esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of Additive and Current Conditions for Void-Free Filled Through-Silicon Via
## 3 Results and Discussion
### Tendency of Plating by Additive  
The effects of each additive on plating are presented in Figure 5[2; 4; 13; 21]. As shown in Figure 5a, PEG and JGB captured Cu\({}^{+}\) ions. This cation is strongly adsorbed onto the surface of the wafer owing to electrostatic interaction with the chloride ion (Cl\({}^{-}\)) on the surface of the Cu electrode, which prevents the precipitation of Cu. As shown in Figure 5b, SPS is an accelerator that promotes the precipitation of Cu through the reaction of the Cu\({}^{+}\) ion with the sulfur trioxide ion (SO\({}_{3}^{-}\)) of SPS. PEG suppressed the plating rate on the surface, SPS accelerated the plating rate in the hole, and JGB suppressed the plating rate at the edge, thereby controlling the clogging of the hole due to the edge effect [2; 21].  
Copper electroplating was performed with DC 1 ASD (\(-10\) mA/cm\({}^{2}\)) to confirm the effect of additives on plating. The effect of additive on plating was confirmed by analyzing images taken with an OM.  
Figure 6a-d shows the plating cross-sectional image and filling ratio graph according to PEG concentration. The experiment results show that as PEG concentration increased, the filling ratio increased, and the highest filling ratio was observed at 500 ppm. Compared with the CV analysis, it was confirmed that plating occurred inside the TSV as the plating rate of the surface was suppressed and clogging of the hole entrance was controlled by PEG. However, bottom-up filling should be undertaken for void-free filling, but it is difficult to satisfy the condition using only PEG. Therefore, the interaction of 500 ppm of PEG with other additives and current conditions was studied.  
Figure 7a-d shows the cross-sectional image of the sample and filling ratio graph according to SPS concentration. A concentration of PEG of 500 ppm as well as the addition of SPS was studied for AR 1:2.5 TSV, with (a), (b), and (c) representing SPS concentrations of 1 ppm, 3 ppm, and 5 ppm, respectively. The addition of 500 ppm PEG and SPS to the VMS bath increased the filling ratio when SPS concentration increased. Also, compared with the PEG test, it is observed that the bottom side is plated even though the aspect ratio is increased. However, when the SPS concentrations of 3 ppm and 5 ppm were compared, plating from the bottom of the hole was observed for 3 ppm but not  
Figure 5: A schematic diagram of additive behavior in plating solution. Action of CI ion: (**a**) PEG and (**b**) SPS in plating bath; (**c,d**) effect of additive in electrodeposition.  
Figure 8a-d demonstrates that the filling ratio increases when JGB concentration increases, and the highest filling ratio was obtained at JGB concentration of 30 ppm. As the amount of additive increased, the rate of clogging of the hole slowed and the proportion of the void was small, but complete filling did not occur. Therefore, complete filling of TSV was performed by pulse plating.
# Failure Analysis of Gantry Crane Slewing Bearing Based on Gear Position Accuracy Error
## 2 Slewing Bearing Mechanical Analysis and Test
### Pre-Assembly Engagement Mechanical Properties  
Based on the meshing relationship between the pinion and the large ring gear of the slewing ring, the assembly is completed in SolidWorks and then imported into ANSYS Workbench for analysis. Since the shape of the large ring gear structure of the pinion and the slewing bearing is more regular and there are more tooth surfaces, we considered using sweeping and curvature-based meshing methods. The minimum size of the overall mesh is 9 mm, the curvature coefficient is 0.5, and the mesh correlation value setting size is 30.  
When defining the tooth contact pair in ANSYS Workbench, the surface-to-surface contact form is selected, in which the target surface selects the large ring gear surface. The contact surface selects the pinion surface. The frictional contact type is used. The contact coefficient is selected to be 0.15. The \"Pure Penalty\" (penalty function equation) is used to reasonably adjust the contact surface contact stiffness. Finally, we complete the definition of the gear contact pair. In the case of ideal gear meshing, the roller seat movement of the outer ring of the slewing ring is restricted, so when setting the boundary condition, the degrees of freedom in six directions of the raceway surface inside the large ring gear of the slewing ring are limited, and according to the parameters of the slewing mechanism of the MQ4038 gantry crane and the uniform contact of the tooth surface of the gear, a rated torque of 3.5 \(\times\) 107 N\(\cdot\)mm is added to the pinion. The mashing results are shown in Figure 5.  
Figure 4: Schematic diagram of the meshing results of the Simulation module.  
Figure 5: Meshing results.  
After setting contact parameters and boundary conditions [9; 10], the static analysis of the large gear ring is performed. The static analysis of the large gear ring is shown in Figure 6. The maximum stress at the root of the gear teeth is 208.8 MPa, which does not exceed the permissible stress. The stresses at the root of the gear teeth at the mesh are more uniform (red area), and the equivalent stresses at the root of the upper teeth and the root of the lower teeth are 205.08 MPa and 205.04 MPa, respectively, which are more consistent with the ideal mesh.
# Cooperative Buffering Schemes for Time-Shifted Live Streaming of Distributed Appliances
## 3 Cooperative Buffering Schemes for Time-Shifted Live Streaming  
In this section, we propose two cooperative buffering schemes for P2P time-shifted live streaming among networked PVRs. We first describe the overall architecture of our system. We then explain the schemes based on the _D_egree of _D_fedicency and \(P\) Proximity (_D_ _DP_) and the _R_RC_(Ratio of Playback Requests to Chunk Copies) in detail.
## 7 PKA Regulates Lipid Metabolism  
In mammalian thermogenesis, \(\beta\)3-adrenergic receptor stimulation of G-proteins leads to the production of cAMP, and, therefore, PKA activation [82]. PKA activates hormone-sensitive lipase (HSL), which releases the glycerol and fatty acids required for the physiological activation of uncoupling protein 1 (UCP1), leading to increased production of heat [83]. Although _C. elegans_ do not regulate body temperature [84], KIN-1/PKA-C does regulate _C. elegans_ response to cold stress [85]. Under cold conditions, PKA signaling is activated in the intestine, where KIN-1 activity leads to increased expression of _host-1_/HSL, fat hydrolysis, increased glycerol availability, and increased cold tolerance. KIN-1 is also required in the neurons for cold tolerance. Although the mechanism is not known, perhaps neurons pass a signal to the intestine, which then upregulates _host-1_ expression and lipid hydrolysis [85].  
PKA activation also results in lipolysis of stored lipid droplets in response to food deprivation. KIN-1 phosphorylates and stimulates the adipose triglyceride lipase ATGL-1 to form a lipid droplet-localized protein complex containing ATGL-1 and the lipid droplet protein LID-1, leading to lipid hydrolysis. The suppression of _atgl-1_ or _lid-1_ hinders fasting-induced lipolysis in adult worms (Figure 3) [86]. Lipolysis is reduced in low-oxygen conditions. Hypoxia-inducible factor (HIF) is a transcription factor that drives adaptive responses to low-oxygen levels, including the suppression of lipolysis. In mammals and _C. elegans_, hypoxia reduces cAMP and PKA activity levels, although the mechanism by which HIF-1 regulates PKA signaling in _C. elegans_ is not clear. Exposure of _C. elegans_ to hypoxia (1% O\({}_{2}\)) prevents PKA-stimulated lipolysis by targeting ATGL-1 for proteasomal degradation [87]. These studies are examples of the interaction between PKA activation and core metabolic processes.  
PKA regulation of lipid metabolism impacts overall lifespan in most organisms [88; 89; 90]. In _C. elegans_, PKA activation of lipid catabolism in muscle cells and subsequent induction of AMPK/AAK-2 expression in non-muscle tissues, including neurons and intestinal cells, leads to enhanced mitochondrial metabolism and lifespan extension. Activating KIN-1/PKA-C and ATGL-1 in muscle cells led to a decrease in abundance of intramyocellular lipid and an extension of lifespan. Conversely, depletion of _atgl-1_ in all tissues shortened lifespan [91].
## 2 Principles
### Interplay of PCPE and Odd-Bit QAM Signals  
Although PCPE is very effective for square QAM constellations [17], as illustrated in Figure 2a-c, when it is applied to cross-shaped odd-bit QAM constellations, it will cause serious incompatibility problems. As shown in Figure 2d,e, the absence of signal points at the four corners of the odd-bit QAM constellation corresponds to the absence of high-power points on the main axis of the squared constellation (as shown by the green circles), which will seriously reduce the accuracy of the PCPE algorithm. Taking 128 QAM and 256 QAM as an example for a qualitative comparison, after applying the PCPE algorithm to perform phase recovery on the constellation diagrams in Figure 2a,d, the results obtained are as shown in Figure 2c,f. It can be clearly seen that the residual phase error in the 128 QAM constellation diagram is more serious than 256 QAM.  
Figure 2: Illustrations of the incompatibility between odd-bit QAM and PCPE: (**a**) 256 QAM constellation with a \(\pi/6\) phase rotation; (**b**) squared 256 QAM constellation with a \(\pi/6\) phase rotation; (**c**) 256 QAM constellation after being recovered by the PCPE algorithm; (**d**) 128 QAM constellation with a \(\pi/6\) phase rotation; (**e**) squared 128 QAM constellation with a \(\pi/6\) phase rotation; (**f**) 128 QAM constellation after being recovered by the PCPE algorithm.  
A quantitative analysis of the interplay between PCPE and odd-bit QAM signals was performed by Monte Carlo simulations. Under different SNR conditions, the PCPE algorithm is applied for phase recovery of the 128 QAM and 256 QAM signals subject to a fixed \(\pi/6\) phase rotation, and the MI performance curves obtained are as shown in Figure 3. For 256 QAM, the PCPE algorithm can achieve relatively accurate recovery performance in the entire SNR interval being tested, and the AIR loss compared to the MI performance in the ideal state is no more than 0.41 bit/symbol. In contrast, for 128 QAM, which is an odd-bit QAM, the PCPE algorithm will have obvious performance defects in the high SNR range, resulting in an AIR loss of up to 0.99 bit/symbol. These results show that the PCPE algorithm is not compatible with the regular cross-shaped odd-bit QAM signal, which will lead to a serious decrease in the estimation accuracy, and the deterioration of the reliability of the entire communication system.
# Analysis of Bifurcation Vibrations of an Industrial Robot Arm System with Joints Compliance
## 5 Experimental Studies  
To verify the accuracy of the mathematical model, experimental tests were carried out using the test stand shown in Figure 8. The stand included the ABB IRB 1600 robot (ABB Ltd., Zurich, Switzerland), an electrodynamic exciter, an impedance head, a set of vibration sensors, an LMS Scadas mobile recorder (LMS International, Leuven, Belgium) and LMS Test.Xpress 5a software (LMS International, Leuven, Belgium). The tests used the functionality of the LMS Test.Xpress 5a software, which has the ability to control the exciter in a loop with feedback based on force measurement using an impedance head. A set of accelerometers attached to the manipulator joints (points B and C, Figure 6) was used to measure the vibrations of the robot. The acceleration signals were integrated twice to obtain the point displacements, and then the angular displacements of the links were calculated using the kinematics equations.  
The tests were carried out in one of the robot positions shown in Figure 6, for \(\beta_{2}=-120\) deg. The excitation described by Equation (32) was used, where the excitation frequency was variable in the range 26-46 Hz in order to excite resonant vibrations in the robot in the two expected resonance zones. The characteristics obtained are shown in Figure 9. In order to compare the characteristics obtained, the results of the simulation (solid line) and the experiment (square markers) are shown simultaneously.  
Figure 9 shows the phenomena predicted in the simulation tests, i.e., in the first resonance zone there are soft resonance curves, while in the second resonance zone there are stiff resonance curves. There are slight differences between the simulation and experimental results, e.g., for link 1 in the first resonance zone slightly higher amplitude values were obtained in the experimental tests (Figure 9a), while for link 2 in the second resonance zone the actual vibration amplitudes were slightly smaller than those predicted in the simulation (Figure 9b). As for the first resonance zone for link 2, the vibration amplitudes are so small that it is difficult to observe a clear nonlinear effect. Only in the context of the knowledge obtained from the simulation of a mathematical model is it easier to interpret the experimental results, and a slight slope of the characteristics can be observed.  
Figure 8: The test stand.
## 4 Conclusions  
Using the first-principle calculations within DFT, the interface structures, atom-resolved magnetism, atom-resolved DOS, and spin polarization of 10 atomic terminations in the Ti\({}_{2}\)CoAl/MgO(100) heterojunction were systematically examined. The results revealed that in equilibrium interface structures, the length of the alloy-Mg bond was much longer than that of the alloy-O bond because of the forceful repulsion interactions between the Heusler interface atoms and Mg atoms, especially those between metal atoms and Mg atoms. Owing to the competition among d-electron hybridization, d-electron localization originating from interface atomic bonding, and the moving effect of interface metal atoms, the interface atomic magnetic moments were complex and varied. In general, the interface atomic magnetism was slightly larger than the corresponding bulk-like layer atoms in alloy-Mg terminations. However, the opposite magnetic behavior was observed in alloy-O terminations. Analyzing the electronic properties near the Fermi level, we found that unexpected interface states appeared at the Fermi level for all terminations and destroyed the \"ideal\" half-metallicity observed in the bulk. A minimal destruction in the half-metallic gap and a maximal spin polarization of approximately 65% could only be observed in TiAl-Mg and AlAl-O terminations. From the Julliere formula, we could deduce that the TMR value was not larger than 150% in the Ti\({}_{2}\)CoAl/MgO(100) heterojunction at low temperature.  
Methodology, B.W.; software, G.Z. and X.W.; data curation, Y.F. and Y.C.; writing--original draft preparation, H.H.; writing--review and editing, B.W.  
This work was partly supported by the National Natural Science Foundation of China (11304410), Key Laboratory and Scientific Research Foundation of Zunyi City (SSKH [2015] 55), Natural Science Foundation of Technology Department (QKHJZ-LKZS [2014] 10, QJHJZ-LKZS [2012]03, and KHJZ[2014]2170), Youth Science Foundation of Education Ministry (QJHKZ [2012] 084, QJHKY[2018]310), and the Key Support Discipline ([2011]275) of Guizhou Province of China.  
The authors declare no conflict of interest.
# Landslide Recognition Based on DeepLabv3+ Framework Fusing ResNet101 and ECA Attention Mechanism
## 4 Experiments and Results
### The Location of the ECA  
We explored the placement of the ECA attention mechanism in our experiments by adding it after the residual block, ASPP module, and decoder feature fusion, respectively. The experimental results are shown in Table 7 below.  
The feature fusion step of the decoder usually involves integrating information from different levels of the encoder. Applying the ECA attention mechanism at this stage can adaptively emphasize the channel information that is critical to the task, thereby enhancing the effectiveness and robustness of the final feature representation and ensuring that the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Location** & **Precision** & **Recall** & **F1 Score** & **MIoU** \\ \hline ASPP-ECA & 0.9251 & 0.9166 & 0.9208 & 0.7534 \\ Residual-ECA & 0.9115 & 0.9222 & 0.9167 & 0.7433 \\ Decoder-ECA & 0.9328 & 0.9260 & 0.9241 & 0.7591 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Model evaluation metrics of the ECA modules when added at different locations.  
Figure 11: Predicted mask images from the different attention mechanisms. The region selected by the blue box is the false positive region, the region selected by the green box is the region with clearer detail capture, and the region selected by the red box is the region with preserved boundary.  
model can make full use of the multi-level information. According to the experimental results in Table 2 above, when the ECA module is added after the decoder feature fusion, the model has better performance and solves the problem of the loss of image detail information caused by multiple down-sampling in the decoder.
# A Light-Weight Autoregressive CNN-Based Frame Level Transducer Decoder for End-to-End ASR
## 2 Models
### Inference  
The CNN block shown in Figure 3 accepts the previous output token sequence as input and generates the token probabilities as output at the token level. Since the CNN block observes only the previous output token sequence, it works as a causal system. Because  
Figure 1: Encoder used in this work.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Train-Clean-100** & **dev-Clean** & **dev-Other** & **Test-Clean** & **Test-Other** & **Sum (\%)** \\ \hline
2 consecutive same tokens & 72,555 & 1410 & 1219 & 1311 & 1104 & 77,599 (1.1\%) \\
3 consecutive same tokens & 16 & 0 & 0 & 1 & 1 & 18 (0.0003\%) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The number of consecutive same token sequences at the token level of the LibriSpeech dataset (the total number of tokens = 7,117,087).  
Figure 2: Proposed frame level autoregressive decoder (inference).  
the CNN block has a 1D CNN layer with a kernel size of 6, it generates the current token probabilities based on the six previous output tokens. Since the average number of word pieces of a word is 2.2 (Librispeech [24] test other, 300 word pieces), six tokens correspond to 2.73 words. This is equivalent to a 3.73 g language model, which is close to the 4-gram language model that has been reported to optimal size [25].  
The RNN-T decoder yields the best accuracy among the decoders without using the beam search. The RNN-T decoder is different from this work in two aspects. One is that the RNN-T decoder adds the token probabilities in the frame + token level ([\(T_{enc}+L\)]) while this work adds in the frame level ([\(T_{enc}\)]) (Figure 4) This increases the decoding steps from \(T_{enc}\) to \(T_{enc}+L\) in the RNN-T decoder and hence increases the decoding time. While this work accepts the added token probabilities once at each frame ([\(T_{enc}\)]), to select the current token with the largest added probability, the RNN-T decoder accepts the added token probabilities whenever either the frame-level token probability([\(T_{enc}\)], encoder output) or the token-level token probability ([\(U\)]) changes.  
The other is that the RNN-T decoder uses an LSTM layer while a CNN layer is used in this work. Because the number of trainable parameters is around 1M in the RNN-T decoder and 177 k in this work and the decoding steps are larger in the RNN-T decoder, the decoding time of the RNN-T decoder is around 1.7 times of the RNN-T decoder.
## 3 LIG-Based Wearable Systems for Multifunctional and Intelligent HCI  
Human-computer interfaces, which serve as the foundation for the interaction between humans and machines, have been extensively utilized in various fields such as robot control, virtual reality (VR), augmented reality (AR), and autonomous driving. The growing interest in the metaverse in 2021 spotlighted the crucial role of HCI systems. Similar to the health care system above-mentioned, HCI systems are also developing toward a more wearable, multifunctional, and intelligent design due to several factors. (1) The miniaturization of the HCI system, which is necessary for long-term usage in daily life, can be achieved through advancements in device-integrated technology. Especially with the development of flexible sensors, actuators, and circuits, more and more HCI systems are becoming flexible and even stretchable. On one hand, this enhances the comfort of human objects in wearing the device, while on the other hand, it makes machine objects more adaptable, particularly on irregular surfaces that are challenging for conventional HCI devices [28; 29]. (2) HCI systems are becoming increasingly multifunctional and equipped with a variety of sensors, actuators, and other interactive modules. This trend enables HCI systems to perform a wider range of tasks and cater to a diverse array of application objects including humans, animals, plants, and robots as well as various application environments such as land and sea [30; 31; 32; 33]. (3) The integration of system functions and the improvement in system integration has led to increased information processing demands for HCI systems. In response to this trend, HCI systems have become more intelligent, utilizing intelligent algorithms to enhance the collection, transmission, and analysis of information. Additionally, there has been an emphasis on improving the information processing efficiency through the integration of sensing, storage, and computing capabilities in HCI systems [12; 13; 16; 34].  
In this section, we explore the applications of LIG in human-computer interfaces by using eight typical LIG-based wearable HCI systems as examples. The eight systems described in this section represent several distinctive efforts for diversified HCI applications including highly sensitive system, multi-signal collaborated system, dual-function acoustic system, intelligent and multifunctional system, intelligent artificial throat, bioinspired dual-channel system, non-contact HCI system, and closed-loop system. We will start with the specific application scenarios for which they are designed, introduce their manufacturing process and performance, and discuss their application prospects in detail.
# Machine Learning Reconstruction of Wyrtki Jet Seasonal Variability in the Equatorial Indian Ocean
## 2 Data and Methods  
### Data  
In this study, we utilized multi-source oceanographic observation data, including satellite-derived sea surface parameters, such as SLA, D20, SSS, SST, and geographical information (longitude and latitude). The SLA data were obtained from the Archiving, Validation and Interpretation of Satellite Oceanographic Data (AVISO) project, featuring a monthly gridded spatial resolution of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\)[57; 58]. The monthly mean sea surface wind field was acquired from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis dataset with 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) spatial resolution [59]. Ocean Surface Current Analysis Real-time (OSCAR) data, derived from the synthesis of satellite altimetry and QuikSCAT wind field observations, were provided at monthly temporal resolution and 1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\) spatial resolution [60]. Additionally, D20, SSS, SST, and MLD were extracted from the Ocean Reanalysis System 5 (ORAS5) dataset, which has monthly temporal resolution and \(1^{\circ}\times 1^{\circ}\) spatial resolution [61]. All datasets cover the period from January 1993 to December 2022. To ensure data consistency, all datasets were temporally averaged to monthly resolution and spatially interpolated to a unified \(1^{\circ}\times 1^{\circ}\) grid. Specific data characteristics are detailed in Table 1.