esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Review of Open Caisson Modeling Technology: Current Practices and Future Prospects
## 4 Advances in Computational Modeling Method
### Fdm  
The finite difference method (FDM) is among the earliest numerical methods to address differential equations with specified initial and boundary conditions. It simulates plastic deformation in soil, rock, and other materials when yield limits are exceeded, utilizing a user-defined grid of discrete components that mimic structural geometry [89]. With parametric studies showing the relevance of compaction pressure on wall displacement, FDM has been effectively used to replicate retaining structure [90]. [PERSON] et al. [91] used FLAC \({}^{3\text{D}}\) to create a numerical simulation model that includes excavation time under various computational ranges to account for creep and seepage effects in order to study pile displacement and ground settlement. [PERSON] et al. [92] simulated mechanized tunneling by incorporating sequential excavation, face support, grouting pressure, and lining installation in Plaxis \({}^{3\text{D}}\) and FLAC \({}^{3\text{D}}\). They evaluated the inconsistencies caused by intricate soil-structure interactions and tunneling mechanics in modeling outcomes. The results demonstrated considerable agreement in ground movement predictions among both methods under stiff soil conditions, highlighting their reliability despite diverse numerical methods. DEM, which characterized soil as an assemblage of discrete interacting particles, provides an effective representation of soil behavior during the failure of a caisson foundation as soils within the failure zone undergo large deformations, rendering continuum mechanics ineffective. However, DEM models were proven to be computationally expensive, especially when applied to boundary value problems like caisson foundation analysis [93].
# Initial Estimation of Kinematic Structure of a Robotic Manipulator as an Input for Its Synthesis
## 3 Results
### Manipulability Comparison  
To compare the results, we decided to evaluate the manipulability of the calculated kinematic structures. This scalar measure was obtained using the Yoshikawa algorithm [1], which describes how spherical the end-effector velocity ellipsoid is. It differs between 0 and 1, where the value 1 shows the best manipulability in all axes. If the value is close to 0, the mechanism might be dealing with singularities.  
The results are shown for both translational and rotational motions in Figure 12 as a logarithmic graph, because the measure differs significantly between particular kinematic structures. It should be kept in mind that the presented manipulators have less than six DoFs, which is one of the reasons why the manipulability measure by Yoshikawa evaluates them with low numbers.  
As expected, the type A kinematic structure has to deal with singularities and the manipulability tends to be the lowest for the given poses. Type B has almost the same  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Joint** & \(\mathbf{d_{i}\) **(m)** & \(\mathbf{a_{i}\) **(m)** & \(\mathbf{\alpha_{i}\) **(rad)** & **Joint** & \(\mathbf{d_{i}\) **(m)** & \(\mathbf{a_{i}\) **(m)** & \(\mathbf{\alpha_{i}\) **(rad)** \\ \hline
1 & 0.8 & 0.34 & 0.79 & 1 & 0.64 & 0.29 & \(-0.39\) \\
2 & 0 & 0.34 & \(-0.79\) & 2 & 0.32 & 0.56 & \(-1.48\) \\
3 & 0 & 0.34 & 0.79 & 3 & 0.46 & 0.38 & 0 \\
4 & 0 & 0.34 & \(-0.79\) & 4 & 0 & 0 & \(-0.28\) \\ \hline \hline \multicolumn{8}{c}{**a** Type A estimation} & \multicolumn{8}{c}{**b** Type B estimation} \\ \hline
**Joint** & \(\mathbf{d_{i}\) **(m)** & \(\mathbf{a_{i}\) **(m)** & \(\mathbf{a_{i}\) **(rad)** & **Joint** & \(\mathbf{d_{i}\) **(m)** & \(\mathbf{a_{i}\) **(m)** & \(\mathbf{\alpha_{i}\) **(rad)** \\ \hline
1 & 1.27 & 0.36 & \(-0.7\) & 1 & 0.64 & 0.29 & \(-0.40\) \\
2 & 0 & 0.36 & \(-0.7\) & 2 & 0.32 & 0.55 & \(-1.48\) \\
3 & 0 & 0.36 & \(-0.7\) & 3 & \(-0.33\) & 0.30 & \(-0.29\) \\
4 & 0.93 & 0 & 0 & 4 & 0.83 & 0 & 0 \\ \hline \multicolumn{8}{c}{**c** Type C estimation} & \multicolumn{8}{c}{**d** Type D estimation} \\ \hline \hline \end{tabular}
\end{table}
Table 1: The DH parameters of manipulators for \(Pose(1)\).  
Figure 11: Comparison of the length of generated manipulators for every pose.  
translational manipulability as type D, but the last two joints are in a singular position, so the rotational manipulability drops in the case of type B kinematic structure. Type C performs better than types A and B. In the case of general poses \(Pose(1,2)\), type D provides the highest manipulability measures. However, for \(Pose(3)\) when \(Z\) axes are parallel, the manoeuvrability of the type D algorithm drops under the values of type C.
# Numerical Implementation of the Barcelona Basic Model Based on Return-Mapping Integration
## 2 Implicit Integration of the BBM  
As a traditional model to characterize the elastic-plastic mechanical behavior of unsaturated soil in geotechnical engineering, the BBM was developed based on the CAM clay model, proposed by [PERSON] et al. [41]. Based on plasticity theory, the critical state model, and the modified CAM clay model, the BBM is suitable for describing the mechanical behavior of unsaturated soils, expansive clay, low plastic sand, silty clay, and other materials [42; 43]. Taking pore suction as a variable, the BBM was used to study the influence of soil suction on soil elastic-plastic mechanical behavior, and can be used to characterize the shear strength and consolidation brought on by hydroscopic expansion, collapsibility, and pore suction change in unsaturated rock and soil [44]. The BBM, like other elasto-plastic constitutive models, divides soil deformation into elastic and plastic deformation, employs three stress variables \((p,q,s)\), and employs yield surfaces \((LC,SI,CLS)\) to divide stress space into plastic and elastic space. Figure 1 presents the three-dimensional representation of the yield surface in the thermo-elasto-plastic BBM three-dimensional yield surface in \(p-q-s\) space and \(p^{\prime}-q-T\) space, where \(p^{\prime}\) is the net mean stress (i.e., total stress minus gas-phase pressure), \(q\) is the deviatoric stress (or shear stress), \(s\) is suction, and \(T\) is temperature [45].  
Due to the low permeability of bentonite materials, the seepage-stress coupling time is long during the encapsulation process [46]. The numerical calculation time step is an important factor affecting the accuracy of the calculation results and the time cost. Therefore, the implicit integration algorithm based on the return mapping is used to establish the numerical integration expressions of the BBM, which is embedded in the OpenGeoSys numerical simulation software to reduce the calculation steps and improve the calculation accuracy. In this section, the equations of the BBM were introduced firstly, afterward, the integration form of the BBM was developed according to the approach of [PERSON] and [PERSON] [47]. The consistent tangent modulus is a very important part of the constitutive model integration, and the determination of Tangent Modulus of Stress-Strain Curve is given in Appendix A.
## 3 Analysis of the Scattering Phenomenon in Underground Mines and Distribution of Dust Particles
### Joint Distribution of the Arrival Time and the Arrival Angle of the Scattering Downlink Link
#### 3.4.1 Cartesian-Spherical Coordinates  
As can be seen in Figure 2, an arbitrary scatterer will have spherical coordinates (\(r\),\(\theta\),\(\varphi\)). Therefore, based on the geometric characteristics of the stage and for convenience in handling the notation, the spherical coordinates related to the Cartesian coordinates (\(x_{s},y_{s},z_{s}\)) are expressed as follows  
\[(x_{s},y_{s},z_{s})=(r\cos\phi,r\sin\phi\cos\theta,r\sin\phi\sin\theta), \tag{23}\]  
\[r=\sqrt{x_{s}^{2}+y_{s}^{2}+z_{s}^{2}}, \tag{24}\]  
\[\theta=\arctan\left(\frac{z_{s}}{y_{s}}\right), \tag{25}\]  
\[\phi=\arccos\left(\frac{x_{s}}{r}\right)=\arccos\left(\frac{x_{s}}{\sqrt{x_{s }^{2}+y_{s}^{2}+z_{s}^{2}}}\right). \tag{26}\]  
Furthermore, based on the premise that any Cartesian-defined PDF function can be expressed in terms of spherical coordinates, we can restructure the \(f^{(he)}_{x,y,z}(x_{s},y_{s},z_{s})\) expression as follows  
\[f^{(he)}_{r,\theta,\phi}(r,\theta,\phi)=\frac{f^{(he)}_{x,y,z}(x_{s},y_{s},z_ {s})}{\left|J(x_{s},y_{s},z_{s})\right|}\begin{array}{c}x_{s}=r\cos\phi\\ y_{s}=r\sin\phi\cos\theta\\ z_{s}=r\sin\phi\sin\theta\end{array}, \tag{27}\]  
where \(J(x_{s},y_{s},z_{s})\) is the Jacobian transformation given by  
\[J(x_{s},y_{s},z_{s})\bigg{|}\begin{array}{c}x_{s}=r\cos\phi\\ y_{s}=r\sin\phi\cos\theta\\ z_{s}=r\sin\phi\sin\theta\end{array}=\begin{vmatrix}\frac{\delta x_{s}}{ \delta\theta}&\frac{\delta x_{s}}{\delta\phi}&\frac{\delta x_{s}}{\delta\phi} \\ \frac{\delta y_{s}}{\delta\theta}&\frac{\delta y_{s}}{\delta\phi}\\ \frac{\delta z_{s}}{\delta\theta}&\frac{\delta z_{s}}{\delta\theta}&\frac{ \delta z_{s}}{\delta\theta}\\ \end{vmatrix}^{-1}=-(r^{2}\sin\phi)^{-1}. \tag{28}\]Hence, by evaluating expression (27) and including expression (28) in it, we obtain the expression of \(f^{(he)}_{r,\theta,\phi}\) independent of \(\theta\) as follows  
\[f^{(he)}_{r,\theta,\phi}(r,\theta,\phi)=r^{2}\sin\phi f^{(he)}_{x,y,z}(x_{s},y_{ s},z_{s})=r^{2}\sin\phi f^{(he)}_{x,y,z}(r\cos\phi,r\sin\phi\cos\theta,r\sin\phi \sin\theta). \tag{29}\]
# Recognition of Earthquake Surface Ruptures Using Deep Learning
## 2 Data
### Machine Learning Samples  
Machine learning samples are the basis of the deep learning model, and their quantity and quality can directly affect the training of the deep learning model. The model learns the target features through input samples and adjusts parameters in real-time to match the input samples during the training process. How well the model learns the features is directly related to its overall performance. There are currently no relevant public datasets for identifying earthquake surface rupture targets; therefore, collecting relevant samples and making sample sets of earthquake surface ruptures before detection is necessary.  
Based on the high-resolution UAV images and field survey data, we used ArcGIS software to mark the fractured area manually. Unfortunately, directly loading images to the deep learning network would cause memory overflow; thus, the image must be cropped into patches before inputting them into the network. The repetition rate of the cropping sliding window was 0.1, and the final image block size was 256 \(\times\) 256.  
Manually labeled samples are limited, and the post-earthquake rupture targets of the image are small compared to unbroken areas. As a result, we could only acquire a limited number of labeled rupture samples. For deep learning models, too few training samples can lead to model overfitting. Therefore, increasing the number of samples through sample enhancement is necessary to avoid such situations. We applied methods to augment the data, such as image rotation, image flip, color transformation, and image stretching [26].  
Figure 3: Post-earthquake UAV orthophoto image and field photos.
## 3 Results and Discussion
### Global Results  
Figure 6 illustrates the HPE on the whole track in both situations: with camera ON (in red) and with camera OFF (in blue). Table 1 summarizes the performance of the PVT algorithm in terms of accuracy, protection levels and integrity.  
For HPE and HPL, we considered in Table 1 three situations corresponding to three time intervals: 67%, 95% and 99% of whole recording's duration. Analyzing Figure 6, it can be noted that the camera makes it possible to improve the positioning accuracy whatever the time percentages (67%, 95% or 99%) because the red curve is under the blue curve almost all the time (except right at the beginning of the trajectory). This remark is in agreement with Table 1, where the HPE values corresponding to the case when the camera is on are inferior to the values of HPE obtained when the camera is off for all three time intervals. However, we observe, by analyzing Table 1, that the HPL are increased by around 15% for 67% and 95% of the time and by around 30% for 99% of the time. This HPL increase is mainly due to DOP degradation, which appears as a consequence of satellite exclusion and to measurement overweighting.  
It can also be seen from Figure 6 that several sections where the camera improves the accuracy can clearly be identified. One of these sections has been selected to be more deeply analyzed in the following sub-section.
###### Abstract  
The measurement of heartbeat rate and breathing rate for patients with sensitive skin, such as skin with burns, is very difficult to do, especially if the number of patients is large and medical personnel is limited. Therefore, this study seeks to propose a preliminary solution to this problem by proposing a device that can measure the vital signs of several people concurrently, especially the heartbeat rate and breathing rate, without attaching sensors to their skin. This is done using an FMCW (frequency-modulated continuous wave) radar that operates at 77-81 GHz. FMCW radar emits electromagnetic waves towards the chest of several targets and picks up the reflected waves. Then, using signal processing of these reflected waves, each target's heartbeat rate and breathing rate can be obtained. Our experiment managed to perform concurrent detection for four targets. The experimental results are between 52 and 82 beats per minute for the heartbeat rates and between 10 and 35 breaths per minute for the breathing rates of four targets. These results are in accordance with normal heartbeat rate and normal breathing rate; thus, our research succeeded in proposing a preliminary solution to this problem.  
concurrent detection approach; frequency-modulated continuous wave radar; vital signs; signal processing
# Comparative Accuracy Analysis of Truck Weight Measurement Techniques
## 1 Introduction
The development of rules that envelope the special vehicle load effects is very important to ensure infrastructure safety [18]. Understanding the impact of overloaded heavy trucks is the key to proposing rational design and management provisions [19]. Several studies have been conducted to determine the damage caused by permit vehicles to ensure the structures' safety. The damage assessment criteria, traffic data collection, load effects computations were studied, and simulation techniques were used to optimize provisions for permit vehicles [20; 21; 22]. The bridge evaluation, under existing traffic, utilized the notional design, legal, and permit vehicles, hence, the WIM data can be utilized to verify standard rating vehicles' adequacy to check the existing bridges' live load-carrying capacity. To maintain the bridge's safety, a live load analysis is required, therefore, several studies have used WIM data to update the live load model to site-specific traffic characteristics. [PERSON] provided a simplified method to compute live load factors for bridge evaluation for the site-specific traffic [23]. The WIM system collects the majority of the traffic data utilized for the bridge live load development [24; 25]. The live load models were then created to assess the traffic-induced effects and provide a rational basis to update the live load provisions and give optimum safety levels [26; 27]. This study presents data collection and analysis for measured vehicles using portable scales, truck weigh station, WIM, and B-WIM systems. The statistical analysis of four different weighing systems were compared to assess static and dynamic systems' accuracy.
## 2 Material and Methods
### Chlorophyll Fluorescence Measurements  
Chlorophyll fluorescence measurements were performed by a pulse-amplitude-modulated photosynthesis yield analyzer (PAM-2000; Walz, Effeltrich, Germany). The measurements were conducted on three healthy and mature leaves at the top of each plant. A saturating light pulse (SP) was applied to dark-adapted (at least 20 min) leaves for the determination of minimum (F\({}_{\mathrm{O}}\)) and maximum (F\({}_{\mathrm{M}}\)) fluorescence. The maximum photochemical efficiency of photosystem II (PSII) (F\({}_{\mathrm{V}}\)/F\({}_{\mathrm{M}}\)) was then calculated according to [PERSON] et al. [57]:  
\[\mathrm{F_{V}}/\mathrm{F_{M}}=(\mathrm{F_{M}}-\mathrm{F_{O}})/\mathrm{F_{M}} \tag{1}\]  
In parallel with F\({}_{\mathrm{V}}\)/F\({}_{\mathrm{M}}\), the actual photochemical efficiency of PSII (Yield) was also calculated based on the rapid measurements of steady-state (F\({}_{\mathrm{S}}\)) and the maximal (F\({}_{\mathrm{M}}\)) fluorescence yield during the full closing of the PSII center in light-adapted leaves under ambient light and full sun conditions around noon:  
\[\mathrm{Yield}=(\mathrm{F_{M}}^{\prime}-\mathrm{F_{S}})/\mathrm{F_{M}}^{\prime} \tag{2}\]  
The differential F\({}_{\mathrm{V}}\)/F\({}_{\mathrm{M}}\) (\(\Delta\)F\({}_{\mathrm{V}}\)/F\({}_{\mathrm{Mrecovery}}\)) and Yield (\(\Delta\)Yield\({}_{\mathrm{recovery}}\)) between the nine different drought treatments values and recovery treatments were also calculated.
## 3 The Proposed Method
### Extract the Silhouettes from Interactable Real Objects
#### 3.1.2 Recognition and Segmentation Network  
In consideration of the RAM and CPU available on commercial mobile devices, we developed a deep neural network compressed from \(U\)-net [42]. We first reduced the number of filters per channel from 64 to 16 filters. In addition, we considered the input size of the network which directly influences the computation cost of the network. To find an optimal input size, we compared the performances of the network for various input sizes (see Table 1). We calculated pixel accuracy (PA) and mean intersection over union (mIOU) score as the model accuracy measures in addition to the inference speed. As shown in Table 1, the larger the input size, the higher the accuracy the network achieved. However, we chose the input size of 192 pixels which achieved a comparable level of accuracy to the original \(U\)-net while keeping the inference time below 500 ms. As a result, the number of parameters was reduced to 2.4 million from 7.76 million (cf. [42]). Finally, we trained the network for 5 epochs using _Adam_[43] optimizer with a learning rate started from \(10^{-4}\), and was divided by 10 after every two epochs. Figure 3 shows the recognition accuracy of our dataset. We trained the model with TensorFlow considering portability to mobile platforms.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Input Size** & **Inference Time** & **IOU** & **PA** \\ \hline
128 pixels & 250 ms & 0.935 & 0.973 \\
192 pixels & 465 ms & 0.971 & 0.988 \\
224 pixels & 820 ms & 0.973 & 0.990 \\
256 pixels & 1310 ms & 0.977 & 0.991 \\
384 pixels & 2840 ms & 0.980 & 0.994 \\ \hline \end{tabular}
\end{table}
Table 1: Performance of our network implanted on Note 9 with different input sizes.  
Figure 3: Recognition accuracy with seven animal dolls.
## 5 Experimental Results
### Noise Attacks  
Noise in images is an arbitrary variety of brilliance or shading data in the image. It is corruption of a signal by the addition of data. Images containing multiplicative noise consist of more noise data. Noise attacks on sample images and extracted watermarks are shown in Figure 8.
Nanocomposite of Si/C Anode Material Prepared by Hybrid Process of High-Energy Mechanical Milling and Carbonization for Li-Ion Secondary Batteries  
[PERSON]  
Department of Advanced Materials Engineering, Kongju National University, Cheonan 31080, Korea; [EMAIL_ADDRESS] (R.M.); [EMAIL_ADDRESS] (C.L.); [EMAIL_ADDRESS] (W.J.C.)  
C. K.  
Si-based composite structures in which nano-sized Si uniformly dispersed in the active or inactive materials (metal oxides, metal alloys and conductive polymers) to accommodate the Si volume expansion/contraction [18; 19; 20; 21]. Moreover, many attempts have been made to use suitable binders, electrolyte additives, and current collector [22; 23; 24]. Among them, silicon/carbon composites (such as Si-graphite, Si-graphene, Si-disordered carbon, Si-carbon nanotubes, and Si-carbon aerogel) have been demonstrated as an efficient approach for enhancing the electrochemical performance [25; 26; 27; 28; 29] because the carbon layer can improve the electronic conductivity of the composite and it can accommodate the volume changes of Si during the cycling.  
Several synthesis methods are employed for the preparation of Si/C composite anode. Among them, there are five representative methods: chemical/thermal vapor deposition [30] or pyrolysis [31], mechanical milling [32], mechanical milling and pyrolysis [33], chemical reaction of gel [34], and dehydration of a carbon precursor [35]. Excluding the above methods, the combination of high-energy mechanical milling (HEMM) and subsequent carbonization process is supposed to yield a finely dispersed nanocomposite materials and also has an advantageous for the mass-production due to its low-cost and operation simplicity. HEMM is not only for fragmenting, but also enables good mixing of the composite powder. Consequently, homogeneously mixed composite powders can be obtained [36]. In this paper, we present a carbon-impregnated Si nanocomposite by a scalable approach of high-energy mechanical milling and carbonization process, in which the nano-sized silicon is besieged with carbon. This structure is expected to improve the cyclic performance because the carbon impregnation can offer the desired mechanical stability and stabilize the solid electrolyte interface layers (SEI), contributing to enhancing the electronic conductivity. Consequently, this research paper covers the Si/C nanocomposite preparation, investigation of the changes in crystal structure and morphology, and evaluation of their electrochemical properties.
## 3 Optimal Efficiency Tracking Method in Full Current Mode
### Analysis of Impedance Matching Circuit: Buck Circuit  
Figure 4 shows buck circuit topology in active impedance matching. In this circuit, S represents the switch transistor, D represents the unidirectional diode, L represents the inductor, C represents the capacitor, RL represents the system's equivalent resistance, Re represents the impedance value at the optimal efficiency point being tracked, and PWM represents pulse width modulation.  
The control of the optimal efficiency tracking point is achieved by adjusting duty cycle D, which satisfies the following expression:  
\[\mathrm{D}=\sqrt{\frac{\mathrm{RL}}{\mathrm{Re}}} \tag{11}\]  
Since duty cycle D must be greater than 1, RL must be greater than Re for the buck circuit to operate.