esa-sceva/satcom-chunk-collection huggingface dataset:
# Fall Detection Approaches for Monitoring Elderly HealthCare Using Kinect Technology: A Survey
## 4 Kinect-Based Fall Detection Approaches
### Feature Extraction  
Measuring human activity leads to collecting a large amount of essential data. Working with the smallest number of values that characterize the relevant aspects of human movement is recommended in order to achieve the most excellent potential performance for the fall detection system. These values are called characteristics, features, descriptors, or judging criteria. Scientific researchers always select relevant descriptors. Characteristics are established during the formulation of objectives, with the primary goals of diminishing error rates, mitigating the risk of model overfitting, and regulating the response time. This is particularly critical in detecting falls, as falls are typically marked by biomechanical shifts resulting in a loss of dynamic balance in the elderly [45].  
A cycle of movements is repeated during a fall before the body reaches the ground. In particular works, the fall has been considered as a spatiotemporal study. For this, researchers have defined a study reference system such as a spatiotemporal coordinate system, allowing the complete characterization of the fall. They analyze events over time, characterized by trajectory, speed, and acceleration. Using a single parameter type does not lead to a reliable system that distinguishes the different types of actions. For example, according to the speed variation alone, the fall detection approach leads to an incorrect classification of the fast stretch on the ground. In other words, the approach detects may daily activity as a fall. Fall detection can be reduced to a simple study of the distance between precise joints and the ground. Among the drawbacks of the evolution of the joint's position, we cite the stop receiving relevant data, especially the occlusion case. This last implies an increase in the rate of false alarms. In addition, during the fall cyclethe body is sometimes represented by its center of mass. The center of mass is estimated using geometric center of the silhouette or an articulation such as the hip center. The fall aspect is sometimes explored by kinematic parameters such as the angle of inclination, which makes it possible to analyze the body's direction and flexion relative to the ground or its initial posture. In other cases, mathematical calculations are applied to the pixels of a frame to derive local visual characteristics. Among these characteristics are the histogram of oriented gradients (HOG), motion history image (MHI), curvature scale space (CSS), wavelet moment, and depth range difference. These features facilitate the interpretation of a person's movement in the field of vision. On the other hand, the main drawback is the processing time of the image data. The fusion of kinematic and visual data with variations in displacement or velocity over time has shown effectiveness in having a global description of all the issues involved, and the time can be used as an additional characteristic to reinforce the decision.
## 2 Materials and Methods
### Laser Flash Photolysis and Transient Absorption Spectroscopy  
The nanosecond laser flash photolysis and transient absorption (TA) spectroscopy apparatus was as described with the following details. Excitation laser pulses (7 ns, 10 Hz) at the wavelength of 445 nm were supplied by an optical parametric oscillator (OPO) driven by an Nd: YAG laser (Quanta-Ray Pro-Series, Spectra Physics Lasers Inc., Mountain View, CA, USA), and pulse energy was attenuated to 0.12 mJ/pulse. The desired probe wavelengths (400-900 nm) were provided by a laser-driven white light source (LDLS-EQ-1500, Energetic Technology, Inc., Woburn, MA, USA). Kinetics were detected with a photodiode (model S3071, Hamamatsu Photonics, Hamamatsu, Japan) attached to a spectrograph (SP2500i, Princeton Instruments, Trenton, NJ, USA) when both the excitation light and the probe light were focused on the optical sample cuvette during the experiment. The signals were stored and averaged with a digital storage oscilloscope (bandwidth of 500 MHz, Teledyne LeCroy HDO 4054, Chestnut Ridge, NY, USA) connected to a personal computer. The original collected data provided single wavelength kinetics, and parameter fitting was based on MATLAB 5.3 (MathWorks) software.  
Unless specified, the concentrations used in this study were as follows: riboflavin (I \(\times\) 10\({}^{-5}\) M), tryptophan (1.0 \(\times\) 10\({}^{-4}\) M), and plant phenols (2.0 \(\times\) 10\({}^{-4}\) M), all prepared in 0.01 M phosphate-buffered saline (PBS). All measurements were carried out in a thermostated room (23 \(\pm\) 1 \({}^{\circ}\)C).
# Transformer-Based Visual Object Tracking with Global Feature Enhancement
## 3 The Proposed Method
### Attention Enhancement Feature Fusion Network
#### 3.3.3 Enhanced Cross-Attention Module  
The enhanced cross-attention module is proposed to boost the feature information of the object area and improve the matching accuracy by reweighting the attention weights. As shown in Figure 4, \(X_{FSA}\) from fast self-attention are taken as the inputs of the ECA module. The inputs \(X_{kv}\) and \(X_{q}\) are fused by a multi-head cross-attention with a residual form. Similar to the self-attention module, spatial position encoding \(P_{x}\) is also used to enhance the cross-attention module. Additionally, a feed-forward network (FFN) is applied to enhance the fitting capability of the ECA module. The FFN can be computed by Equation (6):  
\[FFN(x)=max(0,xW_{1}+b_{1})W_{2}+b_{2}, \tag{6}\]  
where \(x\) is the input feature vector, \(W_{1}\) and \(W_{2}\) denote the weight matrix of the first and second layers, respectively. Thus, \(b_{1}\) and \(b_{2}\) are the corresponding bias vectors. The overall computation formulas for the enhance cross-attention modules are as follows:  
\[X_{ECA}=\tilde{X}_{ECA}+FFN(\tilde{X}_{ECA}), \tag{7}\]  
\[\tilde{X}_{ECA}=X_{q}+Output, \tag{8}\]  
Figure 4: The ECA module. The inputs, \(X_{q}\) and \(X_{kv}\), are the features obtained by FSA module from two different branches in attention enhancement fusion network.  
\[Output=\left\{\begin{array}{l}softmax(\frac{(X_{q}+P_{q})(X_{kp}+P_{kq})^{T}}{ \sqrt{d_{k}}})X_{kp}\times rate,\\ if\quad softmax(\frac{(X_{q}+P_{q})(X_{kp}+P_{kq})^{T}}{\sqrt{d_{k}}})X_{kp}>0.7; \\ softmax(\frac{(X_{q}+P_{q})(X_{kp}+P_{kq})^{T}}{\sqrt{d_{k}}})X_{kp},\\ if\quad softmax(\frac{(X_{q}+P_{q})(X_{kp}+P_{kq})^{T}}{\sqrt{d_{k}}})X_{kp} \leq 0.7;\end{array}\right. \tag{9}\]  
where \(X_{q}\) and \(X_{kp}\) are the two inputs of the ECA module, \(P_{q}\) and \(P_{kq}\) are the corresponding spatial position encoding. The attention scores of ECA can be calculated by the element-wise product of \(X_{kp}\) and \(X_{q}\), as shown in Equation (9). When the scores in the attention map exceed 0.7, the scores are multiplied by a pre-defined coefficient to accomplish the enhancement of cross-attention. Firstly, the average attention level for each position in the search feature sequence is calculated, resulting in average attention weights. Then, the positions in the attention map are sorted based on their attention scores, and positions with high attention scores (exceeding 0.7) are reweighted by multiplying a rate to increase the weights of highly attentive positions by an additional 30%. The weights of other positions in the attention map are kept unchanged. Finally, the reweighted feature sequences are concatenated together to enhance the matching accuracy. Thus, \(X_{kp}\) is reweighted based on the reweighted attention scores, and the results are added to \(X_{q}\) to enhance the representation capability of the feature map. \(X_{ECA}\in R^{N_{q}\times d}\) is the output of the enhance cross-attention module.
## 5 Numerical Experiments and Analysis of Results
### Solving the Standard TOP Without Obstacles or Environmental Factors  
To establish a performance baseline, we solved the simple version of the problem (without obstacles or environmental factors) using Gurobi, a commercial optimization solver. Two different instance sets were considered: a 21-node instance with two UAVs and a 31-node instance with three UAVs. For the 21-node instance with two UAVs, each with a speed of 1.1 km/min and a maximum route duration of 50 min, the solver identified an optimal solution with a total reward of 360.0. Figure 9 illustrates the optimal routes identified by Gurobi.  
The optimal routes for the UAVs are as follows: drone 1 route is [1; 8; 6; 2; 4; 12; 10; 18; 5; 21], with total distance \(=50.81\) km, travel time = 46.19 min, and route reward = 175.0; drone 2 route is [1; 19; 11; 9; 3; 7; 15; 13; 21], with total distance = 53.99 km, travel time = 49.08 min, and route reward = 185.0.  
For the more complex 31-node instance with three UAVs, each with a speed of 1.1 km/min and a maximum route duration of 50 min, Gurobi found an optimal solution with a total reward of 543.0. Figure 10 illustrates these optimal routes.  
Figure 8: Base instances with and without obstacles.  
The optimal routes are as follows: drone 1 route is [1, 17, 10, 8, 4, 6, 7, 21, 30, 31], with total distance = 54.99 km, travel time = 49.99 min, and route reward = 163.0; drone 2 route is [1, 3, 25, 14, 23, 13, 11, 29, 5, 31] with total distance = 48.37 km, travel time = 43.97 min, and route reward = 170.0; drone 3 route is [1, 19, 27, 16, 20, 18, 9, 15, 12, 26, 2, 24, 28, 31], with total distance = 54.27 km, travel time = 49.34 min, and route reward = 210.0.  
When applying our proposed A*-based neural network approach to the 21-node instance without obstacles, our algorithm achieved a solution with a total reward of 345.0. The routes generated were as follows: route 1 = [1, 19, 9, 15, 13, 11, 16, 5, 21], with reward = 175.0, and travel time 45.81 min; route 2 = [1, 6, 8, 4, 2, 17, 12, 10, 18, 14, 21], with reward = 170.0, and travel time = 48.33 min.  
Figure 10: Optimal routes for the 31-node instance without obstacles.  
Figure 9: Optimal routes for the 21-node instance without obstacles.  
For the more complex 31-node instance without obstacles, our algorithm achieved a total reward of 503.0. The routes generated by our algorithm are: route 1 = [1, 3, 25, 14, 23, 13, 29, 11, 18, 9, 15, 12, 31], with reward = 238.0, and travel time = 49.93 min; route 2 = [1, 17, 7, 28, 30, 19, 21, 24, 31], with reward = 150.0, and travel time = 43.60 min; and route 3 = [1, 10, 8, 4, 6, 2, 26, 22, 31], with reward = 115.0, and travel time = 45.47 min.
## 1 Introduction
The IWOLA algorithm applies an equivalent Hilbert transform to the normal WOLA filter bank by shifting the centre frequency of every sub-band by half of the frequency bin, such that the IWOLA filter bank provides K/2 independently output complex sub-bands for real-valued signals instead of the general K/2 + 1 sub-bands, thus reducing the subsequent processing units by one set. After the decimation, FFT processing for the decimated data is carried out to perform spectral analysis with an ultra-high-frequency resolution. Furthermore, by selecting only a portion of the output data from the FFT, the interference between the adjacent channels can be eliminated within the entire Nyquist frequency bandwidth. To obtain a higher channelised performance, an optimized algorithm is finally proposed, which uses the IWOLA filter bank and the second-stage WOLA filter bank, instead of the second-stage FFT. The remainder of this paper is organised as follows. In Section 2, the underlying principle and architecture of the proposed IWOLA + FFT algorithm are presented. In Section 3, the characteristics of the IWOLA filter bank and IWOLA + FFT algorithms are detailed, including the channelisation characteristics, spectra performance, and elimination of the overlapping interference between the adjacent channels. To improve performance of the IWOLA + FFT algorithm, the IWOLA + WOLA algorithm is proposed, and its performance analysis results are presented in Section 4. The simulation of the proposedalgorithms and analysis results are presented in Section 5, followed by the conclusions in Section 6.
# Production and Maintenance Planning for a Deteriorating System with Operation-Dependent Defectives
## 6 Sensitivity and Results Analysis
### Influence of the Defectives Cost  
We proceed with the analysis of the sensitivity of the defectives cost. In Figure 10 we analyze the production trace for two different cost values \(c_{d}=3\) and \(c_{d}=12\). From the results we highlight the fact that when the defectives cost is low, for instance \(c_{d}=3\), there is more liberty to operate  
Figure 8: Sensitivity of the backlog cost on the repair/major maintenance policy.  
Figure 9: Sensitivity of the major maintenance cost on the repair/major maintenance policy.  
the machine at increased paces such as \(u_{2}\) and \(u_{max}\), regardless that in such rates the machine deteriorates faster, thus producing even more defectives. Moreover, we observe that when we increase the defectives cost to \(c_{d}=12\), the production threshold \(Z_{1}\) increases, favoring more the use of the machine at a reduced rate \(u_{1}\), (since at this mode the machine decreases considerably its deterioration pace, hence it generates less defectives). Additionally, it is evident that the greater the value of the defectives cost, the less extensive is the use of the machine at faster production rates such as \(u_{2}\) and \(u_{max}\). Because operating at faster production rates, the machine accelerates its deterioration level, generating more defectives.  
To complement the analysis of the defectives cost, we examine its variation for three cost scenarios, \(c_{d}=2,\ 3\) and \(4\) as illustrated in Figure 11. We begin the discussion when the defectives cost is set to a low value of \(c_{d}=2\), in this context, the major maintenance activity is limited to the smallest area in the analysis. If the defectives cost increases to \(c_{d}=3\), the repair/major maintenance trace varies and the area for the major maintenance increases. At increasing the defectives cost to \(c_{d}=4\), minimal repair is less recommended, whereas the major maintenance zone increases even more. These results amount to the observation that the repair/major maintenance policy is highly sensitive to the defectives cost, since at increasing \(c_{d}\), more major maintenance is conducted with the aim to restore the machine faster and reduce considerably the generation of defectives and at the same time maintain a reasonable total cost.  
Figure 11: Sensitivity of the defectives cost on the repair/major maintenance policy.  
Figure 10: Sensitivity of the defectives cost on the production policy. (**a**) \(c_{d}=3\); (**b**) \(c_{d}=12\).
# Machine Learning-Assisted Secure Random Communication System
## 5 Reception
### Legitimate Receiver 'LR'
#### 5.2.2 Pulse Length-Based Classifier  
For legitimate communication between the T and LR, the DT is trained on an \(\alpha\)-SNS\(\mathcal{D}\), which consists of three different values of N. Therefore, any one value of N can be prechosen while carrying out secure communication. Specifically, from the perspective of the LR, the predicted parameter values \(\{\hat{\beta}_{1},\;\hat{\beta}_{2},\;\ldots,\;\hat{\beta}_{N}\}\) carrying a single binary bit are then provided to the pulse length-based classifier. It classifies the incoming \(\{\hat{\beta}_{1},\;\hat{\beta}_{2},\;\ldots,\;\hat{\beta}_{N}\}\) carrying a unit bit to a single classified beta '\(\beta_{C}\)' using the following relation:  
\[\beta_{C}=\;\begin{Bmatrix}\beta_{0}\,;if\sum_{t=1}^{N}\hat{\beta}_{t}<0\\ \beta_{1}\,;if\sum_{t=1}^{N}\hat{\beta}_{t}\geq 0\end{Bmatrix} \tag{11}\]  
where \(\beta_{0}\) represents the retrieval of the bit as '0' and where \(\beta_{1}\) represents the retrieval of the bit as '1'. The procedure is repeated to retrieve all subsequent binary bits.
## 3 Results
### Search for a Possible Reason for the Biological Activity Difference between 311 and B6 Lines
#### 3.2.2 Sequencing T-DNA Insert  
The second hypothesis explaining the difference in biological activity between the B6 and 311 lines was due to a mutation in the insertion sequence.  
Tobacco plants, the founders of the InterB and Inter311 lines, were transformed with the Agrobacteriumium tumefaciens EHA105 strain carrying the pART27-INT6 plasmid [11; 16]. The T-DNA region inserted into the plant genome is limited by short RB and LB (right and left borders) sequences and includes the target sIFNG gene under the control of the 35S promoter and the kanamycin resistance gene (encoding neomycin phosphotransferase II) with the napaline synthase promoter. The nucleotide sequence of plasmid pART27 is not available in the NCBI database, but it is indicated that RB and LB, as well as the 35S promoter and kanamycin resistance gene, were taken from plasmid pGA643 [18], which sequence is known (GenBank: AY804024.1). A pair of primers was selected for each of the functional elements (Table 1) so that the size of the amplified fragment was about 150 base pairs. Thus, using various combinations of primers, the sequence of the transgenic insert was overlapped by fragments of no more than 1000 base pairs (Figure 3).  
Sequencing showed that transgenic inserts in plants 311.2.7.2-2 and B6.13.8-17 (4 th generation) were 98-99% identical to each other and the corresponding T-DNA region of plasmids from the databases. The observed mismatches are highly likely to be sequencing artifacts. Mismatches are recognized as artifacts since they are located in the end regions of the Sanger sequences and do not repeat in case of overlapping sequences. For plants  
Figure 3: The structure of T-DNA from plasmid pART27-INT6 and primers binding sites. p35S—35S promoter; sIFNG—synthetic bovine gamma interferon gene; OCST—octopine synthase terminator; pNOS—nopaline synthase promoter; nptll—neomycin phosphotransferase II gene (Kanamycin resistance); NOST—nopaline synthase terminator.  
Figure 2: sIFNG gene expression in green tissues of individual tobacco plants, lines 311 and b6. Each sample was tested in three technical replicates. Error bars represent standard deviation.  
of 311 line, a slight rearrangement (deletion) in the terminal region of the NOS terminator was found.  
Thus, no differences were found in the T-DNA sequence between plants of the Inter311 and InterB6 lines, which could explain the difference in the biological activity of their extracts.
# Microbes in Health and Disease: Human Gut Microbiota
## 3 Multifunction of the Human Gut Microbiota
### Intestinal Epithelial Homeostasis  
The gut microbiota maintains intestinal epithelial homeostasis [64; 65]. As shown in Figure 4, the intestinal epithelium is a single-cell layer composed of intestinal stem cells and various differentiated cell types (e.g., enteroendocrine cells, goblet cells, enterocytes, Paneth cells, and colonocytes) [64]. The intestinal epithelium provides both physical and chemical barriers to segment gut microbes and the immune system; the physical barrier refers to mucus secreted from goblet cells, the glycocalyx on the microvilli surface of epithelial cells, and the junction between adjacent epithelial cells, whereas the chemical barrier refers to various antimicrobial compounds produced mainly by Paneth cells. [65]. Therefore, the intestinal epithelium constitutes the first line of defense against microbial invasion. The gut microbiota plays crucial roles in maintaining the structural integrity and function of both the physical and chemical barriers of the intestinal epithelium [64]. Specifically, the metabolites (e.g., SCFAs) produced by the gut microbiota serve not only as the primary energy source for colonocyte proliferation, as discussed above, but also as important regulators of both the physical and chemical barriers of the intestinal epithelium. For example, short-chain fatty acids (e.g., butyrate) improve the structural integrity of the intestinal epithelium via upregulating the expression of tight junction proteins (e.g., occludin and zonula occludens-1) [66], which are crucial for the function of tight junctions between adjacent epithelial cells. Butyrate stimulates goblet cells to secrete mucins, which are the major structural protein component of the mucus layer, by upregulating the _MUC_ genes that encode the protein backbone of mucin [67]. Butyrate also stimulates small intestinal Paneth cells to secrete the antimicrobial compound \(\alpha\)-defensin [68] and induces the production of antimicrobial peptides (RegIII\(\gamma\) and \(\beta\)-defensins) in intestinal epithelial cells [69]. Additionally, SCFAs regulate the differentiation of intestinal stem cells and promote turnover of the intestinal epithelium [70; 71]. Germ-free mice exhibit reduced turnover in epithelial intestinal cells in the small intestine, and normal turnover can be restored by oral administration of short-chain fatty acids [70].
## 2 Theory
### THM Coupling Equation of CO\({}_{2}\) Displacement CBM
#### 2.2.1 Stress-Strain Equation  
The coal seam is a heterogeneous porous medium model. The deformation field of coal seam is affected by pore pressure, temperature, and matrix expansion caused by gas adsorption and desorption. The stress is expressed as the strain equation [25; 33; 34; 35; 36]:  
\[\varepsilon_{ij}=\frac{1}{2G}\delta_{ij}-(\frac{1}{6G}-\frac{1}{9K})\sigma_{ kk}\delta_{ij}+\varepsilon_{PY}\delta_{ij}+\varepsilon_{T}\delta_{ij}+ \varepsilon_{PX}\delta_{ij} \tag{1}\]  
where \(G\) is the shear modulus (Pa); \(K\) is the bulk modulus of coal (Pa); \(\varepsilon_{PY}\) is gas strain; \(\varepsilon_{T}\) is thermal expansion strain; and \(\varepsilon_{PX}\) is the strain caused by gas pressure. The equation is as follows:  
\[\left\{\begin{array}{l}G=\frac{E}{2(1+v)}\\ K=\frac{E}{3(1-2v)}\\ \varepsilon_{PY}=-\frac{\alpha\Delta P}{3K_{s}}\\ \varepsilon_{T}=\frac{\beta\Delta T}{3}\\ \varepsilon_{PX}=\frac{2\rho_{c}RTa_{i}}{3V_{m}K}\ln(1+\sum_{i=1}^{2}b_{i}P_{ i})\end{array}\right. \tag{2}\]  
where \(E\) is the [PERSON]'s modulus (Pa); \(v\) is the [PERSON]'s ratio; \(\beta\) is the thermal expansion coefficient (K\({}^{-1}\)); \(V_{m}\) is the molar constant of gas; \(R\) is the universal gas constant (J/mol\(\cdot\)k); \(\delta_{ij}\) is the Kronecker function; \(a_{i}\) is the [PERSON] volume constant (m\({}^{3}\)/kg); \(b_{i}\) is the [PERSON] pressure constant (Pa\({}^{-1}\)); \(P_{i}\) is the gas pressure(MPa); \(\rho_{c}\) is the density of coal (kg/m\({}^{3}\)); and \(T\) is the temperature of the coal (K).  
The strain and displacement components satisfy the Cauchy equation [28; 37]:  
\[\varepsilon_{ij}=\frac{1}{2}\Big{(}u_{ij}+u_{ji}\Big{)} \tag{3}\]  
where \(\varepsilon_{ij}\) is the train component, and \(u_{ij}\) is the displacement component.  
The equilibrium differential equation of coal seam is defined as the Navier-Stokes equation [28; 37]:  
\[\sigma_{ij,j}+F_{i}=0 \tag{4}\]  
The modified equilibrium differential equation is as follows [38]:  
\[\sigma^{\prime}{}_{ij,j}+\Big{(}\alpha P\sigma_{ii}\Big{)}_{,j}+F_{i}=0 \tag{5}\]  
Combined with Equations (1)-(5), the coal stress equation is as follows:  
Figure 1: Schematic diagram of the full coupling of THM for CO2 displacement CBM.  
\[Gu_{i,jj}+\frac{G}{1-2v}u_{j,ji}+\theta_{PY}\Delta P_{j}+\theta_{T}\Delta T_{j}+ \theta_{PX}a_{i}T\Big{[}\ln(1+\sum\
olimits_{i=1}^{2}b_{i}P_{i})\Big{]}_{,i}+ \alpha P_{j}+F_{i}=0 \tag{6}\]  
where \(\theta_{PY}\) is the stress coefficient caused by gas pressure, \(\theta_{T}\) is the coefficient of thermal stress, \(\theta_{PX}\) is the stress coefficient caused by gas adsorption, and \(\alpha\) is the Biot coefficient. The equation is as follows:  
\[\left\{\begin{array}{l}\theta_{PY}=\frac{3\lambda-2G}{3K}\\ \theta_{T}=\frac{3\lambda+2G}{3}\beta\\ \theta_{PX}=\frac{2\rho_{c}R(3\lambda+2G)}{3V_{m}K}\\ \alpha=1-K/K_{s}\\ \lambda=\frac{Ev}{(1+v)(1-2v)}\end{array}\right. \tag{7}\]  
where \(K_{S}\) is bulk modulus of coal skeleton, and \(\sigma_{ij}\) is stress tensor (\(i,j=1,2\)).