esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The article presents a robotic device for post-stroke wrist and finger exercise. The device enables the wrist and finger joints' coupled movement from flexion to extension, except for the thumb. Even though the device's mechanism has numerous segments, it only has one actuated DOF. Alongside achieving the target movement--the coupled flexion and extension of the wrist and finger joints, provided with just one active DOF--the objectives were to develop a device that:  
* is simple for donning and doffing, so that the patients could do it themselves, even in case of spasticity;
* is safe, so that the device can be used at home, in a nonclinical environment and also in the telerehabilitation applications;
* is adaptable to different patients in terms of their abilities and hand dimensions;
* is easy to use, portable, and has a relatively low manufacturing cost.  
The movement that the device enables was compared to the unconstrained coupled movement of the wrist and finger joints of healthy subjects. Deviations from the base-line trajectory between measurement protocols and RMSD values show satisfactory results regarding the device's movement, especially when moving into extension. The main two disadvantages of the developed solution are attributed to the targeted simplicity of the design and the safety assurance requirements. The limited torque capabilities of the actuation system originate from the back-drivability and the demand for inherent safety. The simple and affordable design, based on a single actuated DOF, imposes coupled motion of the wrist and finger joints; therefore, the fingers and wrist cannot be trained separately, which can be seen as a disadvantage. In the future, we are planning to also implement the assistive and resistive training modality. It is also planned to conduct measurements with post-stroke patients to further evaluate the device's suitability for post-stroke telerehabilitation.
# Mixed Alternating Projections with Application to Hankel Low-Rank Approximation
## 4 Linearized Cadzow Iterations in the Signal-Estimation Problem
### Linearization of the Operator \(\Pi_{\mathcal{M}_{r}(\mathbf{L},\mathbf{R})}\)  
Consider the matrix \(\mathbf{S}_{0}\in\mathbb{R}^{L\times K}\), \(L>r\) and \(K>r\), where \(\mathbf{S}_{0}=\mathcal{T}_{L}(\mathsf{S}_{0})\) and \(\mathsf{S}_{0}\) is a series of rank \(r\); hence, the matrix \(\mathbf{S}_{0}\) has rank \(r\). In the neighborhood of the point \(\mathbf{S}_{0}\), the set \(\mathcal{M}_{r}\) is a smooth manifold of dimension \(r(L+K)-r^{2}\), and the tangent subspace \(\mathcal{N}=\mathcal{N}(\mathbf{S}_{0})\) at \(\mathbf{S}_{0}\) is expressed as follows [19]:  
\[\mathcal{N}(\mathbf{S}_{0})=\{\mathbf{X}\in\mathbb{R}^{L\times K}:\mathbf{U}^ {\mathsf{T}}\mathbf{X}\mathbf{V}=\mathbf{0}_{L-r,K-r}\}, \tag{12}\]  
where \(\mathbf{U}\in\mathbb{R}^{L\times(L-r)}\) and \(\mathbf{V}\in\mathbb{R}^{K\times(K-r)}\) are arbitrary full-rank matrices such that \(\mathbf{U}^{\mathsf{T}}\mathbf{S}_{0}=\mathbf{0}_{L-r,K}\) and \(\mathbf{S}_{0}\mathbf{V}=\mathbf{0}_{L,K-r}\). Note that the definition is correct because \(\mathcal{N}(\mathbf{S}_{0})\) depends only on colspace \(\mathbf{S}_{0}\) and rowspace \(\mathbf{S}_{0}\).  
Let us denote by \(\Pi_{\mathcal{N}(\mathbf{S}_{0})(\mathbf{L},\mathbf{R})}:\mathbb{R}^{L\times K }\rightarrow\mathcal{N}(\mathbf{S}_{0})\) the projection on \(\mathcal{N}(\mathbf{S}_{0})\) in the norm generated by (4). Then, the linearized Cadzow iteration in the neighborhood of the point \(\mathbf{S}_{0}\) has the following form:  
\[\mathbf{X}_{k+1}=\Pi_{\mathcal{H}(\mathbf{L},\mathbf{R})}\Pi_{\mathcal{N}( \mathbf{S}_{0}),(\mathbf{L},\mathbf{R})}\mathbf{X}_{k}. \tag{13}\]  
Set \(\mathbf{X}_{0}=\mathcal{T}_{L}(\mathsf{X}_{0})\), \(\mathsf{X}_{k}=\mathcal{T}_{L}^{-1}(\mathbf{X}_{k})\), \(k\geq 1\). Then, we can rewrite (13) as  
\[\mathsf{X}_{k+1}=\Big{(}\mathcal{T}_{L}^{-1}\Pi_{\mathcal{H}(\mathbf{L}, \mathbf{R})}\Pi_{\mathcal{N}(\mathbf{S}_{0}),(\mathbf{L},\mathbf{R})}\mathcal{T }_{L}\Big{)}\mathsf{X}_{k}.\]Note that each of the operators inside the brackets is linear. Hence, their composition is a linear operator and therefore can be written as a matrix, which we denote \(\mathbf{P}_{\mathsf{S}_{0},(\mathbf{L},\mathbf{R})}\):  
\[\mathbf{P}_{\mathsf{S}_{0},(\mathbf{L},\mathbf{R})}=\mathcal{T}_{L}^{-1}\Pi_{ \mathcal{H},(\mathbf{L},\mathbf{R})}\Pi_{\mathcal{N}(\mathsf{S}_{0}),(\mathbf{ L},\mathbf{R})}\mathcal{T}_{L},\quad\mathbf{X}_{k+1}=\mathbf{P}_{\mathsf{S}_{0},( \mathbf{L},\mathbf{R})}\mathsf{X}_{k}. \tag{14}\]  
Formally, we can calculate the elements of the matrix \(\mathbf{P}_{\mathsf{S}_{0},(\mathbf{L},\mathbf{R})}\) by successive substituting \(N\) standard unit vectors into the composition of operators.
# Performance Analysis of the IEEE 802.11p Multichannel MAC Protocol in Vehicular Ad Hoc Networks
## 3 Background
### IEEE 1609.4 Standard  
The IEEE 1609.4 standard (protocol) is the standard of multichannel operation in VANETs. In the IEEE 1609.4 protocol, the channel time is divided into multiple Synchronization Intervals (SIs) with a fixed length of 100 ms for each SI. An SI consists of a 50 ms CCHI and a 50 ms SCHI as shown in Figure 3. Each CCHI and SCHI begins with a 4 ms guard interval which is allowed for nodes switch among channels, and it is used for precise synchronization among different nodes. All vehicles need tune to CCH during the CCHI for the transmissions of safety-related messages or WSA messages, while during the SCHI, vehicles can optionally tune to the specific SCH to deliver non-safety messages. Periodical and synchronous switching (alternating) between CCH and SCH is mandatory for single-radio transceivers and operates on one radio channel at a time.  
Due to high vehicle mobility and ever-changing vehicle density, the current version of the IEEE 1609.4 standard dose not provide high QoS guarantee [7, 11, 30]. On the one hand, for safety-related messages, due to the mandatory channel switching and fixed duration of CCHI and SCHI specified in the IEEE 1609.4 standard, vehicular networks suffer from low reliability especially in situations of high vehicle density. On the other hand, when the traffic is sparse but heavy non-safety messages such as digital map and media downloading are required, the CCH only needs a little time to transmit a small amount of safety-related messages, and thus, the CCHI may be left idle for a significant periods of time. On the contrary, the 50 ms SCHI is not enough to transmit bulk non-safety messages. These conditions will lead to low throughput and underutilization of SCHs.  
In addition, since, in the IEEE 1609.4 standard, nodes cannot specifically determine the starting time of their transmission on the SCH during the negotiation process on the CCH, there may be collisions on the SCHs. In our next analysis, we relax this condition, and assume that nodes can determine the starting time of their transmission on SCHs after their negotiation on the CCH. Base on  
Figure 3: IEEE 1609.4 multichannel alternating operation.  
Figure 2: EDCA backoff procedure for two kinds of priority.  
this assume, we analyze the performance of the IEEE 1609.4 multichannel MAC protocol under a variety of vehicular environments.
## 5 Calibration and Detection  
Precise wavelength calibration is crucial in spectroscopy, especially with high-resolution spectrographs (e.g., for radial velocity measurements with \(\Delta v\sim\) 10 cm/s), to ensure any short-term or long-term variations in the system are corrected. Currently, iodine cells are used for precision wavelengthcalibration, but it is difficult to get uniform wavelength coverage from them due to non-uniform absorption line density and saturated lines. Fabry-Perot interferometers and photonic combs solve this problem by creating high density of lines for monitoring the wavelength calibration precisely. Astronomical community has also started using frequency combs for calibration [100]. Recently, photonic combs were used to remove the systematic aberrations of a wide-field spectrograph [101]. These new developments integrate well with the photonic spectrographs described above and are geared towards making the photonic spectrographs more stable and precise.  
As described in Section 4, AWGs and PEGs are arguably the most promising and mature photonic dispersion technologies. For both, the focal plane is along a Rowland circle. Exposing the focal plane of the chip to free space for cross dispersion and/or imaging on the detector leads to significant Fresnel losses due to refractive index mismatch. On the other hand, a higher refractive index is desirable on-chip for better mode confinement, and lower propagation and bending losses. Avoiding the index jump to free space can improve the overall throughput of the spectrograph by as much as a factor of two. It should be noted that single-order or tandem AWGs/PEGs can be designed so that cross dispersion is not required.  
Therefore, from a throughput perspective, it is advantageous to directly couple the focal plane of these chips to the detector. This can either be a linear detector array or, in case of stacked photonic circuits, a two-dimensional detector array. Due to the curved focal plane, a curved detector is best suited for these devices [102]. A smaller pixel pitch (\(\lesssim\)10 \(\upmu\)m) is also desirable to achieve the best spectral resolution in the direct coupling case, since most of the dispersed light is focused in an area of the order of a few hundred microns. Development of such detectors is underway and a successful concave CMOS detectors with a radius of curvature of 150 mm and pixel pitch of \(\sim\)7 \(\upmu\)m were reported only few months ago [103]. A highly curved CMOS detector (curvature radius \(\sim\)20-30 mm) by Microsoft has also been reported very recently with a pixel pitch \(<\)10 \(\upmu\)m [104]. There have also been AWG designs with a flat focal plane [105], however, flatness of the focal plane is wavelength dependent. Therefore, for versatile uses, curved detectors will likely be a better solution.
# Analytical Solutions for the Propagation of UltraShort and UltraSharp Pulses in Dispersive Media
## 9 Pulse Broadening Comparison  
To illustrate the large differences between equivalent pulses, the FWHM of four different pulses is presented in Figure 31. To keep the initial FWHM of all pulses equal, the pulses' parameters were adjusted accordingly. For an arbitrary initial FWHM of \(T\), the FWHM as a function of the normalized distance \(\zeta=\beta_{2}z/T^{2}\) is presented for Gaussian pulse, i.e., Equation (14) with the parameter \(\theta=0.85T\); for smooth rectangular pulse, i.e., Equation (64) with \(\theta=1.05T\) and \(\Delta=0.1T\); for smooth exponential pulse, i.e., Equation (77) with \(\Delta=0.1T\) and \(a=0.4T^{-1}\); and finally for Nyquist-Sinc pulse, i.e., Equation (80), with \(\theta=1.1T\). Clearly, the differences will increase if the signals are chirped.
## 5 Discussion  
### Efficiency of MODMSA in Solving Dynamic Mission Scheduling Problems  
For the multi-objective dynamic observation-mission-scheduling problem, the proposed MODMSA demonstrates excellent performance in the diversity and quality of the non-dominated solutions. The performance gap between the MODMSA and other comparison algorithms increases significantly as the number of missions grows. Especially for the diversity of the non-dominated solutions, only the MODMSA can obtain various non-dominated solutions, while the non-dominated solutions obtained by other comparison algorithms are concentrated in highly perturbed solutions. However, it is easy to find that the computational complexity of the proposed MODMSA is higher than that of the compared algorithms as the profit-oriented local search algorithm causes additional computational costs. In addition, the additional computational cost is acceptable since the computational complexity of the profit-oriented local search algorithm is at a polynomial level. Therefore, our proposed MODMSA can provided multiple high-quality alternative observation plans with acceptable computational costs for the EOS system decision-maker, which shows broad application prospects in practical EOS dynamic mission planning.
###### Abstract  
Vibrotactile interfaces are an inexpensive and non-invasive way to provide performance feedback to body-machine interface users. Interfaces for the upper extremity have utilized a multi-channel approach using an array of vibration motors placed on the upper extremity. However, for successful perception of multi-channel vibrotactile feedback on the arm, we need to account for vibration propagation across the skin. If two stimuli are delivered within a small distance, mechanical propagation of vibration can lead to inaccurate perception of the distinct vibrotactile stimuli. This study sought to characterize vibration propagation across the hairy skin of the forearm. We characterized vibration propagation by measuring accelerations at various distances from a source vibration of variable intensities (100-240 Hz). Our results showed that acceleration from the source vibration was present at a distance of 4 cm at intensities \(>\)150 Hz. At distances greater than 8 cm from the source, accelerations were reduced to values substantially below vibrotactile discrimination thresholds for all vibration intensities. We conclude that in future applications of vibrotactile interfaces, stimulation sites should be separated by a distance of at least 8 cm to avoid potential interference in vibration perception caused by propagating vibrations.  
vibration propagation; vibrotactile displays; upper extremity; user feedback +
Footnote †: journal: Applied Sciences
## 4 Influence of Liquid-Filling Ratio on Hydrodynamic Ram Effect  
### Simulation Modes with Different Liquid-Filling Ratios  
Liquid hydrazine will be consumed when the satellite maneuvers or changes orbit, so the satellite tank is not always full. Numerical simulations were conducted in which the spherical aluminum debris with a radius of 2 cm impacted satellite tanks filled with 70%, 80%, and 90% liquid hydrazine at the velocity of 7000 m/s and the damage of the satellite tanks caused by the hydrodynamic ram effect was studied.  
Figure 8: (**a**) Perforation and bulge on the front wall. (**b**) Perforation and bulge on the back wall.  
Figure 9: Curves of the bulge height varying with distance on the front and back walls.  
Liquid hydrazine is squeezed out by high pressure nitrogen, and the satellite tank is filled with nitrogen and liquid hydrazine. Simulation models of the satellite tanks with liquid-filling ratios of 70%, 80%, and 90% are depicted in Figure 10. Lagrange grids were constructed for the debris and satellite tank, whilst Euler grids were constructed for the nitrogen and liquid hydrazine. A multi-material ALE formulation with a second-order accurate advection was selected to treat the nitrogen and liquid hydrazine. The liquid hydrazine was allowed to flow into the nitrogen region through shared nodes on their interfaces. The interaction between the debris and tank wall was achieved through the eroding surface-to-surface contact, and the interaction between the debris and liquid hydrazine was achieved through the automatic surface-to-surface contact. The mesh sizes of the debris, satellite tank, nitrogen, and liquid hydrazine were 0.4 cm, 2 cm, 2 cm, and 2 cm, respectively. The computing time step was 0.6 \(\upmu\)s.
###### Abstract  
In the presented study, multi-parameter inversion in the presence of attenuation is used for the reconstruction of the P- and the S- wave velocities and the density models of a synthetic shallow subsurface structure that contains a dipping high-velocity layer near the surface with varying thicknesses. The problem of high-velocity layers also complicates selection of an appropriate initial velocity model. The forward problem is solved with the finite difference, and the inverse problem is solved with the preconditioned conjugate gradient. We used also the adjoint wavefield approach for computing the gradient of the misfit function without explicitly build the sensitivity matrix. The proposed method is capable of either minimizing the least-squares norm of the data misfit or use the Born approximation for estimating partial derivative wavefields. It depends on which characteristics of the recorded data--such as amplitude, phase, logarithm of the complex-valued data, envelope in the misfit, or the linearization procedure of the inverse problem--are used. It showed that by a pseudo-viscoelastic time-domain full-waveform inversion, structures below the high-velocity layer can be imaged. However, by inverting attenuation of P- and S- waves simultaneously with the velocities and mass density, better results would be obtained.  
complex velocity model; full waveform inversion; wave attenuation; preconditioned conjugate gradient; vibroseis sources +
Footnote †: journal: Applied  
1
Footnote †: journal: Applied
## 4 Comparison of TE, pSWE and 2D-SWE  
Using TE as a reference method, sensitivity of pSWE was 71.42% for detecting F1 fibrosis, 77.77% for F2, 62.5% for F3, and 71.42% for F4. Sensitivity of 2D-SWE was 92.85% for detecting F1, 83.33% for F2, 87.5 % for F3, and 85.71 % for F4. Significant correlation was found between TE and 2D-SWE overall (Kappa correlation factor = 0.843, \(p\) = 0.001). Analyzing the subgroup with success rate (SR) = 60-70%, no significant correlation between TE and pSWE was found (Kappa correlation factor = 0.172, \(p\) = 0.452). Assessing the subgroup with SR \(>\) 70%, a significant correlation between TE and pSWE was found (Kappa correlation factor = 0.761, \(p\) = 0.001). Overall, 2D-SWE correlated with TE better than pSWE [24].
## 2 Materials and Methods
### Total Astaxanthin Content Estimation  
The total astaxanthin content in the lyophilized _H. lacustris_ biomass was measured by bead-beater-assisted ethyl acetate extraction followed by high-performance liquid chromatography (HPLC), with a slight modification to a previously reported protocol [13]. Briefly, 5 mg of algal cells were mixed with 1 mL of ethyl acetate and 1.0 g of glass beads (1.5 mm diameter; Daihan Scientific, Wonju-si, Korea) in a 2.0 bead-beating tube (Biofact, Daejeon, Korea). The algal solution was vigorously homogenized using a Fastprep-24 5G\({}^{\text{TM}}\) bead beater for three cycles (6 m/s, each 30 s; MP Biomedicals, Santa Ana, CA, USA) and centrifuged at 10,000 rpm for 5 min (Sorvall Legend Micro 17R, Thermo Scientific, Waltham, MA, USA). Bead-beater-assisted solvent extraction was repeated for three additional rounds until all the red algal cells turned colorless. The astaxanthin-containing ethyl acetate layer was separated by centrifugation and filtered using a 0.2 \(\mu\)m membrane. Next, 0.1 mL of the ethyl acetate solution was mixed with 0.9 mL of DCM/MeOH (1:1, \(v/v\)) (containing 0.025 N NaOH) in a fresh tube and analyzed using an HPLC instrument equipped with a diode-array detector (Agilent 1260 infinity, Hewlett-Packard, Santa Clara, CA, USA) and a YMC Carotenoid column (250 mm \(\times\) 4.6 mm, 5 \(\mu\)m; YMC Inc., Wilmington, NC, USA). Detailed HPLC conditions have been reported previously [11; 13]. The total astaxanthin content of the 90-day-old _H. lacustris_ cyst cells was estimated to be 26.2 \(\pm\) 1.9 mg/g cell.
# Blind Recognition of Frame Synchronization Based on Deep Learning
## 2 Algorithm Description
### Binary Data Preprocessing  
The frame length is defined as \(L=S+K\), where \(S\) represents the length of the synchronization code and \(K\) represents the length of the data in one frame. Reshape a binary data sequence into a matrix \(H\) of size \(n\times n\), and when \(L|n\), the synchronization code in each frame appear in the same column of the matrix. Then the column vectors of the matrix are divided into synchronization code columns and data bits columns. In this case, if the matrix is converted into a binary grayscale image, there will be distinct vertical stripes in the positions of the synchronization code columns, as shown in Figure 2.  
Figure 1: Frame synchronization code blind recognition algorithm flow based on deep learning. FL refers to Frame length and FS refers to Frame synchronization code.  
When \(n\) is close to but not equal to \(L\), the corresponding synchronization bits in each frame of the digital sequence are not in the same column. Figure 2(a) shows the case for \(n<L\), each row of the matrix is not sufficient to accommodate a complete frame, the synchronization bits of the next frame will shift backward, eventually forming oblique stripes with a negative slope of \(1/(n-L)\). It is similar for the the case of \(n>L\), this time the value of the slope is positive. With the increase of the difference, the stripes will be extremely narrow, as shown in Figure 2(c). To enhance the stripe features of the images, we perform a flat-top interpolation on the original binary data stream with an interpolation factor of 4. The reshaped image sample from interpolated data is shown in Figure 2(d), the more pronounced stripes will facilate the processing later. Thus, the data preprocessing includes the flat-top interpolation and the reshape of the data after interpolation.  
When \(n\) and \(L\) differ greatly, as shown in Figure 2(b), the black and white pixels tend to be randomly distributed, there is no stripes in the image. The above two cases can be defined in the following mathematical way:  
**Definition 1**.: _If the value of \(n\) is equal to or close to the length of the interpolated frame \(L_{\alpha}\) with \(L_{\alpha}=4L\), satisfying_  
\[0\leq|n-L_{\alpha}|\leq d. \tag{1}\]  
_then we define that \(n\) **matches**\(L_{\alpha}\), where \(d\) represents the **matching radius**. In this case, there are obvious stripes in the sample. The case where Equation (1) is not satisfied is defined as \(n\) **dis-matches**\(L_{\alpha}\), there will be no stripes in the sample._
## 3 Results and Discussion
### Impact of Filling Factor of the Autoclave  
The degree of fill can influence multiple features of the synthesis process, such as zeolite crystal nucleation and growth, crystallization rate (i.e., uniformity), crystal size (i.e., thickness), and morphology. Furthermore, the pressure inside the autoclave can impact the reaction rate and equilibrium [50; 51]. To assess the influence of internal pressure on membrane performance, four fill factors (60%, 70%, 80%, and 90%) were employed. The results are depicted in Figure S4. According to Figure 3a, gas permeation measurement showed that as the filling factor increased from 60% to 90%, the ideal CO\({}_{2}\)/CH\({}_{4}\) permselectivity (from 110 to 30), the ideal H\({}_{2}\)/SF\({}_{6}\) permselectivity (from 125 to 31), and CO\({}_{2}\) permeance (from 2.85 \(\times\) 10\({}^{-6}\) to 1.73 \(\times\) 10\({}^{-6}\) [mol/(m\({}^{2}\)sPa)]) decreased and the permeance of SF\({}_{6}\) molecules increased (from 7.44 \(\times\) 10\({}^{-9}\) to 1.73 \(\times\) 10\({}^{-8}\) [m\({}^{3}\)/(m\({}^{2}\)sPa)]). The observed behavior can be attributed to increased pressure within the autoclave due to suspension expansion during the hydrothermal process, and this elevated pressure results in a more compact and stressed SSZ-13 membrane layer. Consequently, an elevated fill factor correlates with an increased incidence of non-zeolitic pathways. Moreover, heightened reactant concentration due to reduced free volume inside the autoclave can accelerate crystal growth, potentially leading to larger crystals but also increasing the risk of agglomeration or second phase formation.  
Figure 3: _Cont._