esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Reliable Joint Percentage Enhancement  
The enhancement of the reliable joint percentage is evaluated by determining the precisely recovered joint rate \(J_{R}\) and precisely recovered frame rate \(F_{R}\). As shown in Equation (6a)-(6c), \(N_{Ej}\) and \(N_{Ef}\) are the unreliable joint number and relevant affected frame number, respectively. \(N_{rj}\) is the number of total recovered unreliable joint positions after deploying the SSSE procedure, while \(E_{rj}\) is the number of inaccurately recovered joints. From the aspect of frame statistics, \(N_{rf}\) is the total number of recovered frames and \(E_{rf}\) is the number of frames containing inaccurately recovered joints.  
\[\begin{split} J_{R}=\frac{N_{rj}-E_{rj}}{N_{Ej}}\\ F_{R}=\frac{N_{rf}-E_{rf}}{N_{Ef}}\end{split} \tag{47}\]  
Figure 7: Deviation between SSSE-extracted joints and ground truth.  
The 20 test sets presented in Table 1 indicate that more than four-fifths of all unreliable joints are successfully redressed based on the proposed SSSE method, and more than three-quarters of all frames containing unreliable joint skeletonization results are accurately fixed. Based on the experimental results above, the fusion application of SSSE and traditional RGB-D method proved effective in reliable joint percentage enhancement.
# Recent Improvements in the Development of Soft Grippers
## 2 Soft Grippers
### Architecture  
Concerning the architectures of soft grippers, there are two main types: anthropomorphic (Figure 1a) and non-anthropomorphic (Figure 1b). The anthropomorphic ones have a structure which is very close to the human hand. They generally have a thumb and three to four other fingers. Anthropomorphic soft grippers are normally made in the interest of performing manipulation tasks from inside the hand (dexterous manipulation). These are more desirable for service robots or robots that do human-like tasks [8]. On the other hand, non-anthropomorphic grippers can also perform dexterous manipulation tasks. Their structure is mostly made of equal fingers, between 2 and 4, that are fixed to a central palm. Their applications are rather directed to the transport and handling of goods.  
In general, anthropomorphic soft grippers are intended for medical applications, notably in the development of prostheses, in rehabilitation or in assistance. Non-anthropomorphic grippers, on the other hand, are generally used in the industry for pick-and-place and object handling tasks. For example, ref. [9] presents a review of soft grippers applied to agriculture. However, other applications can be considered for soft grippers and dexterous soft grippers.  
Soft grippers capable of in-hand manipulation are also anthropomorphic or non-anthropomorphic. The grippers in [12; 13; 14; 15] all have three or four fingers and a thumb that give them a human-like shape. The authors in [16] present a soft gripper that has only one finger and one human-like thumb, making it anthropomorphic. On the other hand, the grippers in [15; 17; 18; 19; 20; 21; 22; 23; 24; 25] have a non-anthropomorphic shape. The number of fingers varies from one work to the other based on the design parameters chosen by the authors but always lies between two and five. One sub-type of robotic hands that has been identified in the literature, falling under non-anthropomorphic hands, is the spherical hand with a two-finger hand topology, [26]. In this topology, the axis that describes the displacement of the grasped object intersects at the same point relative to the palm.
###### Abstract  
In the process of production logging interpretation, a water cut is one of the key factors to obtain the oil phase content in the oil well. In order to measure the water cut of the horizontal oil-water two-phase flow with low yield, the response characteristics of the combined capacitance sensor (CCS) are investigated under different flow patterns. Firstly, the measuring principles of coaxial, cylindrical, and CCS are introduced in detail. Then, according to the different flow pattern conditions of the horizontal oil-water two-phase flow, the response characteristics of the CCS are simulated and analyzed using the finite element method. Additionally, compared with the other two sensors, the advantages of the CCS are verified. Finally, the temperature and pressure calibration experiments are carried out on the CCS. The horizontal oil-water two-phase flow patterns in a low yield liquid level are divided in detail with a high-speed camera. Dynamic experiments are carried out in a horizontal pipe with an inner diameter of 125 mm on the horizontal oil-water two-phase flow experimental equipment. The simulation and experimental results show that the CCS has good response characteristics under different working conditions.  
horizontal oil-water two-phase flow; segregated flow; water cut measurement; combined capacitance sensor (CCS) +
Footnote †: journal: Applied Physics Letters B
## 3 Modulation Techniques
### Differential Phase-Shift Keying  
DPSK modulation technique combines the PSK technique with differential encoding. In DPSK, the phase of the signal is advanced by 180\({}^{\circ}\) when symbol 1 is transmitted while it remains unchanged during symbol 0 transmissions. BER of DPSK is given by:  
\[\mathrm{P_{DPSK}}=\frac{1}{2}\exp\left(-\frac{E_{b}}{N_{0}}\right) \tag{10}\]
# CFD Investigations of Cyclone Separators with Different Cone Heights and Shapes
## 2 Materials and Methods
### Details of the Analyzed Geometric Variants and the Adopted Assumptions of CFD Study
#### 2.3.3 Materials and Boundary Conditions  
For the continuous phase, air with a density of 1200 kg/m\({}^{3}\) and viscosity equal to \(2\times 10^{-5}\) kg/m\({}^{-8}\) has been used. The velocity inlet boundary condition (\(\overline{\mathbf{U}}=-U_{in}\hat{n}\).) [52; 53] with turbulent intensity (\(I=u^{\prime}/U_{in}\); \(u^{\prime}\) as the root mean square of the velocity) set to 5% is used at the inlet and a uniform velocity profile is applied. At the cyclone outlet, the pressure outlet boundary condition (with zero-gauge pressure) is applied. All other walls of the cyclone separator were assigned a _no-slip_ boundary condition (\(\mathbf{u}\cdot\hat{n}=0\).) [52; 53]. Three different inlet velocities viz. \(U_{in}\) =10, 15, and 20 m/s are considered in the present study to analyse the performance of all cyclone models.  
The solid phase was taken powder \(\rho_{p}\) = 2700 kg/m\({}^{3}\)). For solid particles, the uniformly sized particles are considered with eight different sizes ranging from 0.4 to 8.0 \(\upmu\)m--these particles are introduced from the inlet in the direction normal to the inlet face. The solids touching the bottom plate are considered to be collected by the cyclone; hence, the _trap_ boundary condition is applied there. The _escape_ boundary condition is applied at the outlet plane, and the solid particles that pass through this plane are considered uncollected. Assuming a nearly perfect elastic collision between the solid particles and cyclone walls, the coefficient of restitution in tangential as well as wall-normal direction is taken as unity.  
Two criteria were chosen for convergence viz. the residuals and the monitored flow variables. Regarding the former, the simulation was considered converged when the flow residuals fall below the targeted value of \(10^{-5}\), and for the latter, the tangential velocity was monitored at several locations in the flow domain and it was ensured that the monitored value becomes (quasi-) steady with time.
###### Abstract  
The 5 th generation mobile networks (5G) based on virtualized and centralized radio access networks will require cost-effective and flexible solutions for satisfying high-throughput and latency requirements. The next generation fronthaul interface (NGFI) architecture is one of the main candidates to achieve it. In the NGFI architecture, baseband processing is split and performed in radio (RU), distributed (DU), and central (CU) units. The mentioned entities are virtualized and performed on general-purpose processors forming a processing pool (PP) facility. Given that the location of PPs may be spread over the network and the PPs have limited capacity, it leads to the optimization problem concerning the placement of DUs and CUs. In the NGFI network scenario, the radio data between the RU, DU, CU, and a data center (DC)--in which the traffic is aggregated--are transmitted in the form of packets over a convergent packet-switched network. Because the packet transmission is nondeterministic, special attention should be put on ensuring the appropriate quality of service (QoS) levels for the latency-sensitive traffic flows. In this paper, we address the latency-aware DU and CU placement (LDCP) problem in NGFI. LDCP concerns the placement of DU/CU entities in PP nodes for a given set of demands assuming the QoS requirements of traffic flows that are related to their latency. To this end, we make use of mixed integer linear programming (MILP) in order to formulate the LDCP optimization problem and to solve it. To assure that the latency requirements are satisfied, we apply a reliable latency model, which is included in the MILP model as a set of constraints. To assess the effectiveness of the MILP method and analyze the network performance, we run a broad set of experiments in different network scenarios.  
5G networks; next generation fronthaul interface; centralized radio access network; packet-switched fronthaul network; resource placement; network optimization; MILP modeling +
Footnote †: journal: Applied Soft Matter
## 2 Materials and Methods
### Data  
#### 2.2.1 Satellite Data  
OCO-2 SIF data at 757 nm from 2015 to 2017 was used in this study. The footprint size of this dataset was 1.3 km \(\times\) 2.25 km at nadir. Different observation modes of OCO-2 products may cause angular variations of SIF, which impacts relationships between SIF and vegetation production [55]. Following [PERSON] et al. [70], only OCO-2 SIF data with view zenith angle (VZA) below 20 degrees were used for constraining bidirectional influences while keeping as much available data as possible. The OCO-2 SIF data was classified into soybean and maize groups according to crop type maps and the vertex coordinate of each footprint.  
The 16-day composite MODIS EVI product (MOD13A1, collection 6) at a spatial resolution of 500 m from 2015 to 2017 was also used here for assessing the ability OCO-2 SIF to estimate crop yield. We also used a yearly net primary productivity (NPP) product [71] with a spatial resolution of 30 m for the transfer learning as described in Section 2.3.2. The NPP data were produced for the concurinuous United States (CONUS) using the MODIS MOD17 algorithm with Landsat surface reflectance, meteorological, and land cover data.  
Figure 1: Flow chart of the approach used in this work.  
Figure 2: distribution of OCO-2 SIF footprints from 2015 to 2017, and the production area of corn and soybean mapped from the USDA Cropland Data Layer of 2017 ([[https://massgeodata.gmu.edu/CropScape/](https://massgeodata.gmu.edu/CropScape/)]([https://massgeodata.gmu.edu/CropScape/](https://massgeodata.gmu.edu/CropScape/))). Regions located within the Midwest 12 states of the U.S. with bold black boundary lines are the counties used in this study.
# Regolith-Rich PEEK Composite Bricks: Steps Towards Space-Ready Lunar Construction Materials
## 1 Introduction
### Organic Phase Selection
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & **T\({}_{\text{m}}\)** & **T\({}_{\text{g}}\)** & **CTE \({}^{\text{b}}\)** & & & \(\sigma_{y}\)** & & & \\  & \({}^{\circ}\)C & \({}^{\circ}\)C & **1/\({}^{\circ}\)C** & **LT \({}^{\circ}\)** & **RT** & **HT \({}^{\text{d}}\)** & **@10 GMy** & **TML** & **Refs.** \\ \hline PEEK DEXNYL & 343 & 143 & 46 \(\times\) 10\({}^{-6}\) & 200 & 90 & 35 & stable & 0.14 & [28; 36; 45; 46] \\ TPI PL450C & 388 & 250 & 55 \(\times\) 10\({}^{-6}\) & 189 & 92 & 58 & stable & 0.58 & [47; 48; 49] \\ PPS & 280 & 85 & 52 \(\times\) 10\({}^{-6}\) & 260 & 93 & 3.5 & stable & 0.05 & [28; 31; 36; 50] \\ PTFE TEFLON & 260 & \({}^{\text{a}}\) & 124 \(\times\) 10\({}^{-6}\) & 140 & 21 & 2 & lowers & 0.01 & [28; 51; 52; 53] \\ PEI ULTEM & 340 & 217 & 65 \(\times\) 10\({}^{-6}\) & 101 & 86 & 17 & stable & 0.40 & [28; 54; 55; 56] \\ \hline \hline \end{tabular}  
* The exact glass transition temperature is debated [32]. \({}^{\text{b}}\) Measured at room temperature. \({}^{\text{c}}\) Measured at \(-\)196 \({}^{\text{c}}\)C.
* Measured at 150 \({}^{\text{c}}\)C.  
\end{table} 
Table 1: Comparison of advanced thermoplastics for space applications. the limited organic phase content in the composite and the high weight percentage of regolith are expected to provide additional UV protection, acting as an effective shield for the material's inner core.
# The Montreal Experience: A Retrospective Study Part I--Basic Principles and Treatment Algorithm
## 1 Introduction
### The Second Pillar: Binocular Vision Assessment  
There are numerous factors to convince practitioners to pay attention to in binocular vision assessment in light of myopia onset and development. Kids who may become myopic are show a higher accommodative lag versus those who may remain emmetropic [14]. Children with higher AC/A ratios are 20 times more at risk to develop myopia [15]. If a practitioner sees a young patient showing esophora at near and a high lag, still being hyperopic, one of the only ways to prevent myopia onset is to control their binocular function. Red flags for myopic evolution are raised in the presence of negative relative accommodation (NRA) = +2D, positive relative accommodation (PRA) \(<-2\)D and rock flips (+2/\(-\)3) less than 13 cycles per minute.  
From the recent literature, it is possible to associate accommodation and convergence problems with progressive myopia [16]. Thus, accommodation modifies the quality of the optical signal received by the retina and can therefore influence the response to a given defocus. Normal accommodation is necessary for emmetropization, as well as for effective control of its evolution.  
The ME approach aims to treat the binocular vision problems identified at baseline before undertaking myopic control treatment. Likewise, the presence of binocular dysfunctions may also dictate the strategy used when myopia is developed [17]. Thus, patients with a strong exo deviation at near will be better served with anti-myopia glasses, with or without prisms, than with contact lenses. A combination of both contact lenses and prismatic glasses may work even better.
# SATCOM Earth Station Arrays Anti-Jamming Based on MVDR Algorithm
## 1 Introduction
Reference [12] replaces the traditional one-dimensional uniform linear frequency control array structure with a frequency control array multi-input multi-output structure and combines the MVDR algorithm to suppress external jamming outside the platform. Reference [13] uses a joint graph Fourier transform to map multi-channel noisy speech signals to the graph frequency domain. Based on the characteristics of the graph frequency domain, the MVDR algorithm is designed to enhance multi-channel noisy speech signals and improve speech quality and intelligibility. Based on the above analysis, the MVDR algorithm can effectively suppress external jamming in various scenarios, but the scenarios mainly focus on array forms where the distance between elements meets half-wavelength and do not consider the impact of signal incidence angle on signal gain. This article addresses the problem of a decrease in communication quality or even communication interruption caused by jamming to the downlink of satellite communication earth station. Firstly, the expression of the gain of a reflector antenna is deduced based on the calculation method of the maximum gain of an antenna, the pointing error of the antenna, and the maximum gain constraint of the sidelobe, and a mathematical model for the receiving signal of the reflector antenna array is established. Since the antenna elements are reflector antennas for the earth station, their gain varies with the direction of the incident signal. Therefore, a MVDR beamforming algorithm based on reflector antennas is proposed and applied to the satellite communication earth station array. Then, by using computer simulation with variables of the number of snapshots and satellite signal power flux density, the effectiveness of the proposed algorithm relative to conventional beamforming and the algorithm based on the sampling matrix inversion is verified. The output SINR of the array with different jamming incident angles is simulated by setting actual parameters of the satellite communication system, and the reason for the poor jamming resistance performance at some angles is theoretically derived.
## 4 The Bassnet Model
### Learning Latent Representations to Encode the Relationship between \(X\) and \(Y\)
#### 4.4.1 Encoder/Decoder: Stacked Dilated Residual Blocks  
Dilated residual networks [61] combine dilated convolution and additive skip connections in stackable units, referred to as dilated residual blocks. The dilated convolution allows for large receptive fields in higher layers without the need for large kernels, strides, or pooling. This means that high level features can be represented at the same resolution as the input itself. The additive skip connections are a solution to the problem that the gradient of the loss in traditional deep networks tends vanish in the lower layers of the network, which limits maximal depth at which networks can be effectively trained [62]. Dilated residual networks have proven to be especially successful in tasks with dense targets, like image segmentation, and object localization [61], as well as stereo-image depth estimation [63].  
In this work, we use an adaptation of the dilated residual block proposed in [61] as the building block for the encoders and decoders. Figure 3 depicts the block. In this structure, the information flows from input to output through two pathways in parallel. The left pathway involves a typical convolution layer with configurable parameters: the kernel size \(k\), number of kernels \(n\), and dilation factor \(d\). The right pathway convolution uses kernels of size 1 (dilation does not apply in this case) and, thus, does not compute any features from the input. Instead, it outputs \(n\) linear combinations of the input to ensure shape compatibility for elementwise addition to the \(n\) feature maps of the left pathway. The pathways further include batch normalization operations. After the elementwise sum of the pathways, a rectified linear unit allows for a non-linear response.  
Figure 2: Schematic illustration of multi-resolution compression and expansion of data windows.  
A practical advantage of the residual blocks as used here is that the shape of the output is identical to the shape of the input, due to the fact that the convolution stride is always 1 and there is no pooling operator. Thus, residual blocks can be stacked very simply to obtain deeper architectures.  
The encoders and decoders in the current architecture are identical, except for the fact that the decoders use transposed convolution. The weights between the encoder convolutions and decoder transposed convolutions are tied.
## 5 Results
### Quality Assessment of the Extracted DSMs
The land cover factor presented a partial eta-squared statistic (\(\rm{n_{p}}^{2}\)) of 56.21%, while a much lower value of 17.18% was attained for the stereo pair factor. In the case of NMAD, both factors resulted in being significant at \(p<0.05\) level, with \(\rm{n_{p}}^{2}\) values of 41.73% and 40.06% for land cover and stereo pairs, respectively. Overall, the best vertical accuracy results, considering all statistic descriptors used, were achieved in the case of bare soil, followed by plastic covered greenhouse and, finally, urban areas. Regarding the sensor tested, the vertical accuracies from Deimos-2 stereo pairs were worse than the ones derived from WV2. Focusing on the 9 DSMs corresponding to the plastic covered greenhouse land cover, significant differences at \(p<0.05\) (for SD and LE95) and \(p<0.10\) (for RMSE\({}_{\rm Z}\)) were found between the WV2 stereo pair and the Deimos-2 stereo pair acquired in July. However, no significant differences were detected when comparing the DSMs derived from the WV2 stereo pair and the second stereo pair from  
Figure 4: DSMs and completeness values corresponding to the three subareas of urban land cover (UR1, UR2 and UR3) generated from the first and second Deimos-2 stereo pairs (second and third column) and the WV2 stereo pair (fourth column). The reference LiDAR is depicted in the first column. Deimos-2 (August). More robust significant differences were achieved between the two Deimos-2 stereo pairs and the WV2 one when using NMAD. With regard to urban areas, significant differences (\(p<0.05\)) were detected for SD, RMSE\({}_{Z}\) and LE95 statistics between the WV2-based DSM and the DSM derived from the stereo pair of Deimos-2 taken in August, statistically significant differences were not observed with respect to the DSM based on the stereo pair of Deimos-2 acquired in July. Again, NMAD was able to clearly differentiate the worst results obtained with the stereo pairs of Deimos-2. Finally, no significant differences could be found using standard statistics (SD, RMSE\({}_{Z}\) and LE95) between the DSMs produced from WV2 and Deimos-2 stereo pairs when working on bare soil land cover, mainly due to the variability in the vertical accuracy attained for the Deimos-2 imagery (Table 6). However, NMAD did detect significant differences (\(p<0.05\)), indicating that the vertical accuracies obtained from WV2 were significantly better than those obtained with Deimos-2. Figure 5: DSMs and completeness values corresponding to the three subareas of bare soil land cover (BS1, BS2 and BS3) generated from the first and second Deimos-2 stereo pairs (second and third column) and the WV2 stereo pair (fourth column). The reference LiDAR is depicted in the first column.