipes and filled concrete is a passive support structure used to control roadway stability, especially in the case of terrible geological conditions, such as soft rock mass and high ground stress. The regional stability of the thin-walled steel tube is maintained by the inner concrete. In turn, the tube has a confining effect on the concrete, causing a tri-direction stress state and enhancing its strength. Therefore, the CFST has a high bearing capacity and can better control the surrounding rock mass's deformation.  
The short column's ultimate bearing capacity (\(N_{0}\)) of CFSTs under axial compression can be measured using the following formula [17]:  
\[\begin{cases}N_{0}=A_{c}f_{c}(1+\sqrt{\theta}+1.1\theta)\\ \theta=A_{c}f_{c}/(A_{c}f_{c})\end{cases}, \tag{3}\]  
where \(A_{c}\) is the cross-sectional area of the concrete (23,778 mm\({}^{2}\)), \(f_{c}\) is the strength of the concrete (25 N/mm\({}^{2}\)), \(\theta\) is the steel coefficient of the casing hoop, \(A_{s}\) is the cross-sectional area of the tube (5780 mm\({}^{2}\)) and \(f_{s}\) is the steel material yield limit of 215 N/mm\({}^{2}\). Taking a reduction factor of 0.78 into account, the \(N_{0}\) value obtained was 2012 kN.  
Moreover, as illustrated in Figure 14, the supporting force (\(\sigma\)) can be calculated by:  
\[\begin{cases}\text{s}\int_{\pi/4}^{\pi/2}\sin\theta\sigma Rd_{\theta}=N_{0} \cos\frac{\pi}{4},\end{cases} \tag{4}\]  
Figure 13: Synergistic effect of the multistage anchorage system.  
where s is the steel tubes' distance and the tube radius is \(R\).  
Through substitution, we obtained a result of 0.76 MPa for \(\sigma\), which is greater than the 0.45 MPa supporting force of the U-shaped steel. Therefore, CFST is a better choice in this study area and makes up for the low strength of the U-shaped steel.  
After CFST construction, supporting structures and the surrounding rock mass formed a hierarchical support system that cooperated and worked together to deal with roadway deformation, that is, the spray layer and the CFST formed the first level, which provided a normal constrained force for the surrounding rock on the face of the roadways. The bolt's second level improved the shallow broken rock mass's performance and the third level was formed of cables, which developed a continuous bearing structure through effectively connecting the shallow fractured rock and the deep intact rock.
## 3 Proposed Methods and Model Architecture  
### Overall Framework of the Proposed Model  
In order to increase the model's detection accuracy, an infrared target detection framework with high feature expression ability was developed and is presented in Figure 2. The C2f module is introduced as the backbone feature extraction module of the model, and the gradient information flows into multiple branches to improve the efficiency of data calculation. Each branch can focus on different features, such as edge, texture, color, etc., thereby heightening the expression capacity of the network. Furthermore, enhancing the gradient mobility in the network helps with mitigating the vanishing gradient problem, leading to more efficient training. Specifically, the module contains two convolutional layers and adopts a channel-splitting structure. One branch uses residual maps and the other branch processes information by stacking bottlenecks to form n paths. The C2f_ 2 of the backbone in Figure 2 represents n = 2. SPPF is used in the final phase of the backbone network to mitigate the loss in the receptive field, enabling the capture of multiscale information from the input data, which is very important for detecting objects with different scales in the image. At the same time, the feature layers P3, P4, and P5 are selected to integrate with the abstract feature mapping enhanced by the neck network, and the spatial detail information in the deep features is further extracted through the ISW module to increase the model's the information representation power, which can capture more comprehensive local and global information, thereby improving detection precision. Additionally, we selected TAHNet as the head prediction network. The postprocessing part follows the original advanced method of YOLOv5, and the bounding box loss function employs CIOU [33], whose expression is described in Equation (10), which is mainly employed to assess the alignment between the predicted box and the real box. Finally, through a series of comparative test evaluations, it is demonstrated that the network model proposed in the study shows excellent performance in complex conditions.  
Figure 2: The global network framework of the detection model. The backbone network uses the C2f module for feature extraction, where C2f_ 2 indicates that the channel split parallelizes two bottleneck structures.  
\[L_{CIOU}=1-IOU+\rho(\theta)+\gamma v \tag{10}\] \[\gamma=\frac{v}{1-IOU+v^{\prime}}\] (11) \[v=\frac{4}{\pi^{2}}(\arctan\frac{w^{gt}}{hs^{gt}}-\arctan\frac{w}{ h})^{2} \tag{12}\]  
where _IOU_ stands for the ratio of the intersection region of the predicted box and the actual box to the union area. \(\rho(\theta)\) reflects the center distance term and the diagonal length term. \(v\) characterizes the width-to-height ratio term, and \(\gamma\) is an equilibrium coefficient. \(w^{gt}\) and \(hs^{gt}\) stand for the width and height of the true target box, and \(w\) and \(h\) denote the width and height of the predicted box.
# Time Series Clustering of Online Gambling Activities for Addicted Users' Detection
## Appendix A Time Series Analysis
### Time Series Averaging and Centroid Estimation  
Averaging a set of time series, which is a fundamental and necessary task in clustering, needs to address the problem of multiple temporal alignments. A standard way to average two time series, under time warping, is to synchronize them and average each pairwise temporal warping alignment. The problem increases in complexity if there is the need to perform multiple alignment, since there is the need to determine a multiple alignment that links simultaneously all the time series on their commonly shared similar elements.  
There are three different approaches to determine a multiple alignment: dynamic programming, progressive, and iterative.  
Dynamic programming approaches search the optimal path within an \(N\)-dimensional grid that crosses the \(N\) time series. Progressive approaches progressively combine pairs of time series centroids to estimate the global centroid. Iterative approaches works similarly to the progressive approach, but they also reduce the error propagation problem, which arises when early errors propagate in all subsequent centroids through the pairwise combinations, by repeatedly refining the centroid and realigning it to the initial time series. This work focuses on iterative approaches that have a heuristic nature and are limited to the dynamic time warping metric. In particular, the paper uses the DTW Barycenter Averaging (DBA) [24] described in the following section.  
Under DTW, given the time series \(x_{i}\), \(x_{j}\) and their DTW alignment \(\pi^{*}\), the centroid \(c(x_{i},x_{j})\) is defined by averaging their aligned elements as:  
\[c(x_{i},x_{j})=(\text{AVG}(x^{i}_{\pi^{*}_{1}(1)},x^{j}_{\pi^{*}_{2}(1)}), \ldots,(\text{AVG}(x^{i}_{\pi^{*}_{1}(m)^{\prime}},x^{j}_{\pi^{*}_{2}(m)})\]  
where \(\text{AVG}(\cdot)\) is a standard numerical averaging function. The length \(T\leq m\leq 2T-1\) of a centroid \(c(x_{i},x_{j})\) is generally higher than the length of the centered time series.  
Let \(X=\{x_{1},\ldots,X_{N}\}\) be a set of time series \(x_{i}=(x_{i1},\ldots,x_{iT},i\in\{1,\ldots,N\}\) and \(\mathbb{S}\) the space of all time series sequences. \(\forall s\in\mathbb{S}\), the average sequence \(c\) should satisfy:  
\[\sum_{i}^{N}\text{DTW}(x_{i},c)\leq\sum_{i}^{N}\text{DTW}(x_{i},s)\]  
Since no information on the length of the average sequence \(c\) is available, the search cannot be limited to sequences of a given length, so all possible length values for averages have to be considered.
# An Insight to the Outage Performance of Multi-Hop Mixed RF/FSO/IWOC System
## 4 Asymptotic Analysis
### UWOC Link \((R_{2}\to D)\)  
The asymptotic expression of the CDF of the third hop, i.e., the UWOC link, given in (4), can be expressed as in [39], as  
\[F_{\gamma UW}^{\infty}(\gamma)\simeq\frac{\omega\gamma_{th}}{\lambda\tilde{ \gamma}}+\frac{1-\omega}{\Gamma(a+1)}\Big{(}\frac{\gamma_{th}}{b\tilde{\gamma }}\Big{)}^{ac}. \tag{11}\]  
Further, substituting (9), (10), and (11) in (8) and re-arranging terms, the asymptotic expression at high-SNR regime for the end-to-end outage probability of the system can be given as  
\[\begin{split} P_{out}^{\infty}&=\Big{(}\frac{ \Gamma(m+1)^{\frac{1}{n}}}{mL_{RF}\gamma_{th}}.\tilde{\gamma}\Big{)}^{-m}+ \left(\frac{X^{\frac{\gamma^{2}}{b_{k}}}}{\gamma_{th}}\tilde{\gamma}\right)^ {\frac{-b_{k}}{2}}\\ &+\Big{(}\frac{\lambda}{\omega\gamma_{th}}.\tilde{\gamma}\Big{)}^ {-1}+\Big{(}\frac{b\Gamma(a+1)}{\gamma_{th}(1-\omega)}.\tilde{\gamma}\Big{)}^ {-ac}.\end{split} \tag{12}\]  
Now, coding gain and diversity gain are simply evaluated from the asymptotic outage probability of the system mentioned in (12). It can be clearly seen that the system performance is dominated by the parameters of the worst link among the three links. Hence, the diversity gain of the system is \(\min(\frac{b_{k}}{2},m,ac)\). Based on \(G_{D}\), there can be three cases to examine the system's overall outage performance, as shown below.  
1. Case 1: Among three links, when only one link is dominating, the coding gain can be written as \[G_{C}=\begin{cases}\frac{X^{\frac{\gamma^{2}}{b_{k}}}}{\gamma_{th}},&G_{D}= \frac{b_{k}}{2}\\ \frac{\Gamma(m+1)^{\frac{1}{n}}}{mL_{RF}\gamma_{th}},&G_{D}=m\\ \frac{\lambda}{\omega\gamma_{th}}+\frac{b\Gamma(a+1)}{(1-\omega)\gamma_{th}},&G_{D}= ac\simeq 1\end{cases}\] (13)
2. Case 2: When two links are dominating out of three links, the coding gain can be written as \[G_{C}=\begin{cases}\frac{1}{2}\Bigg{(}\frac{\chi\frac{\gamma^{2}}{\aleph_{k}}}{ \gamma_{lh}}+\frac{\Gamma(m+1)\frac{1}{h}}{mL_{RF}}\Bigg{)},&G_{D}=\frac{b_{k}}{2 }=m\\ \frac{1}{2}\Bigg{(}\frac{\Gamma(m+1)\frac{1}{h}}{mL_{RF}\gamma}+\Big{(}\frac{ \lambda}{\omega\gamma_{lh}}+\frac{h\Gamma[a+1]}{(1-\omega)\gamma_{lh}}\Big{)} \Bigg{)},&G_{D}=ac\simeq 1=m\\ \frac{1}{2}\Bigg{(}\frac{\chi\frac{\gamma^{2}}{\aleph_{k}}}{\gamma_{lh}}+\Big{(} \frac{\lambda}{\omega\gamma_{lh}}+\frac{h\Gamma(a+1)}{(1-\omega)\gamma_{lh}} \Big{)}\Bigg{)},&G_{D}=ac\simeq 1=\frac{b_{k}}{2}.\end{cases} \tag{14}\]
3. Case 3: When all three links are dominating, then the coding gain can be written as \[G_{C}=\Bigg{\{}\frac{1}{3}\Bigg{(}\frac{\chi\frac{\gamma^{2}}{\aleph_{k}}}{ \gamma_{lh}}+\frac{\Gamma(m+1)\frac{1}{h}}{mL_{RF}\gamma}+\frac{\lambda}{ \omega\gamma_{lh}}+\frac{h\Gamma(a+1)}{(1-\omega)\gamma_{lh}}\Bigg{)},&G_{D}= \frac{b_{k}}{2}=m=ac\simeq 1.\] (15)
###### Abstract  
In this study, a surrogate Machine Learning (ML)-based model was developed, to predict the load-bearing capacity (LBC) of concrete-filled steel square hollow section (CFSS) members, considering loading eccentricity. The proposed Artificial Neural Network (ANN) model was trained and validated against experimental data using the following error measurement criteria: coefficient of determination (R\({}^{2}\)), slope of regression, root mean square error (RMSE) and mean absolute error (MAE). A parametric study was conducted to calibrate the parameters of the ANN model, including the number of neurons, activation function, cost function and training algorithm, respectively. The results showed that the ANN model can provide reliable and effective prediction of LBC (R\({}^{2}\) = 0.975, Slope = 0.975, RMSE = 294.424 kN and MAE = 191.878 kN). Sensitivity analysis showed that the geometric parameters of the steel tube (width and thickness) and the compressive strength of concrete were the most important variables. Finally, the effect of eccentric loading on the LBC of CFSS members is presented and discussed, showing that the ANN model can assist in the creation of continuous LBC maps, within the ranges of input variables adopted in this study.  
concrete-filled steel square hollow section columns; compressive behavior; Machine Learning; loading eccentricity; surrogate model; load-bearing capacity +
Footnote †: journal: Applied Sciences
# UAV Dynamic Object Tracking with Lightweight Deep Vision Reinforcement Learning
## 3 Preliminaries  
### Problem Statement  
The goal of this experiment is to fly an UAV to autonomously follow a designated target object over a specified duration, while simultaneously avoiding collisions with any surrounding obstacles. The target object exhibits dynamic behavior, possessing the ability to move in arbitrary directions during the mission. The UAV's operations are performed under the constraints of continuous state space (which represents the range of possible positions and orientations) and discrete action space (a finite set of possible control commands). A notable aspect of this experiment is the exclusive reliance on depth images as the primary source of input data for the UAV's navigation and tracking functions. These images are captured by a front-mounted camera, providing crucial information on the relative position and distance of the target object and any potential obstacles in the UAV's flight path. The UAV's state observations rely entirely on the camera inputs, effectively eliminating the need for additional tools or equipment such as sensors or GPS.
# Enhanced Eco-Friendly Concrete Nano-Change with Eggshell Powder
## 3 Results
### Compressive Strength  
Figure 2 shows the dependence of the change in the compressive strength of concrete on the amount of eggshell powder.  
The dependence of the compressive strength of concrete on the amount of eggshell powder is well approximated by a 4 th degree polynomial with a determination coefficient \(R^{2}=0.98\)  
\[\begin{array}{l}R_{b,cub}=48.8+0.248\;*\;x+0.06301\;*\;x^{2}-\;0.006684\;*\;x ^{3}+0.0001303\;*\;x^{4}\\ R^{2}=0.98\end{array} \tag{6}\]  
As shown in Figure 2, the compressive strength of concrete when replacing 5%, 10%, 15%, 20%, 25%, and 30% cement with eggshell powder was 50.5 MPa, 53.2 MPa, 49.5 MPa, 47.3 MPa, 40.5 MPa, and 38.1 MPa, respectively. It can also be seen that the maximum compressive strength of concrete was recorded when replacing 10% of cement with eggshell powder. With a further increase in the dosage of the powder, the strength values decrease. A particularly sharp drop is observed at the points of 25% and 30%.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Indicator Title** & **Value** \\ \hline Compressive strength, MPa, \(R_{b,cub}\) & 48.9 \\ Flexural tensile strength, MPa, \(R_{bb}\) & 5.8 \\ Axial compressive strength, MPa, \(R_{b}\) & 36.8 \\ Axial tensile strength, MPa, \(R_{tt}\) & 3.3 \\ Limit deformations under axial compression, mm/m \(\times\)\(10^{-3}\), \(\varepsilon_{b}\) & 2.20 \\ Limit deformations in axial tension, mm/m \(\times\)\(10^{-4}\), \(\varepsilon_{tt}\) & 1.32 \\ Modulus of elasticity, MPa, \(E\) & 33.7 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Strength and deformation characteristics of concrete samples of the control composition.
## 2 Materials and Methods  
### Method of Navigation for the Automated Guided Vehicle  
The research results presented in the article concern an AGV vehicle in which odometry was used. This is the basic method for determining the position of an AGV [43]. In odometry, the current position of the vehicle is determined on the basis of the distance traveled by the characteristic point of the vehicle (Figure 1). The directional angle \(\alpha\) is determined on the basis of the difference between the speeds of the left wheel \(\mathrm{v_{L}}\) and the right wheel \(\mathrm{v_{R}}\).  
Odometry is a method consisting of counting the distance traveled by a vehicle's wheels (\(\mathrm{W_{L}}\), left wheel; \(\mathrm{W_{R}}\), right wheel) and determining the change in the directional angle \(\alpha\) of the vehicle movement at each calculation step. This method is used in vehicles with two independently driven wheels. The rotation of the vehicle about a vertical axis of rotation that passes through the point \(\mathrm{O}\) and the change in the directional angle \(\alpha\) is achieved by varying the rotational speeds of two independently driven wheels.  
The vehicle position and actual position of the \(\mathrm{O}\) point in the base reference system \(\mathrm{X_{m}O_{m}Y_{m}}\) (Figure 1), in the \(i+1\) iteration, are given by the relationship in (1), assuming that the iteration \(i\) is expressed by a state vector, \((x(i),y(i),\alpha(i))\):  
\[\begin{bmatrix}x(i+1)\\ y(i+1)\\ \alpha(i+1)\end{bmatrix}=\begin{bmatrix}x(i)\\ y(i)\\ \alpha(i)\end{bmatrix}+\begin{bmatrix}\Delta t\cdot v_{\mathrm{O}}(i+1)\cdot \cos(\alpha(i)+\Delta t\cdot\omega(i+1))\\ \Delta t\cdot v_{\mathrm{O}}(i+1)\cdot\sin(\alpha(i)+\Delta t\cdot\omega(i+1) )\\ \Delta t\cdot\omega(i+1)\end{bmatrix} \tag{1}\]  
Figure 1: An AGV vehicle in the adopted coordinate system.  
Equations (2) and (3) show the relationships for speed of \(v_{\rm O}(i+1)\) and \(\omega(i+1)\):  
\[v_{\rm O}(i+1)=(v_{\rm R}(i+1)+v_{L}(i+1))/2 \tag{2}\]  
\[\omega(i+1)=(v_{\rm R}(i+1)-v_{L}(i+1))/b \tag{3}\]  
where \(v_{\rm R}(i+1)\) and \(v_{L}(i+1)\) are the speeds of the left and right wheels, respectively; \(b\) is the wheelbase of the driven wheels.  
The quantities \(v_{\rm R}(i+1)\) and \(v_{L}(i+1)\) can be written using Equations (4) and (5):  
\[v_{\rm R}(i+1)=\omega_{\rm R}(i+1)\cdot r \tag{4}\]  
\[v_{L}(i+1)=\omega_{L}(i+1)\cdot r \tag{5}\]  
The relationships for angular speed \(\omega(i+1)\) and speed \(v_{\rm O}(i+1)\) are as follows:  
\[\omega(i+1)=(\omega_{R}(i+1)-\omega_{L}(i+1))\cdot r/b \tag{6}\]  
\[v_{\rm O}(i+1)=(\omega_{R}(i+1)+\omega_{L}(i+1))\cdot r/2 \tag{7}\]  
where \(\omega_{R}(i+1)\) and \(\omega_{L}(i+1)\) are the angular speeds of the left and right wheels, respectively; \(r\) is the radius of the drive wheels.  
The above considerations apply to the case where the wheels are rigid, they roll without slipping, the contact of the wheel with the road is the point and the radiuses \(r\) of the drive wheels are the same.
# Comparison of Enhanced Noise Model Performance Based on Analysis of Civilian GPS Data
## 3 Analysis of Observations
### Stochastic Processes
This confidence interval at the \(\alpha=0.05\) level is plotted with dashed lines in Figure 3.  
Figure 3: Correlogram of latitude errors of a time series from Dunedin of \(\sim\)250 h. The dashed lines correspond to the classical 95% confidence interval, while the dash-dotted curve corresponds to the Box–] Jenkins standard error. The thin solid line represents the exponentially decaying curve fitted to the first half-hour section. The inset depicts the empirical partial autocorrelation function. The majority of \(\widehat{\rho}(h)\) falls outside this interval; therefore, our observations and the assumption of Gaussian white noise are inconsistent with each other. One may take into account that the empirical autocorrelation function estimated from a finite-length time series also carries error [37] and could derive a modified confidence interval. At significance level \(\alpha\), this is given by the Box-Jenkins standard error [38; 39; 40]:  
\[-\frac{z\left(1-\frac{\alpha}{2}\right)}{\sqrt{n}}\;S(h)\leq\widehat{\rho}(h) \leq\frac{z\left(1-\frac{\alpha}{2}\right)}{\sqrt{n}}\;S(h),\]  
where \(S(h)=\sqrt{1+2\sum_{k=1}^{h-1}\widehat{\rho}^{2}(k)}\). Figure 3 also depicts the Box-Jenkins standard error with dash-dotted lines, and it is apparent that the two confidence intervals differ from each other substantially, thus confirming the statistical dependence in the measured location readings. It is worth noting here that the exponential fit and the first crossing of \(\widehat{\rho}(h)\) with the Box-Jenkins confidence interval agree with each other. The inset in Figure 3 shows \(\widehat{\rho}_{\text{p}}(h)\) for \(h\leq 20\) seconds with its standard error at a 0.95 significance level. This graph may suggest the application of a high-order autoregressive noise model (see next section), since \(\widehat{\rho_{\text{p}}}(h)\) vanishes only for lags larger than approximately 30 s.  
However, we find that other datasets exhibit much shorter'memory', so lower-order processes might also be sufficient. Datasets from other locations exhibited different behaviour (see Figure 4). The autocorrelation still takes less than an hour to reach the Box-Jenkins standard error, but the initial drop is much sharper. This drop may explain the different behaviour of the partial autocorrelation function, which vanishes for lags larger than unity. Figures 3 and 4 illustrate the extreme cases. The autocorrelation of the Thursday Island dataset, see Figure 5, displays persistent oscillations not observed at other locations. This could be because of a temporary loss of power experienced on Thursday Island, which forced the data to be split into two sets, each approximately 24 h long, or an artefact of the local geography contributing to long-term multipath effects. Figure 4: Correlogram of longitude errors for the GPS data taken in Port Douglas. The line styles are identical to those in Figure 3.
# Full-Duplex MAC Protocol for CSMA/CA-Based Single-Hop Wireless Networks
## 2 System Model  
We assumed that there are \(n\) nodes in the network that all have FD capability and adopt the CSMA/CA mechanism to contend for the channel. Each node maintains \(n\)\(-\)\(1\) frame sending queues for the other nodes and these queues are always non-empty. Each node can successfully receive signals from any of the other \(n\)\(-\)\(1\) nodes, but it cannot simultaneously receive two or more signals from the others. This model is the single-hop network model, where no hidden terminal problems exist. For simplicity, we did not require the contention windows of the nodes to increase as the numbers of their transmission collisions increased. The contention windows of the nodes were all set to a constant value (denoted by \(W\)). If the node obtained a channel access opportunity, then it randomly selected one of the \(n\)\(-\)\(1\) other nodes to send a frame. Here, the scheduling fairness [8] and routing protocol [9] were not considered. We assumed that no errors occurred in the frame transmission and each node could reduce the SI strength below the background noise. We did not discuss the interference cancellation in the physical layer [10]. The SI did not affect its own receiving or sensing. The frames sent by the nodes had the same size and were all composed of a header (abbreviated as \"Hdr\") and a payload (abbreviated as \"Pyl\"). Their sizes and transmission times were denoted by \(L_{Hdr}\) (\(L_{Pyl}\)) and \(T_{Hdr}\) (\(T_{Pyl}\)),respectively. The semantic and size of each field of the frames were consistent with those used in the HD networks. When the node received a frame, we assumed that it could decode the frame's header independently. This mechanism is called the cut-through [11], which is commonly used to fast-forward the frame (link layer) or packet (network layer).