esa-sceva/satcom-chunk-collection huggingface dataset:
# Wearable Haptic Device for Stiffness Rendering of Virtual Objects in Augmented Reality
## 4 Discussion  
We propose a wearable haptic device for stiffness rendering, which can generate kinesthetic haptic feedback corresponding to elastic/rigid virtual objects in AR. The fundamental limitations of existing devices for AR finger-based manipulation are the co-presence of AR objects with the user's hand, which raises problematic inter-penetration between the finger-tips and object, and the haptic device, which hampers the user experience and performance by occluding finger-tips. To minimize the size of the proposed device, we adopt a passive (i.e., locking) actuation mechanism with the tendon-driven transmission, which is a suitable hardware setup for AR (small-size form factor, lightweight). We also design a selective locking part, which can effectively generate two types of kinesthetic feedback for rigid and elastic objects.  
From the experimental results, we quantitatively compare the various feedback conditions for stiffness rendering. The kinesthetic haptic feedback \(KH_{r}\) is verified to deliver the highest stiffness, even stiffer than the real rubber ball consistently. The realness is also perceived as the highest value, which means that the kinesthetic haptic feedback would be the most adequate feedback for rendering a virtual rigid object, and other feedback (e.g., vibrotactile feedback) is not strong enough for rendering a rigid/realistic object in AR finger-based manipulation.  
For rendering an elastic/deformable object, the elastic kinesthetic feedback \(KH_{e}\) and vibrotactile feedback \(VH\) are reported to render similar stiffness values; however, the kinesthetic feedback shows better consistent perception (small standard deviation) with a superior realness. Our device is able to render a wide range of elasticity by replacing the elastic spring \(k_{e}\) along with the aiming applications, which would be applicable for many applications in AR/VR, (e.g., social interaction with an avatar, manipulation of a deformable object). However, given the simplicity of implementation of the vibrotactile feedback, the vibrotactile feedback would be a possible option for some applications.  
Particularly, the subjects report the high realness of virtual objects for the pseudo-haptic feedback \(VH\), which renders deformation along the inter-penetration. The realness is even larger than the haptic feedback conditions \(VH\) or \(KH_{e}\), which implies that the visual feedback can also substantially enhance the user experience of the AR finger-based manipulation. These complementary results of haptic feedbacks and visual (pseudo) feedback have an interesting implication for the integration of different types of feedback (i.e., across visual, vibrotactile, kinesthetic) for realistic rendering daily objects with a wide range of stiffness.
# Optimized Computation of Tight Focusing of Short Pulses Using Mapping to Periodic Space
## 3 Results and Discussion
### Verification and Accuracy Determination
Therefore, while the simulation in entire region (\(D/L=20\)) for a grid with the resolution of 28 points per wavelength takes 256 s and 40 GB of memory, the simulation using the presented scheme with \(D/L=2\) takes only 18 s and 4 GB of memory. Thus, we are able to reduce the calculation time and memory costs by a factor of 10, while the error introduced by the method did not exceed 0.85%.
## 4 Experimental Results and Analysis
### Experimental Effect Display  
To visually show the effect of the improved algorithm, we compare the experimental results of the traditional SSD algorithm and our algorithm. It can be seen from Figure 9, that in each image, objects of the same type are represented by the same color box. It can be seen from this group of pictures that under simple background conditions, both the SSD algorithm and the algorithm in this paper can accurately detect the location and category of the target, but the accuracy of the algorithm in this paper is significantly higher than that of  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Backbone** & **Train Data** & **Test Data** & _[EMAIL_ADDRESS] (\%)_ & **FPS** \\ \hline Faster RCNN [13] & ResNet-101-FPN & COCO 2017 trainval & COCO 2017 test-dev & 58.0 & - \\ SSD300 [15] & VGGNet-16 & COCO 2017 trainval & COCO 2017 test-dev & 41.2 & 46 \\ SSD512 [15] & VGGNet-16 & COCO 2017 trainval & COCO 2017 test-dev & 50.4 & 8 \\ DSSD321 [16] & ResNet-101 & COCO 2017 trainval & COCO 2017 test-dev & 46.1 & 12 \\ RetinaNet [39] & ResNet-101 & COCO 2017 trainval & COCO 2017 test-dev & 49.5 & 11.6 \\
**OURS** & VGGNet-16 & COCO 2017 trainval & COCO 2017 test-dev & **50.8** & 23 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Detection results on COCO dataset.  
Figure 8: Comparison of default box and ground truth frame under different feature maps. From left to right are the original pictures, the conv4_3 feature map visualization of the fusion feature map of conv4_3 and conv5_3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Backbone** & **Fusion** & **GC Block** & **RFB** & _mAP (\%)_ \\ \hline SSD & VGG16 & & & 77.5 \\ SSD & VGG16 & \(\surd\) & & 78.6 \\ SSD & VGG16 & & \(\surd\) & 80.1 \\ SSD & VGG16 & & \(\surd\) & 79.2 \\ SSD & VGG16 & \(\surd\) & \(\surd\) & 80.7 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation study on the PASCAL VOC 2007 Test Dataset.  
the SSD algorithm. Under complex background conditions, the algorithm in this paper can detect more targets more accurately. As shown in the first picture, the SSD algorithm can only identify nearby bicycles, and the algorithm in this paper can not only identify bicycles, but also people and cars in the distance are recognized, and the accuracy of bicycles is also higher than that of the SSD algorithm.  
The SSD algorithm has serious false detections and missed detections, and our algorithm improves the situation of false detections and missed detections, which can identify more small objects. Our algorithm has higher accuracy, the overall detection effect is significantly better than the SSD algorithm.
# Coregistration and Spatial Compounding of
## 2 Materials and Methods  
### Optoacoustic Imaging System  
The OAT system used for the acquisition of three-dimensional images of the mouse heart is depicted in Figure 1a. A custom-made matrix ultrasound transducer array (Imasonic, SaS, Voray, France) consisting of 256 piezocomposite elements (4 MHz central frequency, \(\sim\)100% detection bandwidth) densely distributed along its spherical active surface (40 mm radius, 90\({}^{\circ}\) angular coverage) was used to collect OA signals [19]. An optical parametric oscillator (OPO) laser (tunable wavelength between 670 and 1250 nm) guided with a fiber bundle inserted through a central aperture (8 mm diameter) of the transducer array was used to provide optical excitation. In the experiments, the pulse repetition frequency of the laser was set to 100 Hz and the wavelength was tuned to 700, 800 and 900 nm. OA signals were digitalized by a custom-made data acquisition system (Falkenstein Mikrosysteme GmbH, Taufkirchen, Germany), triggered with the Q-switch output of the laser, at 40 megasamples per second and transferred through a 1 Gbps Ethernet connection to a PC for storage and further processing. Raster scanning of the transducer array along the x-y axes (being y the vertical direction and x a normal direction parallel to the mouse surface) was performed inside a water tank by two translation motorized stages (Model: RCP2-RGD6C-I-56P-4, IAI Inc., Shizuoka Prefecture, Japan) in order to achieve a larger FOV (Figure 1a). For this, a custom-made holder for the spherical array along with a counter weight were attached to the stages [20; 21]. The holder enables translating the array in a direction normal to the mouse surface. In the experiments, the array was fixed at a position for which the heart was located at approximately the center of its spherical surface. For each position of the transducer array, a sequence of 1000 consecutive frames was acquired to fully cover multiple cardiac cycles. Scanning parameters were adjusted with a custom MATLAB interface (MathWorks Inc., Natick, MA, USA).
## 3 System Model
### The Establishment of Attribute Indexes
#### 4.1.2 Relay Coefficient  
During data transmission, the closer the forward neighboring node \(j\) is to the shortest path from the node \(i\) to the BS, the more energy can be saved in the routing path. Meanwhile, the more residual energy of the node \(j\), the more data can be forwarded to it. Thus the relay coefficient is defined as follows:  
\[RC(j)=\frac{E_{j}}{E_{S}}\times\frac{d_{is}}{d_{ij}+d_{js}},j\in FN(i) \tag{6}\]
## 2 Numerical Methods  
### Cfdship-Iowa  
The simulations were performed by the URANS (unsteady Reynolds-averaged Navier-Stokes) solver CFDSHIP-IOWA V4.5 [43] to model the viscous flow field around ship and propeller complex geometries, ship motions and unsteady propeller rotational flow. A multi-block structured grid with overset grid capability was used. The solver featured semi-captive, full-6 DOF (degree of freedom), parallel, and high-performance computing (HPC) capabilities. The non-dimensional governing equations in the solver were [41]:  
\[\
abla\mathbf{u}=0, \tag{1}\]  
\[\frac{\partial u}{\partial t}+(\mathbf{u}-\mathbf{u}_{C})\cdot\
abla\mathbf{u}=-\
abla P_{ S}+\
abla\cdot\left[\frac{1}{\text{Re}_{eff}}\Big{(}\
abla\mathbf{u}+\
abla\mathbf{u}^{T} \Big{)}\right]+\mathbf{S_{b}}, \tag{2}\]  
where \(t\) is time, \(\mathbf{u}\) is the vector form of velocity field, \(\mathbf{u}_{C}\) is the grid velocity for considering ship motions, and \(P_{S}\) is the pressure term. The gravity term is included in \(P_{S}\). \(\mathbf{S_{b}}\) is the source term, which will represent the body forces produced by propeller model (Sections 2.2 and 2.3). \(\text{Re}_{eff}\) is the effective Reynolds number defined by ship speed \(U\), ship length \(L\), kinematic viscosity of fluid \(\
u\), and turbulent viscosity \(\
u_{t}\) solved by the SST (shear stress transport) \(k\)-\(\omega\) turbulence model without wall function [44]:  
\[\text{Re}_{eff}=\frac{UL}{\
u+\
u_{t}}. \tag{3}\]  
The SST \(k\)-\(\omega\) model is widely used in ship hydrodynamics application, and its robustness and global reliability had been concluded at T2015 [45] and the previous workshop [46].  
To model the free surface, the governing equation of the single-phase level set method [47] is:  
\[\frac{\partial\phi}{\partial t}+(\mathbf{u}-\mathbf{u}_{G})\cdot\
abla\phi=0, \tag{4}\]  
where \(\phi\) is the level set function or distance function. The free surface is defined along the iso-surface of \(\phi=0\), and the undisturbed free surface is located at z/\(L=0\). \(\phi>0\) indicates that the grid is full of water. In the region of \(\phi<0\), i.e., air, URANS is not solved.  
The numerical methods to discretize and solve the above-mentioned governing equation are based on the second-order finite-difference method. The Euler backward method is used for the time derivatives, the upwind method is used for the convection term, and the central difference is used for the diffusion term. The velocity and pressure field were coupled by the projection method. The details have been addressed in [41].
## 3 Results and Discussion
### Physicochemical Characterization of Soil and Leaf
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Property**} & \multicolumn{3}{c}{**Treatment**} & \multirow{2}{*}{**F Test Sig.**} \\ \cline{2-2} \cline{4-4}  & **Control** & **Compost** & & \\ \hline OM (\%) & 77 \(\pm\) 2 & 75 \(\pm\) 6 & 79 \(\pm\) 4 & ns \\ NO3- (\%) & 1.49 b \(\pm\) 0.17 & 1.13 c \(\pm\) 0.07 & 2.2 a \(\pm\) 0.2 & * \\ P2O5 (\%) & 0.38 \(\pm\) 0.03 & 0.45 \(\pm\) 0.01 & 0.45 \(\pm\) 0.01 & ns \\ K2O (\%) & 0.374 c \(\pm\) 0.009 & 0.401 b \(\pm\) 0.005 & 0.436 a \(\pm\) 0.014 & * \\ Cr (mg/kg) & 1.2 \(\pm\) 0.1 & 1.49 \(\pm\) 0.02 & 1.74 \(\pm\) 0.03 & ns \\ Cu (mg/kg) & 13.7 a \(\pm\) 0.3 & 10.8 b \(\pm\) 1 & 12.6 b \(\pm\) 0.7 & * \\ Ni (mg/kg) & 3.0 \(\pm\) 0.1 & 2.3 \(\pm\) 0.1 & 2.91 \(\pm\) 0.04 & ns \\ Pb (mg/kg) & 3.0 \(\pm\) 0.1 & 2.7 \(\pm\) 0.1 & 2.4 \(\pm\) 0.2 & ns \\ Zn (mg/kg) & \textless{}LOQ (20) & \textless{}LOQ (20) & 26 \(\pm\) 2 & - \\ \hline \hline \end{tabular} Data expressed in dry weight basis. OM: organic matter; LOQ: limit of quantification. Different letters indicate significant differences (* \(p<\) 0.05), ns = non-significant. (–) No test was performed.  
\end{table} 
Table 3: Leaf properties at the end of the experiment. Regarding the content of organic matter and assimilable phosphorous, no significant differences were found between the treatments. And, as per the content of heavy metals, results showed that the application of these types of amendments to the soil, despite the slight increase in the total concentration of metals in the soil treated compared to the control, caused no increase in the content of metals in the leaves. These results can be explained based on two phenomena that reduce the mobility of the metals and, therefore, their availability for the plant. On the one hand, the formation of insoluble metal chelates with the humic fraction present in the organic amendment [57; 58] and, on the other hand, the capacity of the worms to reduce the bioavailability of the heavy metals [59; 60]. These results are similar to the ones provided by the studies of other authors on organic amendments with different origins that were also applied to vineyards [61; 62].
## 2 FE-SEA Hybrid Model for Bending Vibration of a Simply Supported Beam
### Solution of the Global Dynamic Equation  
The solution of the global motion equation primarily involves determining two correction terms in Equations (10) and (11): the dynamic stiffness correction term, denoted as \(\Delta D=D^{gl}(D^{ll})(D^{gl})^{T}\), and the generalized force correction term, denoted as \(\Delta F=D^{gl}(D^{ll})^{-1}F^{l}\). The solution process follows the same principles as the conventional finite element method (FEM).  
Assuming the structure contains only a single subsystem, it can be derived that [3]  
\[\Delta D_{mn}=\omega^{4}\sum_{j=1}^{N^{l}}M_{mj}^{gl}M_{nj}^{gl}[\omega_{j}^{2 }(1+i\eta_{j})-\omega^{2}]^{-1} \tag{16}\]  
\[\Delta F_{m}=-\omega^{2}\sum_{j=1}^{N^{l}}M_{mj}^{gl}F_{j}^{l}[\omega_{j}^{2 }(1+i\eta_{j})-\omega^{2}]^{-1} \tag{17}\]  
where \(\omega\) is the excitation frequency, \(\omega_{j}\) and \(\eta_{j}\) are the natural frequency and damping loss factor of the \(j\)th local mode, respectively, and \(i\) is the imaginary unit. It can be seen from the above expressions that the correction terms essentially represent the influence of local modes on the global modes. To facilitate time-domain response analysis, the common term \([\omega_{j}^{2}(1+i\eta_{j})-\omega^{2}]^{-1}\) in Equations (16) and (17) can be approximated based on the relationship between excitation and local modal frequencies:  
* Resonant case (\(\omega_{j}\approx\omega\)): When the excitation frequency matches the natural frequency of a local mode, Equation (16) simplifies to \[\Delta D_{mn}=-i\frac{\pi\omega^{3}}{2}E(M_{mj}^{gl}M_{nj}^{gl})v\] (18) where \(v\) is the modal density of the subsystem (defined as the number of modes per unit frequency bandwidth). Equation (18) shows that resonant local modes increase the damping of the global system.  
* Non-resonant case (\(\omega_{j}\ll\omega\)): In this case, Equation (10) can be approximated \[\Delta D_{mn}=-\omega^{2}{\sum_{j=1}^{N^{l}}M_{mj}^{sl}M_{nj}^{sl}}\] (19) which implies that non-resonant local modes primarily affect the effective mass of the global system. For local modes with natural frequencies higher than the excitation frequency, their influence on the global motion is neglected. Only the input power from global modes to local modes is considered in the analysis. Finally, based on the excitation frequency, Equations (12) and (14) are substituted into Equations (18), (19) and (17), respectively, to obtain all required correction terms. The resulting corrected mass matrix, damping matrix, and generalized force vector are then substituted into the governing dynamic equation (Equation (6)) to compute the nodal displacements, velocities, and accelerations. Using Equation (5), the displacement, velocity, and acceleration responses at any location along the beam can be accurately reconstructed.
## 1 Introduction
The paper describes the beamline components shown schematically in Figures 1 and 2: TiS laser-driver with \(\sim\)50 fs pulse duration, 4 W average power and 1 kHz repetition rate (Section 2); HHG EUV Source generating photons with energies \(>\)75 eV, with maximum flux of single harmonic \(>\)10\({}^{11}\) photons/second (ph/s), \(\sim\)50 fs pulse duration (\(>\)10\({}^{8}\) ph/pulse/harmonic) (Section 3); EUV Off-Plane Mounting (OPM) monochromator preserving the pulse duration (Section 4); Focusing and recombination of the EUV and excitation pulses. The excitation and EUV pulses are focused on the same focal spot on the sample material (Section 5). The sample material is mounted on a five-axis manipulator with liquid helium cooling for Spin-ARPES measurements (Section 6); Ultrafast materialsengineering stations: MBE and PLD growing the new, ultrafast sample materials. The stations are connected by fast and contamination free UHV transfer tubes to the TR-Spin-ARPES spectrometer (Section 7). Section 8 describes the beamline characterization. Section 9 describes measurements of static and time-resolved ARPES. Finally, Section 10 describes the possible EUV beamline scaling reduction of data acquisition time. This could be achieved by upgrading the laser driver from the 4 W, 1 kHz Ti: Sapphire to the new femtosecond fiber laser with \(\sim\)kW average power, and MHz repetition rate, resulting in over two orders of magnitude reduction of data acquisition time. Figure 1: Ultrafast spintronic materials facility has two integral, interconnected parts: the EUV TR-Spin-ARPES Beamline and the materials engineering stations. Figure 2: The EUV Beamline: (**a**) engineering drawing and (**b**) laboratory picture. (1) TiS laser driver; (2) Laser beam-splitter; (3) Delay line; (4) Focusing lens for excitation pulse); (5) Focusing lens for HHG; (6) HHG EUV source; (7) EUV monochromator; (8) monochromator exit slit; (9) EUV detector chamber; (10) Focusing and recombination chamber; (11) Spin-ARPES spectrometer chamber; (12) Spin-ARPES hemispherical analyzer; (13) Liquid Helium cooling pipes; (14) 4-axis manipulator for sample; (15) Load lock for sample material; (16) Transfer tube bringing sample material from materials engineering stations; (17) MBE-2 materials engineering station.
# Using a Cable-Driven Parallel Robot with Applications in 3D Concrete Printing
## 3 Analysis of the Workspace of the CDPR
\right]\); Equation (8) can be rewritten as  
\[\mathbf{A}\boldsymbol{\tau}+\mathbf{w}_{\text{P}}=0 \tag{11}\]  
Figure 3: Wrench diagram of the moving platform. where:  
* \(\mathbf{\tau}\): vector of the cable tensions (m x1);
* \(\mathbf{\mathrm{A^{T}}}\): Jacobian matrix of the CDPRs (n x m);
* \(\mathbf{\mathrm{w_{p}}}\): vectors of the external wrenches applied on the center of mass of the mobile platform (n x 1). The wrench closure workspace (WCW) of the CDPM defines a set of poses of the moving platform wherein the cables can balance any external wrench with positive tensions. Or, the WCW is the set of mobile platform poses that are wrench feasible; this means that with any wrench \(\mathbf{\mathrm{f}}\) in **WCW**, there exists a vector of cable tensions \(\mathbf{\tau>0}\) such that \(\mathbf{\mathrm{A}\tau+\mathbf{\mathrm{f}}=0}\). From Equation (11), for each pose of the MB, it is necessary to calculate the combination of tension forces so as to balance it with the external forces, which can be obtained by inverting the \(\mathbf{\mathrm{A^{T}}}\) matrix. \[\mathbf{\tau}=\left(\mathbf{\mathrm{A^{T}}}\right)^{-1}\mathbf{\mathrm{w_{p}}} \tag{12}\]  
Because \(\mathbf{\mathrm{A^{T}}}\) is not square, a pseudo inverse-matrix can be used to solve this problem. There are some methods to determine the existence of an inequality system, with a solution or not, like pseudo inverse-matrix, linear programming, interval analysis, etc. The cable configuration of the concrete printing cable robot with 6 DOF in this study is shown in Figure 4. The base frame, [B], is located at the center of the lower surface of the robot frame. The MP is a rectangular frame connected to a robot frame by anchor points Ai and attach points Bi; li (or Lei according to the notation of this research) is the Euclidean norm cable lengths. In all cases i = 1,...,8. This structure has no WCW because there is no set of cable tensions \(\mathbf{\tau}\) to satisfy the equilibrium Equation (11). The static workspaces of the cable robot are show in Figure 5, as determined by the linear programming algorithm; with this configuration, ignore the external forces, as the wrench force has only gravity force caused by the weight of the moving platform with the end-effector, so the workspace of the robot is proportional to the payload (weight of the moving platform and concrete). In the same configuration, when changing the payload placed on MB, the workspace also changes: the larger the payload, the wider the workspace. Based on this feature, a suitable trajectory can be designed, depending on the load. Figure 4: General kinematics of the cable robot (6 DOF; 8 cables). This result shows that this configuration is suitable for printing purpose when the payload is large enough but do not exceed the upper limit of the tension. In the next sections, the MB and concrete extruded is designed with a total weight from 50 kg (without cement mortal) to 70 kg (full cement mortal).
# Parallel Accelerated Fifth-Order WENO Scheme-Based Pipeline Transient Flow Solution Model
## 3 Numerical Calculation Method
### Boundary Condition Processing
#### 3.4.3 Bifurcated Pipe Interface Handling  
When there is a bifurcation in the water delivery system, as shown in Figure 3, it can be handled as the following.  
According to the positive characteristic line of pipe A and the negative characteristic line of pipes B and C, we have  
\[\left\{\begin{array}{l}h_{(A,N(A))}+\frac{c_{A}}{g}v_{(A,N(A))}=h_{(A,N(A)-1 )}^{n-1}+v_{(A,N(A)-1)}^{n-1}\left(\frac{c_{A}}{g}+\Delta t\sin\theta_{A}- \frac{f\Delta x\Big{|}v_{(A,N(A)-1)}^{n-1}\Big{|}}{2 gD_{A}}\right)\\ h_{(B,0)}-\frac{c_{B}}{g}v_{(B,0)}=h_{(B,1)}^{n-1}-v_{(B,1)}^{n-1}\left(\frac{ c_{B}}{g}+\Delta t\sin\theta_{B}-\frac{f\Delta x\Big{|}v_{(B,1)}^{n-1}\Big{|}}{2 gD_{ B}}\right)\\ h_{(C,0)}-\frac{c_{C}}{g}v_{(C,0)}=h_{(C,1)}^{n-1}-v_{(C,1)}^{n-1}\left(\frac{ c_{C}}{g}+\Delta t\sin\theta_{C}-\frac{f\Delta x\Big{|}v_{(C,1)}^{n-1}\Big{|}}{2 gD _{C}}\right)\end{array}\right. \tag{23}\]  
The following relation can be obtained from the conservation of flow at the bifurcation and the equal head relationship.  
\[A_{A}v_{(A,N(A))}=A_{B}v_{(B,0)}+A_{C}v_{(C,0)} \tag{24}\]  
\[h_{(A,N(A))}=h_{(B,0)}=h_{(C,0)} \tag{25}\]  
Figure 3: Bifurcated pipe interface mesh division diagram.  
Equations (23)-(25) should be solved to derive the flow rate and head value at the bifurcation connection of the corresponding moment. If there are more than three sections of pipes connected at the bifurcation, referring to this paper, the number of pipes should be expanded for calculation.