esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of an Anthropomorphic Prosthetic Hand with Underactuated Mechanism
## 5 Conclusions  
In this paper, we developed an anthropomorphic prosthetic hand for wrist or forearm amputees. The hand was designed mechanically in an underactuated system to realize grasping motions. In this paper, we also suggested a new method for designing couple joints in an underactuated system. By using two four-bar linkages composed of a combination of linkages and gears, the positions of each phalanx were analyzed accurately, and the sustainability of performance and available payload were improved. Torsional springs installed between coupled joints realized passive motions for self-adaptive grasping. In addition, by using CV joints, the torque produced from the outside can be transferred to finger modules regardless of adduction/abduction motions. From these two main design concepts, the hand has the advantages of a linkage mechanism and can be designed with a more compact weight and size than other published hands. The final design of the developed hand has a weight of 475 g and a normal human hand size of 175 mm. The prosthetic hand realizes self-adaptive grasping, adduction/abduction, and natural motions such as flexion and extension. The hand's fundamental grasping performance on everyday objects was verified experimentally. In further works, we will perform in-depth experiments with diverse subjects and complex tasks in order to improve its comfort and functionality. We also have a plan to make the prosthetic hand lighter by analyzing which parts can be replaced by light material and fabricating the parts using 3D printing.  
Conceptualization, W.R., Y.C.; Methodology, W.R., Y.C. and S.L.; Formal analysis, W.R., Y.G.L. and Y.J.C.; Writing--original draft preparation, W.R.; Supervision, Y.C., Y.J.C. and S.L.; Project administration, S.L.; Writing--review and editing, Y.C., Y.J.C. and S.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the convergence technology development program for bionic arm through the National Research Foundation of Korea (NRF) funded by the Ministry of Science & ICT (No. 2015M3C1B2052811), Republic of Korea. This work was also supported by the Technology Innovation Program (or Industrial Strategic Technology Development Program) (20001856, Development of robotic work control technology capable of grasping and manipulating various objects in everyday life environment based on multimodal recognition and using tools) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea).  
The authors declare no conflict of interest.  
Figure 9: Performance of diverse tasks by a patient: grasping a hair brush and spoon (**upper**) and pushing computer mouse and remote control buttons (**lower**).
## 2 Materials and Methods  
### Overview of Material Preparation  
TE materials are being synthesized via different methods, such as microwave-assisted thermolysis [17], mechanical alloying [5; 23; 24], solve-thermal [25], fast quenching methods [23], nanocrystal synthesis [26; 27; 28], melting and annealing [29; 30], and solid-state reaction [23; 31]. In this study, the skutterudite materials were prepared via melting and annealing, bismuth telluride was prepared via a chemical synthesis route, and antimony telluride was prepared via physical vapor deposition followed by spark plasma and/or hot press methods [3; 32; 33; 34]. Irrespective of the method of preparation chosen, characterization processes, such as Scanning Electron Microscopy [5; 35], Transmission Electron Microscopy [36; 37], and X-ray Powder Diffraction [38; 39], are carried out on the dried powders. Scanning Electron Microscopy (SEM) is for the analysis of surface topography and the composition of the samples. Transmission Electron Microscopy (TEM) is used to create high magnification images of the internal structure of the samples for analysis. X-ray Powder Diffraction (XRPD) is used for the phase identification of the crystals. The sample undergoes compaction through either spark plasma sintering [30; 38; 39], a cold pressed technique [40; 41], a hot pressed method [42; 43], or Arc melting [26]. Furthermore, a diamond saw blade is used to cut the compacted samples (pellets) into appropriate disc shapes and prismatic bars. Disc-shaped samples can be used to measure thermal diffusivity (\(D\)) and specific heat capacity (\(C_{p}\)) via Laser Flash Analysis (LFA) and Differential Scanning Calorimetry (DSC) systems, respectively. The prismatic bar shaped samples, used by the Seebeck coefficient and electric resistance measurement system, are for the measurement of the Seebeck coefficient (\(S\)) and the electrical resistivity (\(\mathcal{P}\)). The electrical conductivity is the reciprocal of the electrical resistivity and the total thermal conductivity \(\kappa=D\)-\(p\)-\(C_{p}\), where \(\rho\) is the bulk density of the disc obtained from the Archimedes method. In Hall effect measurements, the sign of the Hall coefficient \(R_{H}\) determines the carrier type (holes or electrons), while the concentration of the charge carrier is \(n_{H}=(eR_{H})^{-1}\) and the carrier mobility is \(\mu_{H}=\sigma R_{H}\), where \(e\) is the elementary charge and \(\sigma\) is the electrical conductivity [17]. With the results obtained from the measurements, the figure of merit and power density can be determined. Materials (n-type and p-type) found suitable for thermoelectric applications could be used in TEG to generate electricity from heat.
# Hybrid Positioning for Smart Spaces: Proposal and Evaluation
## 5 Implementation and Evaluation of the Solution
### Accuracy Analysis  
The distribution of errors committed by the positioning system in the previous test is plotted in Figures 5 and 6. Figure 5 includes all the position estimations returned by each technology during the test. On the contrary, Figure 6 only includes the most accurate position approximations at each moment, i.e., the final estimation chosen. The histogram labeled with the hybrid solution in Figure 5 includes the final error produced by the hybridization process, while the rest of histograms show the error produced by each technology itself. Hence, all data included in Figure 6 belong to the hybrid part of Figure 5.  
As can be seen in Figure 6, most of the calculated positions by the hybridization process had an error below 10 m, and a great part below 5 m. Most of the high-impact errors were due to GPS, since it was used near the building with a metal-made facade that reflected part of the satellite signals. However, there were some errors between 10 and 20 m that were attributed to WiFi fingerprinting, which presented deviations when the user moved near the floor edges. This was due to the bad distribution of fingerprints surrounding the user at these locations. Although accuracy was highly impacted by the wide usage of WiFi fingerprinting, overall results were improved by the small areas where Bluetooth LE proximity and ID card readers were used. Here the errors detected were usually below 5 m. The Android Network positioning source was not used, since the accuracy value obtained for this technology (based on cell ID and WiFi proximity) was worse than the accuracy estimations of the rest of technologies. This is the reason why in Figure 6 there are not values in the NET histogram.  
A summary of the error results for each technology is included in Table 1. From these results we can estate that the mean error of the system was 4.62 \(\pm\) 0.31 m for our testbed involving a mix of technologies. It is important to remark the challenging scenario considered, where the GPS signal is partially disrupted by the metal-made building facade, increasing the overall error. These results could be further improved with a lower resolution grid for WiFi fingerprinting, deploying more Bluetooth LE tags and entering more spaces with a card reader available. Many rooms in the building provide such capability and we have also disabled many other Bluetooth LE tags in order to present a realistic and cost-efficient setup.  
Figure 5: Histograms of the error committed for each technology (all the samples).  
Figure 6: Histograms of the error committed for each technology (only the selected samples).
# Semantic Segmentation of Aerial Imagery Using U-Net with Self-Attention and Separable Convolutions
## 2 Literature Review  
### CNN Approach for Aerial Imagery Segmentation  
Leveraging convolutional neural networks (CNNs), this approach involved precise pixel labeling for training and extracting intricate building areas. This method significantly contributed to the advancement of semantic segmentation in aerial images, utilizing CNNs' robust pattern recognition capabilities to enhance spatial understanding and provide valuable insights into the distribution of buildings. The refined methodology showcases ongoing progress in deploying deep learning techniques for extracting meaningful information from complex visual data [15].
# Sep-RefineNet: A Deinterleaving Method for Radar Signals Based on Semantic Segmentation
## 1 Introduction
* A multi-receptive field and multi-channel separation semantic segmentation network named Sep-RefineNet is used to learn the semantic features of the FCM. Through position decoding, the deinterleaving algorithm is made more intelligent, reducing the dependence on manual experience parameters. * Under the influence of aliasing pluses and missing pluses, it can sort constant PRI, staggered PRI, and jittered PRI, and improve the robustness and accuracy of the deinterleaving. Section 2 of this paper introduces the related work about radar signals deinterleaving. Section 3 introduces the deinterleaving system framework. It includes the methods of encoding and position decoding of the FCM and the semantic segmentation network named Sep-RefineNet. Experimental data and results are given in Section 4, which also analyzes the effectiveness of the proposed method. Finally, the work of this paper is summarized in Section 5.
# A Neural-Network-Based Watermarking Method Approximating JPEG Quantization
## 5 Computer Simulations
### Evaluation of the Proposed Attack Layer
#### 5.2.1 Experimental Conditions  
The training and test images were selected as they were in ReDMark [11]. The training images were 50,000 images of \(32\times 32\) pixels from CIFAR10 [21] (\(H=32\), \(W=32\)). An \(h\times w\)-bit watermark was embedded, where \(h=4,w=4\). The watermark was randomly generated. Therefore, the amount of watermark embedded per pixel was approximately 0.0156 bits per pixel. In the parameters used for training, the block height and width sizes were set to \(M=8\) and \(N=8\), respectively. The parameter of the loss function (20) was set to \(\gamma=0.75\), the number of learning epochs was set to 100, and the mini-batch size was set to 32. For the parameters of SGD, the training rate was set to \(10^{-4}\), and the moment was set to 0.98. For training the proposed attack layer, the gradient of the QAF (13) was set to \(\beta=1000\), and the number of the hyperbolic tangent functions was set to \(n=500\). In the attack layer of JT-Net and the proposed method, the quantization level was set to \(Q=70\), and the quantization table \(T_{70}\) was used. The embedding, attack, and extraction networks were all connected, and the network was trained using the training images and watermarks. Here, the embedding intensity was fixed at \(\alpha=1\).  
For testing, 49 images of \(512\times 512\) pixels from the University of Granada were used. These images were divided into \(32\times 32\) pixel subimages, and the embedding process was performed on each of them. The 256 subimages were given to the network as  
Figure 10: QAF-applied images and their PSNRs.  
Figure 9: Histogram of PSNRs for JT-Net-applied images and QAF-applied images.  
test images. Meanwhile, a \(32\times 32\)-bit watermark was randomly generated. One watermark was embedded four times in one image. That is, the watermark was divided into \(4\times 4\)-bit subwatermarks, and finally each subwatermark was embedded in one subimage. An estimated watermark was determined by bit-by-bit majority voting because the same watermark was embedded four times in one image.  
As stated in Section 3.1, the attack layer was not used during testing. The test images and the watermarks were used to output stego-images in the embedding network. Here, the embedding intensity was set to values from \(\alpha=0.5\) to \(1.0\). The stego-image is published. Subsequently, we assumed that it was JPEG-compressed by some JPEG tool with quantization levels \(Q=10,20,\cdots,90\). The compressed stego-images were input to the extraction network, and the estimated watermarks were output. These networks were trained 10 times with different initial weights for the comparison of our network with the JT-Net on image quality and BER. The mean and standard deviation of structural similarity index measures (SSIMs), PSNRs, and BERs were calculated.
# Adsorption of Lead and Nickel on to Expanded Graphite Decorated with Manganese Oxide Nanoparticles
## 3 Results and Discussion
### Effect of Humic Acids (HA)
Regarding the partition coefficient (PC), the higher value of PC is generally referred to as a good adsorption affinity [32; 57]. However, our results found that the PC values are inversely proportional to the initial metals concentration in solution. This mean that the higher affinity of metal ions towards the MONPs-EG could be achieved at lower initial concentration. Therefore, an outstanding adsorption affinity might not necessarily correspond to an excellent adsorption capacity. The partition coefficients obtained in this study are comparable to the results of previous studies with various adsorbents although there are distinct differences in adsorption capacities between them (Table 6). The magnitude of the computed PC values is generally higher when the initial concentration loaded on to the batch system becomes lower due to the enhanced efficiency of adsorptive processes. However, the occurrences of exceptionally high PC value for Pb\({}^{2+}\) at the lowest initial concentration of 0.10 mM are still somewhat unusual. It may possibly be attributed to the unusually preferential removal of Pb\({}^{2+}\) under the initial conditions which considerably stimulated the interaction between the adsorbate (Pb\({}^{2+}\)) and the adsorbent (MONPs-EG). The strong affinity of Pb\({}^{2+}\) to MONPs-EG over that of Ni\({}^{2+}\) can also be ascribable in terms of thermodynamic consideration in that the magnitude of \(\Delta\)G\({}^{0}\) was significantly higher in Pb\({}^{2+}\) than in Ni\({}^{2+}\) (Table 5). Figure 7: Effect of humic acids (HA) on adsorption behaviors of MONPs-EG for: (**a**) Pb\({}^{2+}\); (**b**) Ni\({}^{2+}\) in single and binary metal solutions.
## 2 Mathematical Model
### Colloidal-Particle-Associated Contaminant Transport in Saturated Porous Media  
Assuming the all pore space is accessible to colloidal particles (i.e., no dead pores for colloidal particle transport) and colloidal particles are irreversibly retained during contaminant transport (i.e., no detachment), the mass balance equation for the colloidal-particle-associated contaminant transport in saturated porous media can be expressed as [9; 30]:  
\[\frac{\partial(nC)}{\partial t}+\rho\frac{\partial S_{e}}{\partial t}+\rho \frac{\partial S_{k}}{\partial t}+\rho\frac{\partial S_{c}S_{ic}}{\partial t} =-q\frac{\partial(nC)}{\partial z}+\frac{\partial}{\partial z}\left(nD\frac{ \partial C}{\partial z}\right), \tag{7}\]  
where \(C\) (M L\({}^{-3}\)) is the dissolved contaminant concentration in aqueous phase, \(\rho\) (M L\({}^{-3}\)) is the bulk density of sand medium, \(S_{e}\) (M M\({}^{-1}\)) is the concentration of contaminant sorbed instantaneously to the sand, and S\({}_{k}\) (M M\({}^{-1}\)) is the concentration of contaminant sorbed kinetically to the sand. In addition, \(S_{ic}\) (M M\({}^{-1}\)) is the contaminant sorbed by retained colloidal particles. For three of the time derivative terms on the left-hand side associated with the adsorption of contaminant, the application of the Langmuir isotherm model on the contaminant-sand adsorption and the contaminant-retained colloidal particles adsorption leads to [31]:  
\[\rho\frac{\partial S_{e}}{\partial t} =\rho fK_{1}q_{\max(1)}\frac{\partial C}{\partial t}, \tag{8}\]  
\[\rho\frac{\partial S_{k}}{\partial t} =\rho\omega_{1}\bigg{[}(1-f)\frac{q_{\max(1)}K_{2}C}{1+K_{2}C}-S _{k}\bigg{]}, \tag{9}\]  
\[\rho\frac{\partial S_{c}S_{ic}}{\partial t} =\rho\omega_{2}\bigg{[}\frac{q_{\max(2)}K_{2}C}{1+K_{2}C}S_{c}-S _{ic}S_{c}\bigg{]}+\mathrm{k}_{c}\rho S_{c}S_{ic}, \tag{10}\]  
where \(f\) (-) is the fraction of instantaneously sorbed contaminant in the two-site adsorption model (\(f\) was assumed to be 0.01 in this study), \(K_{i}\) (L\({}^{-3}\) M) is the distribution coefficient of dissolved contaminant to the adsorbent (\(i\) = 1 for sand, \(i\) = 2 for colloidal particle), \(q_{\max(i)}\) (M M\({}^{-1}\)) is the adsorption capacity, \(\omega\) (T\({}^{-1}\)) is the first-order rate constant, and \(k_{c}\) (T\({}^{-1}\)) is the first-order reaction rate constant. The application of the Langmuir isotherm in Equations (8)-(10) keeps the amount of adsorbed contaminant less than \(q_{\max}\).
# The Polar Code Construction Method in Free Space Optical Communication
## 6 Conclusions  
In this paper, the polar code was used as the error-correcting code on FSO information transmission in a strong turbulence channel, and the STPO construction method was proposed which is suitable for an atmospheric strong turbulence channel, then the performance comparison between the STPO and Monte Carlo method with different code lengths, code rates, and turbulence intensities was analyzed. The results indicated that when the code rate was 0.25 and the turbulence intensity \(\sigma_{R}^{2}=2.3\), STPO had almost the same error performance as MC. When BER was \(10^{-4}\), the turbulence intensity \(\sigma_{R}^{2}=3.5\), the code rate was 0.5, and code length was 512, and the turbulence partial sequence produced a loss of about 0.03 dB. When the length increased to 1024, the BER loss was about 0.17 dB. The BER loss was approximate when the turbulence intensity met the condition that \(\sigma_{R}^{2}=5.2\) or \(\sigma_{R}^{2}=3.5\). STPO adopted offline ways to construct polar codes, which generated negligible complexity in actual communication and were not affected by SNR changes. This advantage was even more obvious at low encoding rates and low turbulence intensity. The influence of atmospheric turbulence on the FSO system is relatively complex, and the traditional polar code construction method generated a large number of coding complexities, which limited the transmission performance of FSO system. The study in this paper provides more practical references for the practical application of the FSO system.  
Conceptualization, Y.C. and W.L.; methodology, W.L.; software, W.L.; validation, X.P.; formal analysis, J.Z.; data curation, Y.L.; writing--original draft preparation, W.L.; writing--review and editing, Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Education Commission Foundation of Chongqing (Grant No. KJ1500934), Science and technology project of Chongqing Education Commission (Grant No. KJ1709205), the graduate scientific research innovation project of Chongqing (Grant No. CYS18311), the basic and frontier research program of Chongqing (Grant No. cstc2015 cyjA40051), the Science and Technology Program of Chongqing Banan (Grant No. 2019 TJ07) Science and Technology Research Youth Project of Chongqing Education Commission (Grant No. KJQN202101124).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results
### In Vitro Assays  
3.5.1. Arabidopsis Thaliana Inoculation  
Inoculation of the model plant _A. thaliana_ with the three selected strains and their different combinations (BR15, OR15, RB13, BR5 + OR15, BR5 + RB13, OR15 + RB13, and BR5 + OR15 + RB13) was used to assess their capacity to improve growth under different salt concentrations (Figure S4 and Figure 3). Analysis of morphological parameters of the plants, root elongation (Figure S4A), fresh weight (Figure S4B) and leaf area (Figure S4C) showed a significant decrease (\(p\)\(\leq\) 0.05) of these different growth parameters under salinity stress. Inoculation with bacteria significantly improved the weight mass (Figure S4B) and the leaf area of _A. thaliana_ (Figure S4C) at low NaCl concentrations (0 and 50 mM). At high NaCl concentrations (100 and 150 mM), the different morphological parameters were significantly reduced. Bacterial inoculation improved the aerial part of the plants (fresh weight and leaf area) (Figure 4B,C). Whereas, the root length seemed not to be affected by the increase in salt (Figure S4A). The analysis showed that the effect of bacteria was visible either for the mono inoculation or the different combinations. However, a better response was obtained for the OR15 and RB13 strains and their combinations (Figure 4A-C).  
Salinity resulted in notable reductions in chlorophyll contents a, b and total (\(p\) < 0.01) in A. thaliana plant (Figure 2A-C). Treatments with bacteria and their combinations improved the chlorophyll a (Figure 2A), chlorophyll b (Figure 2B), and chlorophyll a + b (Figure 2C) contents at all salinity levels. Significantly improved rates were observed for the OR15 and RB13 strains and their combinations (Figure 2A-C).  
Figure 2: Effect of bacteria isolates on (**A**) Chlorophyll a (\(\mu\)g/g FW), (**B**) Chlorophyll b (\(\mu\)g/g FW), and (**C**) Chlorophyll a + b (\(\mu\)g/g FW) contents against different concentrations of NaCl. Values are means \(\pm\) SD of three replicates. GraphPadPad Prism 8 was used for statistical analysis. Two-way ANOVA setting followed by [PERSON]’s multiple comparison post-test was used to identify the differences between different treatments each family group. Bars in each group with different letters are significantly different at \(p\leq 0.05\). **** indicating the significant variations \(p<0.0001\) in comparison to the control sample.  
Figure 3: In vitro effect of bacterial isolates on _Arabidopsis_ seedlings growth exposed to different NaCl concentrations.
# Optimization of Uplift Piles for a Base Plate Considering Local Anti-Floating Stability
###### Abstract  
This paper focuses on the optimization of uplift piles for a base plate considering local anti-floating stability. According to the force characteristics of the base plate subjected to buoyancy, a bi-directional evolutionary structural optimization (BESO) process is proposed. The optimization process takes the pile length as the design variable and the pile end displacement as the sensitive coefficient. The proposed process is conducted with FLAC3D to optimize the length of each uplift pile, including two cases with different pile diameters and spacing. The optimization process shows that the deformation degree of the base plate is significantly reduced when the piles are adopted with large diameters and sparse spacing, and oscillates at a low level when the piles are adopted with small diameters and dense spacing. Consequently, the design method of uplift piles considering the local anti-floating stability is proposed by referring to the optimization results of two cases. Finally, the proposed design method is applied to a practical project, and the original design of the uplift piles is optimized. The comparison results show that the deformation degree of the base plate of optimization designs is significantly lower than the original design.  
uplift piles; BESO; numerical simulation; local anti-floating stability Article