esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Method
### Fault Diagnosis Method
#### 4.4.2 PCA-SOM Algorithm Flow
 \tag{36}\]  
(4) SOM clustering  
(1) Weight vector initialization  
After PCA feature dimensionality reduction, the weight vector matrix \(w_{l}(n)\) between the real-time sample vector \(\hat{x_{k}}\) and the Lth neuron of the output layer in the period of \((t_{k},t_{k+1})\) is, after n updates,  
\[w_{l}(n)=[w_{l1}(n),w_{l2}(n),\ldots\cdots w_{lD}(n)] \tag{37}\]  
After random replication and normalization of L weight vectors in the output layer, the initial superior domain \(M_{l}(0)\), the initial learning rate \(\mu_{l}(0)\), and the initial weight vector \(w_{l}(0)\) are determined. (2) Look for winning neurons  
The real-time sample vector \(\hat{x_{k}}\) after dimensionality reduction is combined with the weight vector \(w_{l}(n)\) of each neuron in the output layer to obtain \(d_{l}\), in which the neuron with the largest inner product \(d_{l}\) is the winning neuron. The winning neuron can be obtained by calculating the minimum Euclidean distance, so the inner product \(d_{l}\) can be improved to  
\[d_{l}=\|\hat{x_{k}}-w_{l}(n)\|=\sqrt{\sum_{\text{d}=1}^{\text{D}}(\hat{x_{kd} }-w_{ld}(n))^{2}} \tag{38}\]  
(3) Adjust the winning areas  
Taking the winning neuron as the center, the winning domain \(w_{l}(n)\) is adjusted to determine the winning region. A variety of distance functions can be used to determine the winning field, and common ones such as Euclidean distance functions are used in this paper. (4) Adjust the weight value  
Adjust the weight vector of all neurons in the winning domain and update the formula as follows:  
\[w_{l}(n+1)=w_{l}(n)+\mu_{l}(n)(\hat{x_{k}}-w_{l}(n))=(1-\mu_{l}(n))w_{l}(n)+ \mu_{l}(n)\hat{x_{k}} \tag{39}\]  
(5) End the iteration  
When the learning rate \(\mu_{l}(n)\) decays to the preset threshold, the SOM neural network can be trained and the optimal weight vector \(w_{l}^{*}\) of each neuron in the output layer can be obtained. (6) Output cluster analysis results
## 4 Role of DAPG-Producing _Pseudomonas_ spp. in Natural Soil Suppressiveness  
### Take-All Decline  
Take-all is an important root disease of wheat caused by the ascomycete _Gaeumannomyces tritici_ (formerly _Gaeumannomyces graminis_ var _tritici_). This soil-borne pathogen primarily infects wheat, but it can also cause root rot in other _Poaceae_, such as triticale, barley and rye [79; 80]. Because of its relative inability to survive for a long period of time in the soil without a host, growing a non-host crop, such as oats, for one or two years can effectively control this disease [81]. An alternative to crop rotation is to grow wheat and barley continuously, which leads to a spontaneous decline in take-all occurrence and severity over time, a phenomenon known as take-all decline (TAD). TAD represents one of the best examples of induced specific suppression, and this field phenomenon occurs across the world [4; 82]. It is defined as \"the spontaneous decrease in the incidence and severity of take-all that occurs with monoculture of wheat or other susceptible host crops after one or more severe outbreaks of the disease\" [82]. Take-all decline is usually achieved after 4-6 years of monoculture of wheat and barley in the same fields [82]. The suppressiveness associated with TAD soils can be transferred to conducive soils by mixing a small amount of TAD soil with conducive soil. TAD suppressiveness can be reduced by growing a non-host plant (oats) and it can be eliminated by soil pasteurization/fumigation [82].  
The role that DAPG-producing _Pseudomonas_ spp. play in the natural suppressiveness associated with TAD soils was demonstrated in a series of experiments conducted with several TAD soils from the Washington State (USA) and the Netherlands. These soils were compared with local conducive soils [83; 84]. Several lines of evidence indicate that DAPG-producing _Pseudomonas_ spp. play a preponderant role in take-all decline. First, DAPG-producing _Pseudomonas_ spp. were detected in TAD soils at population density greater than \(10^{5}\) CFU per gram of root, which represents the threshold required for take-all control under controlled conditions [83; 84; 85]. DAPG-producing _Pseudomonas_ spp. were not, however, detected in conducive soils or were detected at population densities below the threshold required for the control of take-all [83; 84; 85]. Secondly, the introduction of a DAPG-producing _Pseudomonas_ strain in a conducive soil at sufficient levels resulted in take-all control equivalent to the natural disease suppression found in TAD soils [83; 84]. By contrast, the introduction of a mutant impaired in DAPG production did not confer to the conducive soil a level of disease suppression equivalent to TAD soils [84]. Finally, DAPG was isolated from the rhizosphere of wheat colonized by DAPG-producing _Pseudomonas_ spp. [86], and the take-all pathogen was shown to be highly sensitive to DAPG [87].
# A Four-Stage Mahalanobis-Distance-Based Method for Hand Posture Recognition
## 3 Proposed Method
### Stage I  
We consider a hand skeleton as a set of \(N\) joints (knuckles) where each joint \(P_{i}\) is described by three coordinates: \(P_{i}^{x}\), \(P_{i}^{y}\), \(P_{i}^{z}\) calculated based on a color image using the MediaPipe library. The skeletons are in a clockwise coordinate system in which the horizontal \(X\) axis is directed to the left, the vertical \(Y\) axis is facing up, and the \(Z\) axis coincides with the optical axis of the camera and is turned towards the observed objects. All 21 joints and their descriptions are presented in Figure 2.  
The first stage of the proposed recognition method involves initial processing of hand skeletons to make the recognition invariant to hand size, location, and orientation around the Z axis perpendicular to the camera lens. The size invariance is achieved by dividing all coordinates of each joint by the sum of distances between joints 0 and 9, and between joints 9 and 10. We decided to include not only the segment representing palm size (0-9) but also the segment representing the longest finger length (9-10). Our motivation to normalize hand size according to both segments was the observation that the fingers-to-palm ratio varies between people and the differences can be significant [39]. The decision to exclude the segments between joints 10-11 and 11-12 was made upon the observation that these joints are often imprecisely calculated because they are occluded by other hand parts in some gestures.  
The invariance of the hand location is achieved by translating the whole skeleton so that joint 0 is always at (0, 0, 0) m coordinates. The final step of data preprocessing involves making the method invariant to the orientation of the hand around the Z axis by rotating the whole skeleton so that the segment between joints 0 and 9 is vertical. It is important to verify whether among the recognized gestures there exist any whose shape is identical, and the only difference is their orientation. If such gestures exist, the normalization of hand orientation should be omitted.
## 2 Literature Review
[PERSON] proposed a location determination method in a scenario that does not use the comprehensive search [31]. [PERSON] used multi-target signal source matrix data to determine and correct position data [32]. [PERSON] used the Maximization-minimization method to deal with the positioning problem when only two base stations are available [33]. The collected positioning data could be used in the research of many transportation fields. For instance, [PERSON] estimated the length of a vehicle queuing at the intersection through the change of vehicle location information to analyze the road traffic state [34]. [PERSON] proposed a method of collecting the moving speed of vehicles using cellular data positioning [35]. [PERSON] researched the processing methods of the raw data of public transportation vehicle positioning [36]. [PERSON] used vehicle positioning data to analyze the performance of public transportation lines [37]. To analyze the service of public transportation, [PERSON] introduced vehicle positioning data as a basis [38]. [PERSON] used public transportation vehicle positioning data to control traffic lights to achieve the purpose of public transportation priority [39]. [PERSON] perfected the positioning method that combines GPS and GSM methods from the perspective of hardware design and planned to apply the positioning module to locate public vehicles [40]. Furthermore, there are also many scholars concerned about the privacy protection of positioning data and information security. [PERSON] devised a method to protect the privacy of location data [41]. [PERSON] proposed a cache-based mobile positioning data storage and post-processing method [42]. To ensure the safety of positioning data, [PERSON] proposed a positioning data transmission strategy [43]. Asuquo summarized relevant research results on vehicle positioning data security and privacy [44]. With the continuous development of modern urban road construction to three-dimensional structures, when vehicles are driving under tunnels and viaducts, the upper space of the vehicle will be attenuated due to the relatively heavy building structure, which will cause positioning failure. For vehicles using trilateration positioning, since the accuracy of the positioning method is directly related to the base station, when one or more of the required multiple base stations appear to be overloaded or the signal attenuation is excessive due to building interference, this makes the positioning results inaccurate. Most of the existing research is based on the data collected by ideal GPS positioning or multi-base station positioning, and there are few discussions or studies concerning the data collection environments that are relatively harsh. Therefore, it is necessary to design a supplementary positioning method for when the conventional positioning method fails.
# An On-Demand Scheduling-Based MAC Protocol for UW-WiFi Networks
## 5 Experiments and Results
### Experimental Implementation  
A. Simulation Settings  
The simulator used in this work is Aqua-Net Mate [27]. In the simulation scenario, the physical layer parameters are set as follows: the preamble time is 480 ms, and the data rate is 1 kbps, which refers to the commercial off-the-shelf modern. Data packets from all terminal nodes are delivered to BS. In throughput simulations, all terminal nodes, being fixed, are randomly deployed in a circular area with a radius of 3 km centered on BS. Either the number of data packets sent or the number of terminal nodes gradually increases, and the size of data packets generated by each node is set to 544 B. In comparative experiments of the aforementioned three MAC protocols on other performances, the data packet size is fixed to 300 B, and the data packet generation rate of each node gradually increases. In comparative experiments, there are four terminal nodes, in which one terminal node is mobile at a range of 800 to 1131 m, moving back and forth in a square trajectory at a speed of 2 knots, and the other three terminal nodes are at distances of 1500 m, 2000 m, and 3000 m, respectively. The network topology is shown in Figure 4. During the simulations, the packet generation rate \(\lambda\) is from 0.02 to 0.2 pkt/s, and each set of experiments runs lasting 1 h. Finally, various performance results are computed through the stored logs.  
The simulated environment is the coastal ocean area, with a depth 10-30 m. For the sake of making the simulation settings closer to the real situation, we have carried out many sea trials on point-to-point communication in offshore area of Shenzhen and summarized the results of communication success ratio (CSR) at different distances as shown in Table 1. Then the ocean trial results in Table 1 are used as the CSR for different ranges in the simulations.
###### Abstract  
The purpose of this study was to clarify the relationship between vertical ground reaction force (VGRF) and acceleration from wearable inertial measurement units (IMUs) during single-leg drop landing after anterior cruciate ligament reconstruction (ACLR). Twenty-six participants, \(42.4\pm 5.3\) weeks after ACLR, performed three single-leg drop landing trials bilaterally. The peak VGRF was assessed using a force plate. The resultant acceleration was calculated using IMUs attached to the shank, thigh, and lumbar region. Univariate regression analysis was performed to examine the linear relationship between the VGRF and resultant acceleration. The limb symmetry index (LSI) of the VGRF was linearly associated with the LSI of the resultant accelerations at the shank, thigh, and lumbar sensors (\(R^{2}=0.166\), \(p=0.039\); \(R^{2}=0.525\), \(p<0.001\); and \(R^{2}=0.250\), \(p=0.009\), respectively). In the involved limb, only the resultant acceleration at the thigh was a significant predictor of VGRF (\(R^{2}=0.490\), \(p<0.001\)), whereas in the uninvolved limb, the resultant accelerations at the shank, thigh, and lumbar sensors were significant predictors of VGRF (\(R^{2}=0.245\), \(p=0.010\), \(R^{2}=0.684\), \(p<0.001\); and \(R^{2}=0.412\), \(p<0.001\), respectively). Caution may be required when using IMUs to predict VGRF asymmetry because the coefficient of determination for predicting VGRF is lower in the involved limb than in the uninvolved limb.  
impact forces; asymmetry; second injury; knee; rehabilitation; return to sports; biomechanics
## 3 Test Results and Discussion
### Strength Characteristics  
To analyze the effect of the end restraint on the strength of the coarse-grained soil, the peak strength values of the coarse-grained soil in the triaxial tests with end-restraint and end-free (microfriction) conditions under different confining pressures were obtained, as shown in Table 2. The end restraint slightly increased the peak strength under the same confining pressure, but when the specimen had the usual height-to-diameter ratio of 2 and was under the drained conditions, the influence of the end restraint on the peak strength of the coarse-grained soil was smaller, not exceeding 10%, a finding which is the same as the conclusion reached by [PERSON] et al. (1978) [PERSON] et al. (1978).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Confining Pressure (MPa)**} & \multicolumn{4}{c}{**Peak Strength (MPa)**} \\ \cline{2-5}  & \multirow{2}{*}{**End Restraint**} & **End Free** & **Peak Strength** & **Error** \\  & & **(Microfriction)** & **Increasing Ratio (\%)** & \\ \hline
0.2 & 0.981 & 0.907 & 7.5 & - \\
0.4 & 1.789 & 1.703 & 4.8 & - \\
0.6 & 2.447 & 2.382 & 2.7 & - \\
0.8 & 3.045 & 2.910 & 4.4 & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Peak strength values of coarse-grained soil under different confining pressures.  
Figure 5: Deviator stress–strain curves of coarse-grained soil under the end-restraint and end-free (microfriction) conditions.  
Soil strength is divided into the categories of linear strength and nonlinear strength. There is a linear relationship between the shear strength and normal stress in the linear strength region of soil. The shear strength can be calculated using the strength index cohesion c and internal friction angle \(\varphi\). There is a nonlinear relationship between the shear strength and the normal stress in the nonlinear strength region of soil. For noncohesive soil, there is no cohesion. The internal friction angle is related to the normal stress. It is expressed by the following equation ([PERSON], 1970) [29]:  
\[\varphi=\varphi_{0}-\Delta\varphi\text{lg}(\sigma_{3}/p_{a}) \tag{1}\]  
where \(\sigma_{3}\) is the small principal stresses, \(p_{a}\) is the atmospheric pressure, \(\varphi_{0}\) is the value of \(\varphi\) when \(\sigma_{3}/p_{a}\) equals \(1\) (\({}^{\circ}\)), and \(\Delta\varphi\) is the reduction value of \(\varphi\) when \(\sigma_{3}\) is increased \(10\) times (\({}^{\circ}\)).  
Figure 6 shows the Mohr stress circles and linear strength envelopes of the coarse-grained soil. Figure 7 shows the Mohr stress circles and nonlinear strength envelopes of the coarse-grained soil. The results were sorted according to the linear and nonlinear strength values. It can be seen that the end restraint slightly increased the linear strength index cohesion \(c\), internal friction angle \(\varphi\), nonlinear strength index \(\varphi_{0}\), and \(\Delta\varphi\) of the coarse-grained soil.
## 3 A Methodology to Differentiate Dissolution from Gas Losses in Gas-Metal Systems
### Boundary Conditions (BC) on a Double Metallic Barrier for the Dissolution of a Diatomic Gas
The pressure decrease inside the container can be inferred with the ideal gas law, and the new pressure is subsequently used to define the Coupled BCs of the next time step. Finally, for scenarios exhibiting double metallic barriers, Co-Dependent BCs must be computed. To do so, two continuity laws must be enforced: for the flux and for the chemical potential. In cases where the Sieverts' constant is similar for both metals, the chemical potential can be approximated to the concentration, which results in profiles as seen in Figure 3. In it, equating the fluxes exiting Metal 1 and entering Metal 2 leads to a discontinuity on the first derivative of the concentration profiles (blue lines), caused by the difference in diffusivities between metals. However, in cases where the Sieverts' constant varies significantly across the interface, the profiles themselves also present a discontinuity due to the different equilibrium distribution coefficients of each metal [42]. In these cases Equation (7) is used (alongside the flux continuity) to derive the concentration values _at the interface_, which act as the Co-Dependent BCs for each diffusion sub-channel. \[\frac{c_{X_{1}}^{M_{1}}}{c_{X}^{M_{2}}}=\frac{\mathcal{S}_{X_{2}}^{M_{1}}}{ \mathcal{S}_{X_{2}}^{M_{2}}} \tag{7}\]  
The derived concentration values (i.e., Dirichlet BCs) can be re-calculated at each time step using the fluxes obtained at the previous one. This implies a non-implicit algorithm scheme; simulation stability and negligible error can then be ensured by increasing the number of space/time nodes. Figure 4: Diagram of BC determination at the gas-metal interface for a bulk-controlled single metallic barrier, when experiencing on both sides an infinite supply of diatomic gas at given temperature \(T\) and pressure \(p_{X_{2}}\). (i) Calculation of gas density (\(c_{X_{2}}\)) at the metal surface with the ideal gas law. (ii) Calculation of the surface atomic concentration (\(c_{X}\), in _green_) by multiplying the gas density by the number of atoms that compose the gas molecules, due to dissociation. (iii) The effective fixed BC applied is the minimum between the atomic concentration and Sieverts’ concentration (\(c_{X}^{\star}\)) for that gas pressure, obtained using Sieverts’ constant (\(\mathcal{S}_{X_{2}}^{M}\)). This BC leads to concentration profiles evolving in time (in _blue_, from light to dark). The particular case of \(c_{X}>c_{X}^{\star}\) is represented; in equilibrium, the concentration inside the bulk tends to \(c_{X}^{\star}\) (in _black_, dashed).
## 5 Numerical Examples
As with the increases in the manufacturer's unit sales price, it raises the manufacturer's optimal equilibrium technology investment for reducing carbon emissions and total profit, as well as the retailer's optimal equilibrium selling price, but lowers the manufacturer's optimal equilibrium shipping quantity and the retailer's optimal equilibrium order quantity, which results in less retailer's total profit with carbon emissions from the manufacturer and the retailer. 3. The manufacturer's higher ordering cost of raw material lowers the manufacturer's optimal equilibrium technology investment for reducing carbon emissions and total profit but increases the manufacturer's optimal equilibrium shipping quantity, retailer's optimal equilibrium order quantity and total profit, and carbon emissions from the manufacturer and retailer. The manufacturer's setup cost, unit raw material cost, and unit production cost have the same impact trends on the results as the manufacturer's ordering cost of raw material. 4. Increases in the manufacturer's holding cost of raw material or finished goods results in higher manufacturer's optimal equilibrium technology investment for reducing carbon emissions but lower manufacturer's optimal equilibrium shipping quantity, retailer's optimal equilibrium selling price, and order quantity that correspondingly decrease the total profits of the manufacturer and the retailer, as well as the carbon emissions from the manufacturer and retailer. 5. As to the deterioration rate of raw material, the impact on the optimal equilibrium solutions is the same as the deterioration rate of finished goods, except on the retailer's selling price. Faster deterioration rate of finished good positively impacts the retailer's optimal equilibrium selling price, whereas the deterioration rate of the raw material decreases the retailer's optimal equilibrium selling price. 6. An increase in the amount of raw materials for the finished goods lessens the manufacturer's optimal equilibrium technology investment and shipping quantity and the retailer's optimal equilibrium selling price and order quantity, resulting in lower total profits of both players. Meanwhile, the manufacturer's carbon emissions increase whereas that of the retailer's decrease as the amount of raw materials for the finished goods increases. 7. It is obvious that changes in manufacturer's parameters except for its unit sales price are relatively insensitive to the optimal equilibrium solutions. Especially in the retailer's optimal equilibrium selling price, the degree of impact is very small.
# Monitoring and Data Analysis in Small-Scale Landslide Physical Model
## 3 Review of Landslide Monitoring Equipment  
By definition, a movement is a term that characterizes landslides [38]. That is why a movement distribution along a slope is the most important data for all landslide analyses [39] and presents an important object of landslide monitoring. According to [PERSON] [40], the types of monitoring systems and techniques can be divided into groups as follows: (i) remote sensing or satellite techniques; (ii) photogrammetric techniques; (iii) ground-based geodetic techniques; (iv) satellite-based geodetic techniques; and (v) geotechnical techniques. Selection of instrumentation and methods for landslide monitoring system largely depends on movement types and deformation as well as the purpose of the monitoring campaign [40]. Monitoring system should be suitably designed for the type of experiment being performed, and equipment installed at the correct locations to take into account the possible positions of the sliding zone. Monitoring system established in a physical model follows the principles used in observation of real landslides, and it consists of a geodetic monitoring system and geotechnical monitoring system [39]. Design of the monitoring system greatly depends on the experience of the users and the needs of the experiment. Although most of the equipment is miniature in a size comparison with in situ equipment for landslides, it depends on the projected size of the flume model. In Table 3, the summary and purpose of equipment used in small-scale physical models are provided along with a literature review.