esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Literature Review on Manipulator Design and Optimization Methods
### Topology Optimization  
There are also numerous examples in the literature where topology optimization is applied in manipulator design, often in combination with parametric optimization of geometric parameters. For instance, in [17], the upper arm of an industrial manipulator used in welding was optimized by reducing its mass and increasing its natural vibration frequency. In [18], a multi-criteria optimization of an industrial manipulator was carried out to minimize both initial and running costs.  
As shown in [19], a serial manipulator can be optimized by integrating topology optimization at the part level with parametric optimization at the system level. The researchers first developed a stiffness model of the manipulator and then determined typical load values and configurations. Topology optimization was performed on individual parts, while parametric optimization was used to distribute mass across different components.  
According to [19; 20], the most significant mass reduction can be achieved by combining parametric and topology optimization when designing manipulator structures.
# Audio-Visual Speech Recognition Based on Dual Cross-Modality Attentions with the Transformer Model
## 3 Proposed AVSR Method Based on DCM Attention
### Input Features
#### 3.1.2 Video Features  
To prepare visual features that represent sequential lip movements, we crop a 120 \(\times\) 120-pixel patch covering the mouth region and convert it into a grayscale image. The cropped mouth images are then fed into the pre-trained model in [38]. Using the network based on VGG-M [39], we can get a 512-D feature vector that describes about 200-ms-long lip movement.
## 5 Latency and Reliability Performance Evaluation in Wearable-Based MCPTT Applications
### Evaluation Methodology and Parameters
In sidelink transmission mode-2, the MCPTT call members are responsible for determining the modulation and coding scheme (MCS), the allocation size (i.e., number of resource blocks (RBs)), and the number of transmission opportunities in each time resource pattern defined by the kTRP parameter. These sidelink grant parameters are preconfigured in the devices, as stated in Table 3. The Physical Sidelink Control Channel (PSCCH) period parameter defines the periodicity of this grant configuration performed by each UE. It is also called sidelink period and can have one of these values: 40, 60, 80, 120, 140, 160, 240, 280, or 320 ms. Within a PSCCH period, there are separate subframes for control (PSCCH) and for data (PSSCH). Hence, the PSCCH to PSSCH (PSCCH/PSSCH) ratio needs to be fixed. This ratio reflects the distribution of sidelink resources in the time domain (i.e., number of subframes). An illustration of the three adopted sidelink channel configurations is provided in Figure 4. For instance, the same number of subframes is allocated for both sidelink channels in the second configuration (PSCCH/PSSCH ratio = 1). The two other configurations are based on the preference (i.e., allocation of more subframes) for one channel over the other. At the application level, the MCPTT model that we utilize in the evaluation assumes that 60-byte voice packets are generated every 20 ms. This means that the data rate demand for the voice communication is 24 kbps.
## 2 Materials and Methods
### Data Wrangling
#### 2.3.2 Feature Engineering  
Subsequently, we engaged in feature engineering to augment the informativeness and relevance of the data for our predictive objectives. Initially, binning was applied to the \"BMI\" feature to facilitate interpretability, categorizing BMI values into underweight, healthy weight, overweight, and obese ranges [1,37].  
Binning continuous variables like BMI can help mitigate the impact of outliers and non-linear relationships, making the data more amenable to modelling techniques that assume linearity or require categorical inputs. Furthermore, we devised a composite variable labelled \"Overall_Diet\", which quantifies an individual's dietary habits by considering the consumption of green vegetables, fruits, and fried potatoes. The inclusion of fruits and vegetables contributes positively to the score, while fried potato consumption detracts from it.  
In an effort to discern potential correlations between individual habits and heart disease development, the feature \"Substance_Use\" was introduced, encapsulating the interaction between smoking and alcohol consumption. Notably, a modified mapping was employed for the smoking variable, with smokers being assigned a value of \"\(-1\)\" and non-smokers designated as \"0\", thereby accentuating higher negative values for individuals engaging in both tobacco and alcohol use.  
Further preprocessing steps involved converting features such as Heart_Disease, Skin_Cancer, Other_Cancer, depression, arthritis, Smoking_History, and exercise from Yes/No values to binary format. Additionally, ordinal features like General_Health, BMI_Category, and Age_Category underwent label encoding to retain their ordinal nature, while categorical nominal features such as sex and diabetes were subjected to one-hot encoding to prevent the model from inferring spurious relationships between categories.
## 3 Results
### The Photogrammetric Spatial Resection  
The photogrammetric spatial resection method allows for determining orientation (\(Y_{0}\), \(Y_{0}\), \(Z_{0}\), \(\omega\), \(\varphi\), \(\varkappa\), \(\varkappa\), \(y_{0}\), \(y_{\ell}\)s) and distortion (\(k_{1}\), \(k_{2}\), \(k_{3}\), \(k_{4}\)) parameters of an image based on the known control points deployed on the object [28]. In the case of the left image, the 13 orientation and distortion parameters were computed by solving the set of 22 Equations (7) and (8) for eleven visible control points 1, 2,..., 12 (Figure 1) by the least-squares method. The camera's determined position (\(Y_{0}\), \(Y_{0}\), \(Z_{0}\)) in the left (L) is shown in Figure 3. The pixel coordinates \(x_{\text{comp}}\), \(y_{\text{comp}}\) of the control points computed after adjustment according to the Equations (7) and (8) are not right equal to the measured values \(x\), \(y\). The residual mean squares (RMS) of the obtained pixel coordinates residuals \(\varepsilon_{x}=x-x_{\text{comp}}\), \(\varepsilon_{y}=y-y_{\text{comp}}\) are equal to 1.8 and 2.0 in pixels, respectively.  
In the case of the right image, the 13 orientation and distortion parameters were computed by solving a set of 22 Equations (7) and (8) for eleven visible control points 1, 2,..., 12 (Figure 2) by the least-squares method. The camera's determined position (\(Y_{0}\), \(Y_{0}\),\(Z_{0}\)) in the right (R) is shown in Figure 4. The RMS for obtained pixel coordinates residuals \(\epsilon_{\rm x}=x-x_{\rm comp}\), \(\epsilon_{\rm y}=y-y_{\rm comp}\) are equal 1.3 and 1.6 in pixel values, respectively.  
Due to the nonlinear least-squares problem, the approximate values of the parameters were computed using the Direct Linear Transformation (DLT) method [29].
## 6 Stability Analysis of Surrounding Rock Based on Refined Modeling
### Analysis of Plastic Zone Evolution  
The analysis of the plastic zone evolution of the ore body is shown in Figure 18a,c,e. Under the combined mining conditions of the 100 m and 220 m sublevels, the fill bodies, stop pillars, and inter-store pillars in the areas near the ore body faults F10, F2-1, and F18 undergo plastic deformation. However, with the increase in the thickness of the isolation pillars, the extent of this plastic deformation is significantly reduced. When the isolation pillar thickness is set to 20 m, it effectively suppresses the initiation and development of the plastic zone within the stopes and fill bodies of the 100 m and 220 m sublevels. Nevertheless, certain plastic zones still exist, and special attention should be paid to the stability of the ore-rock contact zones and mining operations near faults. High-intensity pre-support may be necessary, when required.  
The analysis of the plastic zone evolution of the faults is shown in Figure 18b,d,f. Under the combined mining conditions of the two sublevels, significant plastic deformation occurs at the junction of the ore body fault F2-1 and the near-ore fault F10 only when the isolation pillar thickness is 0 m. As the thickness of the isolation pillar increases, the tendency for the plastic zone to develop towards this junction weakens. However, the area of the plastic zone at the junction of the ore body fault F2-1 does not significantly improve with increased isolation pillar thickness. Nonetheless, it helps to some extent in controlling the stability of the rock mass near the faults, particularly when the isolation pillar thickness is set to 20 m, further improving the plastic deformation area of the faults.  
Figure 18: Analysis of plastic zone evolution of ore body and faults: (**a**) 0 m isolation pillar (frontal view of ore body); (**b**) 0 m isolation pillar (top view of fault); (**c**) 10 m isolation pillar (frontal view of ore body); (**d**) 10 m isolation pillar (top view of fault); (**e**) 20 m isolation pillar (frontal view of ore body); (**f**) 20 m isolation pillar (top view of fault).
###### Abstract  
In response to the challenges of karst geophysical exploration in an environment with strong external interference, this paper proposes a new method, namely the frequency-Bessel transform method, for extracting multi-order dispersion curves of surface waves from background noise to characterize karst. The observation noise data of the Wuhan karst development area are used as an example, where the dolomitic limestone and limestone mixed with dolomite of the Jialing River Formation of the middle lower Triassic are widely developed in the observation area. The frequency-Bessel transform method involves performing a Bessel integral transformation on the cross-correlation coefficient of background noise in the frequency domain. Firstly, by synthesizing theoretical noise data and comparing it with the spatial autocorrelation method--which is currently the main method for extracting the fundamental dispersion curve of surface waves--it is verified that the frequency-Bessel transform method can extract the higher-mode dispersion curve. Then, by taking the actual measured single-point noise data as an example, the effect of applying the frequency-Bessel transform to the actual noise data is tested, and the inversion of the fine structure of the strata by the addition of higher-mode dispersion, the use of the damped least squares inversion method, and the joint inversion of fundamental and higher-mode dispersion curves are analyzed. The higher-mode dispersion curve of Rayleigh surface wave extracted by the frequency-Bessel transform is much clearer, and the 2D shear wave velocity structure profile obtained from inversion explains the karst development area, karst strip area, and thickness of the Quaternary overburden. The inferred results match with the actual borehole data. Multi-mode imaging of background noise based on the frequency-Bessel method can be applied to depict karst in complex backgrounds, and has significant potentiality in the field of ambient seismic noise tomography, providing a new idea and method for karst detection in near-surface engineering.  
Wuhan karst; background noise; surface wave multi-mode; higher-mode dispersion; frequency-Bessel transform; damped least squares inversion; geophysical exploration 2023
# Effect of Drying-Wetting Cycle and Vibration on Strength Properties of Granite Residual Soil
## 2 Experimental Setup
### Test Methods
#### 2.2.2 Vibration Simulation and Drying-Wetting Cycle Process  
In order to simulate the effects of mechanical vibration and hot and rainy climates on the soil samples, the specimens were first vibrated and then simulated with DW cycles.  
1. Vibration simulation  
Referring to the vibration method in Refs. [27; 28], a ZBSX-92A shock vibrating sieve machine was used to simulate the effect of mechanical vibration on the soil. To observe the effect of specimen disturbance on strength degradation of GRS, the duration of vibration was determined based on previous preliminary experiments. The vibration duration was different for the direct shear specimens and the UCS specimens. The former was set at 15 min, 30 min and 45 min and the latter at 5 min, 10 min and 15 min. To prevent moisture loss during vibration, the specimens were wrapped in cling film and placed on a matching vibrating sieve tray. The completion of these steps marked the end of the vibration specimen preparation process.  
2. Drying-wetting cycle process  
In this study, the number of DW cycles for the tests was set to range from 0 to 5 cycles. Each DW cycle consisted of two stages: drying process and wetting process. The specific path of the DW cycle can be found in Figure 4.  
Figure 3: Experimental flowchart.  
Drying process. The soil specimens were put into the oven at 55 \({}^{\circ}\)C for about 8 h, then taken out and left for 24 h to naturally cool down to room temperature. When the moisture content of a specimen is maintained at about 10%, it is considered to be completely dry.  
Wetting process. The unconfined compression strength specimens and direct shear specimens were placed in the triaxial saturation apparatus and the disk saturation apparatus, respectively, before being placed in the saturation chamber. Subsequently, the saturation chamber was evacuated using a vacuum pump for 2 h. Then, the water inlet valve of the saturation chamber was opened to fully submerge the specimens. Finally, the interior of the saturation chamber was connected to the atmosphere, and the specimens were left undisturbed for a minimum of 24 h to complete the saturation process.
## 2 Overview of Smart Water Grid Technologies and Systems
### Technological Description
#### 2.2.3 Data Transmission and Power  
Once data is collected and stored temporarily in a data logger connected to a sensing/control device, it needs to be transmitted to a centralized location. Direct line transmission via cable is ideal in principle, because there would be no practical limits on bandwidth. While direct connection is an attractive option within an end-user facility, it may not be practical to hardwire the water delivery system. Another possibility is to bridge the smart meter from the broadband systems of water customers to the utility, but this is organizationally infeasible as the utility cannot be dependent on the end-user.  
These jurisdictional and technical issues make wireless data transmission an attractive approach. Because the smart water grid is comprised of various technologies with different data transmission goals, a variety of wireless technologies and protocols are potentially useful. This includes mobile broadband (cellular towers), wireless broadband (Wi-Fi), personal area networks (device-to-device transmission), and satellite communication. For example, AMR, which is a drive-by meter reading system via personal area networks, is already becoming a common alternative to manual meter reading for water utilities for their water billing programs. In addition, satellite communication is a common technology used by smart irrigation controllers companies to update landscape water scheduling on a daily basis. The regularity in spacing of smart water meters suggest that a mesh network design, in which each device is a communication hub for neighboring devices, is a promising approach for the smart water grid [24]. Another promising technology for smart water grids is a wireless protocol able to broadcast a signal up to 45 miles that is designed specifically for smart meter communication [25].  
Another issue that comes up with wired or wireless communication is the powering of devices. Direct connection to the power grid is feasible within a facility, but for devices on the water distribution system, off-grid power may be needed. This means that the power needed by the device to use a particular wireless technology/protocol will be an issue, along with the frequency of data collection and transmission by the device. Current off-grid power solutions include solar panels, water turbines, and long-life batteries [18, 26, 27, 28]. The wireless communication protocol developed just for smart grids has the advantage of low-power usage [25].
## 2 Methods
### Auto-Regressive Integrated Moving Averages (ARIMA)  
ARIMA models were introduced by [PERSON] and [PERSON] [2]. ARIMA has been a widely used forecasting linear model during several decades. In these kind of models, the future value is accepted to be a linear function of several past observation and an error term. Three prediction terms compose this linear function: the autoregressive term (AR), the moving average term (MA), and the integration term (I). A SARIMA model can be obtained by extending the ARIMA model to include seasonal features. In this way, the model is specified as SARIMA(\(p\),\(d\),\(q\))(\(P\),\(D\),\(Q\))\({}_{S}\), where \(q\) represent the order of the moving average terms, \(p\) denotes the order of the autoregressive terms, and \(d\) is the degree of differencing. (\(P\),\(D\),\(Q\)) deals with the seasonal part and the capital letters corresponds to their counterparts for the seasonal models with the seasonal orders and the seasonality of the model is represented by the parameter \(s\). Equation (3) depicts a typical expression of the SARIMA model:  
\[\varphi_{p}(L)\Phi_{P}(B^{s})\
abla^{d}\
abla_{s}^{D}y_{t}=\theta_{q}(B)\Theta _{Q}(B^{s})a_{t}, \tag{3}\]  
where \(y_{t}\) is the observed value, \(\
abla^{d}\) and \(\
abla_{s}^{D}\) are the regular and seasonal differencing operators, respectively, \(p\) and \(P\) are the number of non-seasonal and seasonal autoregressive terms, \(q\) and \(Q\) are the number of non-seasonal and seasonal moving average terms, \(d\) and \(D\) are the number of regular and seasonal differences, \(\varphi\) and \(\Phi\) depict the value weights of the non-seasonal and seasonal autoregressive term, \(\theta\) and \(\Theta\) represent the weights of the non-seasonal and seasonal moving average term, the seasonality is represented by \(S\), and \(a_{t}\) is the noise term. The first step is to identify the SARIMA structure assisted by the observation of the simple and partial autocorrelation function (ACF and PACF) of the time series. In addition, data must be stationarity in mean, using power transformations, and stationarity in variance, employing differencing of the time series. Second, the parameters of the model are estimated. Third, the estimated residuals must be checked. The requirements of a white noise process should be satisfied by the residuals and they are assumed to be independent. Severalstatistic tests and plots of the residuals are used for this purposes. Finally, an estimation of the future value of the volume of containers is obtained. In this work, a value range of the model parameters are iteratively tested to identify the most suitable model.
## 3 Results  
### Technical Components of the Experimental Setup  
The materials and devices utilized in the experimental research for developing the test stand dedicated to compensating for leg length discrepancy (LLD) using the computer vision-based application are presented in detail. The selection of these materials and equipment was based on performance criteria in relation to acquisition costs, compatibility with the technical requirements of the prototypes, and the accuracy necessary to obtain reliable results. The specifications of each component involved in constructing the test stand and prosthetic device prototypes are detailed, along with their role in achieving the established objectives.  
The study further demonstrates the integration of these components into various functional devices or prototypes, enclosed in protective casings to ensure the safeguarding of electronic equipment and the overall efficiency of the system.  
The implementation and testing of the LLD compensation method using computer vision involved the use of an extensible prosthetic module (EPM), as previously described in [24]. The EPM was adapted and integrated into a new experimental configuration, allowing it to be attached via standardized prosthetic pyramidal adapters. This adaptation was driven by the objective of optimizing a novel testing protocol, with the aim of ensuring conditions that closely resembled real-world usage. The final configuration of the setup is illustrated in Figure 5.  
Figure 5: Anteriorlateral view of the EPM attached to the mannequin’s residual limb. (**a**) Overall view. (**b**) Zoomed-in detail (1—EPM: 1′—mobile assembly; 1′—fixed assembly; 2—prosthetic socket; 3—prosthetic foot; 4—right lower limb growth simulation mechanism: 4′—fixed component; 4′′—movable/adjustable component).  
A mannequin with anatomical shaping was selected for the procedure, and a transtibial residual limb was created by sectioning the lower leg and modelling it to simulate the appearance and consistency of such a residual limb. The resulting residual limb was then covered with a 3 mm-thick silicone liner (Icecross Dermo(r) Cushion, Ossur, Reykjavik, Iceland) and a custom-made prosthetic socket composed of a deformable material containing a standardized pyramidal adapter. In order to guarantee effective fixation and adequate suspension during testing, under conditions as close to real-life scenarios as possible, an ALPS Flex Sleeve prosthetic knee sleeve (ALPS(r) South, LLC, St. Petersburg, FL, USA) was also utilized. Finally, the EPM was attached to the prosthetic socket along with a SACH (solid-ankle cushion-heel) prosthetic foot using standardized prosthetic adapters.  
The EPM was actuated by a CHR-GM25-370 12 V DC motor (TT Motor (HK) Industrial Co., Ltd., Hong Kong, China). A selection of the electrical parameters is highlighted in Table 1.  
The parameters listed in Table 1 are based on the manufacturer's technical datasheet [34] and were used as selection criteria during the design of the EPM.