esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion  
### Slope Safety Protection  
For the problem of slope stability evaluation, feature importance analysis can reflect the relevant influence degree of each influencing factor on slope stability, so as to provide an effective reference for the formulation of slope safety protection. Figure 10 shows the results of the sensitivity analysis. The results show that slope angle (\(\Phi\)) is 0.28, cohesion (\(c\)) is 0.16, pore pressure ratio (\(r_{u}\)) is 0.13, slope height (\(H\)) is 0.12, unit weight (\(\gamma\)) is 0.03, and friction angle (\(\varphi\)) is 0.011. It is important to note that the importance scores and ranking of each input variable will be different for different datasets and different feature importance scores. It is worth mentioning that the importance ranks of each of the input variables obtained in this paper apply only to the dataset in Section 3 and cannot be considered as general rules. With more valid slope cases and more comprehensive feature consideration in the future, more representative results can be obtained.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{5}{c}{**FoS**} \\ \hline
**Case** & **True Values** & **ANN Model**[83] & **Error (\%)** & **PCA-PANN Model** & **Error (\%)** \\ \hline Case 1 & 1.458 & 1.40 & \(-\)3.978 & 1.447 & \(-\)0.754 \\ Case 2 & 1.904 & 1.38 & \(-\)27.521 & 1.894 & \(-\)0.525 \\ Case 3 & 1.150 & 1.08 & \(-\)6.087 & 1.136 & \(-\)1.217 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of model prediction effects.  
Figure 9: (**a**) Locations of the monitoring slope case. (**b**) Stratigraphic model of the monitoring slope [83].  
Several relatively important characteristics (slope angle (\(\Phi\)), cohesion (\(c\)), pore pressure ratio (\(r_{u}\)), and slope height (\(H\))) are selected and combined with the slope engineering practice for analysis and prevention. It is suggested to carry out slope prevention from the following three aspects:  
1. Reduce the slope height and slope angle: for high and steep slopes, the slope height and slope angle can be reduced by cutting the head and cutting the slope to reduce the load.
2. Reduce unit weight and pore water pressure: for surface water can be set up drainage ditch, drainage ditch, etc.; the blind ditch, drainage hole, and collection well can be set for groundwater.
3. Increase shear strength: A vegetation cover and vertical root system are used to prevent slope erosion, reduce pore water pressure, and improve shear strength. Specific methods include planting trees, planting grass, and laying sod.
## 3 Method
### Repeated Pattern Size Optimization  
Considering fronto-parallel images have the characteristics that even though with diverse image content, they have relatively regular or near-regular topology, an autocorrelation model integrating template matching with the _dynamic similarity threshold_ is developed for repeated pattern size optimization in this study.  
Based on the initial repeated pattern size \(d^{*}\), a template patch \(T\) is obtained by cropping the image from the left-upper point \(o=(0,0)\) by size \(d^{*}\). The autocorrelation model determines the repetition by matching template \(T\) on every possible area within the image by computing a pairwise correlation \(cor_{(x,y)}\):  
\[cor_{(x,y)}=\frac{\sum_{x,y}\left(T_{(0,0)}\cdot S_{(x,y)}\right)}{\sqrt{ \sum_{x,y}{T_{(0,0)}}^{2}\cdot\sum_{x,y}{S_{(x,y)}}^{2}}} \tag{6}\]  
where \(S_{(x,y)}\) denote the area started from point \((x,\,y)\), and the correlation \(cor_{(x,y)}\) indicates the similarity degree between template \(T\) and area \(S_{(x,y)}\). The larger correlation value \(cor_{(x,y)}\), the higher the similarity.  
Since the image can be understood as a continuously duplicating repeated pattern,the repetition structures in the image can be obtained by finding all the areas identical to the template patch. For efficient calculation and to avoid confusion at the starting region, the search is applied to a cropped image \(I_{ox}\gets I\left[\frac{1}{3}d_{x}^{*}:w,0:d_{y}^{*}\right]\), \(I_{oy}\gets I\left[0:d_{x}^{*},\frac{1}{3}d_{y}^{*}:h\right]\) instead of the entire image \(I\). In order to optimize \(d_{x}^{*}\), \(T\) is moved over the cropped image \(I_{ox}\), pixel by pixel, and all the \(cor^{x}\) values are calculated by Equation (6).  
Considering that input images may have diverse content, a dynamic threshold is set for identifying repeats:\[sim_{x}=\varepsilon*\ max(cor^{x}) \tag{7}\]  
where \(\varepsilon\) is adjustment parameter in range of \((0,1)\). When the first matched area is found at the \(x^{*}\)th pixel in the \(x\) direction, i.e., \(cor^{x^{*}}>sim_{x}\), the repeated pattern size in the \(x\) direction is refined as  
\[d_{x}^{**}=x^{*}+\frac{1}{3}d_{x}^{*} \tag{8a}\]  
Moreover, a minimum pixel distance, e.g., 2 pixels, is defined to avoid too small repeat sizes being detected. If the distance \(\triangle d\) between the adjacent area \(S_{w,0}\) and \(S_{w+\triangle d,0}\) is smaller than the required minimum distance, then \(d_{x}^{**}=d_{x}^{**}+\triangle d\). The threshold of pixel distance \(\triangle d\) can be adjusted based on the complexity of the image. Similarly, the optimization is repeated along the \(y\) direction.  
\[d_{y}^{**}=y^{*}+\frac{1}{3}d_{y}^{*} \tag{8b}\]  
Figure 4a illustrates the size optimization along the \(x\) direction, and the repeated pattern grids before and after size optimization are compared in Figure 4b,c. As shown, the detection accuracy is significantly improved with this optimization algorithm.
# Enhanced Fall Detection Using YOLOv7-W6-Pose for Real-Time Elderly Monitoring
## 1 Introduction
_Key point Analysis_: The generated skeleton map allows for detailed posture and movement analysis, enabling accurate fall detection and differentiation from normal activities. In aged care settings, where continuous monitoring is often required, an efficient fall detection system can reduce the risk of unattended falls. The proposed system offers reliable real-time detection, supporting caregivers and healthcare professionals in delivering timely interventions in response to incidents [38]. The objective of this study was to develop a real-time fall detection system by using the YOLO object detection algorithm with pose estimation. This system was developed under the ongoing project entitled \"Advanced Artificial Intelligence Techniques in Science and Applications\", which focuses on advancing mental health monitoring using the Neuro-Predict platform. Pre-trained models were implemented to improve the accuracy of fall detection and to expedite the development process, enhancing overall system performance. The main contributions of this study are as follows:  
* Development of a vision-based fall detection approach: The proposed system enhances the detection accuracy and reliability of fall detection by utilizing advanced computer vision techniques. * By integrating a pre-trained YOLOv7-W6-Pose, the system accurately estimates human key points, enabling precise monitoring of posture and movement. * Creation of a comprehensive fall dataset: This dataset includes a wide variety of scenarios, compiled from videos sourced both from the internet and through original recordings. This diverse dataset enables the robust training and evaluation of the proposed model. This paper is organized into the following sections: the Introduction presents the primary objectives and the scope of the study, highlighting the integration of human detection and pose estimation techniques to improve fall detection accuracy and efficiency. Related work contains a brief presentation of relevant emerging approaches. The Materials and Methods describes the methodologies and tools utilized in this study, including materials, data collection and preprocessing, the Fall Detection Algorithm for pose estimation, and the Alert System developed for achieving the paper's objectives. The Results section presents the analysis of the performance of the system and the benefits of using a real-time fall detection system, particularly in the context of elderly care. The Discussion and Conclusions contain a brief description of the findings and their implications, limitations of the work, and future research directions.
## 4 The Proposed Two-Step Access and Handover Strategy
### Utility Function-Based GEO/LEO Satellite Network Selection  
The first step of the strategy is the selection between the GEO satellite and LEO satellites. The utility function is introduced to process the delay and load status in this step.  
In decision making, utility refers to the satisfaction of a commodity or service provided to decision makers [28]. Decision makers with different preferences will have different utility values for the same service. The utility function is a mapping transformation of decision attributes according to attribute utility. In the decision-making design of network selection, the utility function is used to process the decision attributes, which can not only better show the users demands for a certain attribute, but also drive a normalized value [29].
## 5 Interface
### Option Flags  
The vector nf contains 12 components, each representing an option with the default value zero and the following functionality:  
nf(1) Time series centering:  
= 0 No centering, = 1 Steady-state (for empirical covariance matrices), = 2 Final state, = 3 Arithmetic average over time (for empirical Gramians), = 4 Root-mean-square over time, = 5 Mid-range over time.  
nf(2) Input scale sequence:  
= 0 Single scale: un \(\leftarrow\) un, = 1 Linear scale subdivision: um \(\leftarrow\) un * [0.25, 0.5, 0.75, 1.0], = 2 Geometric scale subdivision: um \(\leftarrow\) un * [0.125, 0.25, 0.5, 1.0], = 3 Logarithmic scale subdivision: um \(\leftarrow\) un * [0.001, 0.01, 0.1, 1.0], = 4 Sparse scale subdivision: um \(\leftarrow\) un * [0.01, 0.5, 0.99, 1.0].  
nf(3) Initial state scale sequence:  = 0 Single scale: xm \(\leftarrow\) xm,  = 1 Linear scale subdivision: xm \(\leftarrow\) xm * [0.25, 0.5, 0.75, 1.0],  = 2 Geometric scale subdivision: xm \(\leftarrow\) xm * [0.125, 0.25, 0.5, 1.0],  = 3 Logarithmic scale subdivision: xm \(\leftarrow\) xm * [0.001, 0.01, 0.1, 1.0],  = 4 Sparse scale subdivision: xm \(\leftarrow\) xm * [0.01, 0.5, 0.99, 1.0]. nf(4) Input directions:  = 0 Positive and negative: um \(\leftarrow\) [-um, um],  = 1 Only positive: um \(\leftarrow\) um. nf(5) Initial state directions:  = 0 Positive and negative: xm \(\leftarrow\) [-xm, xm],  = 1 Only positive: xm \(\leftarrow\) xm. nf(6) Normalizing:  = 0 No normalization,  = 1 Scale with Gramian diagonal (see [82]),  = 2 Scale with steady-state (see [34]). nf(7) Non-Symmetric Cross Gramian, only \(W_{X}\), \(W_{Y}\), \(W_{J}\):  = 0 Regular cross Gramian,  = 1 Non-symmetric cross Gramian. nf(8) Extra input for state and parameter perturbation trajectories, only \(W_{O}\), \(W_{X}\), \(W_{S}\), \(W_{I}\), \(W_{J}\):  = 0 No extra input,  = 1 Apply extra input (see [83]). nf(9) Center parameter scales, only \(W_{S}\), \(W_{I}\), \(W_{J}\):  = 0 No centering,  = 1 Center around arithmetic mean,  = 2 Center around logarithmic mean. nf(10) Parameter Gramian variant, only \(W_{S}\), \(W_{I}\), \(W_{J}\):  = 0 Average input-to-state (\(W_{S}\)), detailed Schur-complement (\(W_{I}\), \(W_{J}\)),  = 1 Average input-to-output (\(W_{S}\)), approximate Schur-complement (\(W_{I}\), \(W_{J}\)). nf(11) Empirical cross Gramian partition width, only \(W_{X}\), \(W_{J}\):  = 0 Full cross Gramian computation, no partitioning. < N Maximum partition size in terms of cross Gramian columns. nf(12) Partitioned empirical cross Gramian running index, only \(W_{X}\), \(W_{J}\):  = 0 No partitioning.  > 0 Index of the set of cross Gramian columns to be computed.
## 3 Resource Allocation Based on a Multi-Agent Reinforcement Learning Algorithm
### Formulation of Rewards  
The designing of well-balanced rewards becomes essential for successfully addressing the optimization challenges (13). In the considered scenario, each CV agent attempts to utilize the accessible subchannel resource for two distinct purposes: (i) To minimize the AoI by updating the communication to the RSU; (ii) To reliably deliver the CAM messages to the FVs. The local reward for each platoon \(i\) is given as  
\[r^{i}_{loc}=\underbrace{\{\mathsf{w}_{1}\mathsf{N}_{i}^{r}/\mathsf{N}_{i}\}} _{intra-platoon}\underbrace{\mathsf{-w}_{2}Aol_{i}^{t}+\mathsf{w}_{3}G( \mathcal{C}_{i,\mathsf{c}}^{t}-\mathcal{C}_{i,\mathsf{c}}^{min})}_{inter-platoon} \tag{25}\]  
In Equation (25), weighting parameters represented as \(\mathsf{w}_{1}\)-\(\mathsf{w}_{4}\) are applied to optimize the rewards. The optimization of rewards is formulated to reflect the constraints of the objectives. The initial component corresponds to the rewards obtained for communicating in intra-platoon mode, and the later component corresponds to the rewards for the inter-platoon communication mode. Similarly, the global reward can be formulated as  
\[r^{i}_{glo}=-\frac{1}{P}\sum_{i\in\mathcal{P}}\sum_{b\in\mathcal{B}}\log_{10} \{l_{i}^{t}[b]\} \tag{26}\]  
The inclination of platoons to discover the subchannels that elicit minimized interference toward other platoons motivates the formulation of global rewards focusing on approximating the mean interference. The revenue \(R\) is desired to be a positive integer indicated by a piecewise constant function \(G(x)\) in Equation (25). \[G(x)= \begin{cases}R,&x\geq 0\\ 0,&x<0\end{cases}
## 5 Experiment Results and Discussion
### Contrasting Physical Experiment Results against Finite Element Analysis Outcomes  
In order to test the accuracy of the experiment results, a finite element simulation was carried out on the sandstone specimen using the common FEM software Abaqus for the cross-check process. Damage evolution was not considered in the simulation considering the fragility of sandstone materials and the short periods of experiments. The main purpose of the cross-check process was to verify the reliability of strain patterns obtained from DIC by qualitatively examining the consistency between the corresponding strain patterns and trends received from the two methods: DIC analysis and Abaqus simulation.
## 2 Coupling of Chromosomal Gene Order and Transcription  
The continuous information underpinning the operation of the bacterial growth cycle is apparently of a hereditary nature and, therefore, it must be encoded (either itself or its mechanism of production) in the cellular genome. In order to vary concertedly with bacterial growth, this information also has to be dynamic. The genetic information encoded in the form of unique genes is of a discontinuous or digital nature and is static. Therefore, it cannot readily satisfy the demands of continuity and dynamics. However, during the _E. coli_ growth cycle the genes, especially those encoding the major regulators of growth and adaptation, are expressed in temporal succession corresponding to their spatial order along the chromosomal replication origin-to-terminus (OriC-Ter) axis. The latter was shown to serve as a coordinate system for genetic regulation [6; 7]. Thus, although the underlying gene order is static, the gene expression is subject to control by a dynamic, continuous variable determining the sequential, chromosomal position-dependent gene expression pattern. In this article, we argue that the continuous variable governing the patterns of gene expression at the most fundamental level is the changing genomic distribution of the effective DNA superhelicity [8], defined as the torsional energy available for the untwisting of gene promoters and transcription initiation.  
The bacterial chromosome (aka nucleoid) has been proposed to behave as a \"smart polymer\", capable of undergoing large conformational transitions (e.g., reversible collapse) in response to small changes in environmental factors such as pH, temperature, or ionic strength [9]. Furthermore, the nucleoid was shown to undergo cycles of compaction-decompaction under the manipulation of crowding conditions [10], in line with the proposed switch-like conformational transition model for chromatin folding [11]. The regulatory role for \"pre-programmed\" phase transitions [9] was suggested by observations indicating that the nucleoid structure and gene expression are interdependent [6; 12; 13; 14; 15; 16]. However, the tight coupling of chromosome structural transitions with gene expression dynamics appears at variance with the notion of gene expression as a fundamentally \"noisy\" stochastic process [17]. The deterministic average gene expression pattern observed at the cell population level emerges gradually from noisy single-cell expressions [18]. Yet, even though the gene expression patterns in individual bacterial cells might appear stochastic, the chromosomal gene order is not. Furthermore, temporally the gene transcription is correlated with the gene order along the OriC-Ter axis at the population level [12; 19] and with the timing of gene replication at a single-cell level (see below).
## 2 Introduction of Analysis Methods
### Method of Modeling Structural Stressing State  
The stressing state of a structure is the description and reflection of inner or outer response characteristics featured by the entire structure or a segment of the whole (components, joints, et al.) with certain parameters under specific loads. It can be interpreted as the structural working behavior characterized by the numerical mode consisting of the mechanical responses of all concerned points. This numerical mode is referred to as the stressing state mode. Structural responses, like strains or stress, are the most direct reflection of the working behavior and deformation of the structure, but are considered directional and limiting. Therefore, to express and model the stressing state mode of arch structures, the strain energy density (SED) was applied [25; 26]. This parameter is scalar, depending on stress and strains but evading the directivity effect.  
With the application of NSF method, strains were not restricted to the limited ones measured in the experiment, so the SED of the arch structure under a certain load was calculated as the sum of SEDs of all elements by Equation (3):  
\[E_{j}=\int_{A}\mathrm{ed}A=\sum_{A}e_{i}A_{i},\ e_{i}=\int_{0}^{e_{i}}\sigma \mathrm{d}\varepsilon \tag{3}\]  
where \(E_{j}\) is the generalized strain energy density (GSED) sum of the arch at load \(F_{j}\) and \(e_{i}\), \(A_{i}\), and \(\varepsilon_{i}\) are the SED value, area, and strain of \(i\)-th element, respectively. Then, \(E_{j}\) is normalized as \(E_{j,\mathrm{norm}}\) by Equation (4) to plot the \(E_{j,\mathrm{norm}}\)-\(F_{j}\) (denoted as \(E\)-\(F\) for convenience) curve for characterizing the stressing state mode of the arch:  
\[E_{j,\mathrm{norm}}=\frac{E_{j}}{E_{\mathrm{M}}} \tag{4}\]  
where \(E_{j,\mathrm{norm}}\) is the normalized SED sum at load \(F_{j}\) and \(E_{\mathrm{M}}\) is the maximum SED sum over the whole loading process.  
The stressing states of arch structures are bound to evolve with increasing load and reflect different stressing state characteristics at different loading stages. As long as the load reaches a certain level, the structural stressing state may present the qualitative leap characteristic from the quantitative change. As a trend analysis tool in hydrometeorology, the Mann-Kendall (M-K) criterion [27; 28; 29] has been found to well-detect characteristic loads from the \(E\)-\(F\) curve without the necessity for statistic samples to comply with certain distributions or interference of a few outliers. The effectiveness of this method has been previously verified [30]. Hence, the leap working behavior characteristic of arches can be detected approximately by utilizing the M-K criterion, and two characteristic points of the _E-F_ curve can be judged from the calculation.
## Methodologies
### Experimental Program  
It is not necessary to use ANN to model the effect of RA on the properties of RAC when only one type of RA is used, since in this case the complexity of RA cannot be reflected and the predictive ability of ANN is generally no better than that of traditional methods like regression analysis. When RAs from different sources were used, ANN models, which are more capable of modeling complex non-linear relationships, may be more suitable for predicting the hardened properties of RAC. The published data of RAs used can be divided into two cases: (1) several types of RAs used by a single researcher; (2) the data of RAs from different literature sources. The factors that influence the properties of RAC and used as the input variables of the networks in the two cases are quite different. For the former case, the types of materials other than aggregate, specimen size and operator error are essentially the same, so only the mix proportions and RA characteristics are chosen as the input variables; for the latter case, on the other hand, in addition to the mix proportions and RA characteristics, more factors such as cement type, specimen size, etc. should be included to establish a generalized model. In this paper, only the first case is considered. The second case will be dealt with in a separate paper. (1) The source of the data  
As introduced above, experiments on the mechanical properties of RAC with different RAs were carried out in the laboratory, which had 46 concrete mixes and were divided into 3 groups. The properties of these aggregates are shown in Table 1. Except for the chosen aggregate characteristics, the particle size was also listed in Table 1 for different aggregate types. The details of the mixes and the corresponding hardened properties of the concrete prepared are shown in Tables 2 and 3, respectively. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sources**} & **Aggregate** & **Particle Size** & **SGSD** & **W\({}_{\mathbf{a}}\)** & **M\({}_{\mathbf{c}}\)** & **ACV (\%)** & **TFV (KN)** & **m** & \(\delta\) & \multirow{2}{*}{**FM**} \\ \cline{2-2} \cline{6-10}  & **Type** & **mm** & **g/cm\({}^{3}\)** & **\%** & **\%** & **10-14 mm** & **10-14 mm** & **\%** & **\%** & \\ \hline \multirow{4}{*}{Source 1} & NA1 & 20 & 2.6 & 1.01 & 0 & 21.7 & 155 & 0 & 0 & / \\  & RA1 & 20 & 2.48 & 3.36 & 21 & 22.5 & 143 & 1.4 & 0.4 & / \\ \cline{1-1}  & RA2 & 20 & 2.36 & 6.14 & 35.1 & 23.4 & 133 & 2.9 & 1.1 & / \\ \cline{1-1}  & RA3 & 20 & 2.36 & 6.44 & 62 & 23.9 & 127 & 1 & 0 & / \\ \cline{1-1}  & FNA1 & 5 & 2.63 & 0.94 & 0 & / & / & 0 & 0 & 2.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Properties of aggregates.