esa-sceva/satcom-chunk-collection huggingface dataset:
# Progress of the Computer-Generated Holography Based on Deep Learning
## 4 The Learning-Based CGH
### Autoencoder-Based CGH  
In addition to all the works above, [PERSON] et al. proposed an autoencoder-based neural network for phase-only hologram generation, called Holo-Encoder [10]. It incorporated the Fresnel diffraction model into the network framework to realize the unsupervised training. The network was trained for 10 epochs using an Adam optimizer and the training images were obtained from the DIV2K dataset. The problem of gradient disappearance in deep learning training could be solved by adding the residual to U-Net. Using a combination of negative Pearson correlation coefficient (NPCC), perception loss function, and TV regularization, the training results were constrained from three aspects: pixel-wise, feature-wise, and smoothness of phase. The Holo-Encoder was compared with the GS holography and the Wirtinger holography. All three algorithms were run on the same workstation with a Xeon CPU E5-2650 (2.20 GHz) and 128 GB of RAM. An NVIDIA Quadro GV100 GPU was used for the prediction of the Holo-Encoder. As shown in Figure 8, a smoother phase hologram and a better reconstructed image were obtained, while the computation time was only 0.15 s, outperforming other methods with regard to the runtime.
## 5 Results  
### RAC Performance Tests  
The research team simulated roll gap misalignment control as provided by the proportional RAC controller (the design version), see Figure 10a for the results. These transients were obtained by testing the disturbance stemming from the variation in gauge between different sides of the feed.  
The disturbance in Figure 10a is the signal \(h_{\text{ODS}}\) that sets the feed gauge at the entrance to the stand on the driving side. It is a response to a signal jump in the input of the external AGCS circuit (not shown in the Figure 10a). At time \(t_{1}=5\) s, \(h_{\text{ODS}}\) signals a 2 mm increase in the gauge, which starts a transient. As a result, DS-OS gauge begins to differ, i.e., a wedge emerges as shown in Panel 4; the gauge \(h_{\text{DS}}\) at the stand exit increases, see Panel 1. The disturbance causes an increase in the force \(F_{\text{DS}}\) (Panel 2), which triggers a nearly simultaneous increase in the signal of the RAC controller, Panel 3.  
Signal \(\Delta S\) of the gauge difference between the sides, Panel 4, is within a 5% window of deviation from the steady-state value by the time \(t_{2}\approx 5.15\) s. Thus, the transient associated with this disturbance lasts 0.15 s, which is not acceptable. The key requirement to the RAC controller is to eliminate the misalignment \(\Delta S\) in the shortest possible time. Thus, in order to improve its performance, the research team decided to reconfigure RAC by their developed method.  
Figure 10: Simulation of misalignment compensation transients associated with a proportional controller (**a**) and a PD controller (**b**): Panel 1 shows gauge curves; Panel 2 shows DS-OS force values; Panel 3 shows the RAC output; window 4 Panel the difference in gauge between different sides (\(\Delta S\)).  
Figure 10b shows the transients of coordinates similar to Figure 10a as produced by simulating the proportional RAC controller above with a positive feedback. Data suggests more intensive processes <1 s before the 5% zone is reached. Thus, the performance of processing a disturbance is >1.5 times better compared to Figure 10a.  
Notably, the curve of gauge difference in Panel 4 enters the negative range. This indicates a short'reverse' roll gap misalignment, which has a positive effect in terms of correcting the feed bending. Should the feed shift off the rolling axis horizontally, additional force will be applied to bring it back to the rolling plane.  
The absolute value of the bending can be indirectly found as the area bounded by the curve \(\Delta S\). It is computable as an integral of the dependencies shown in Panels 4, Figure 10a,b. Calculations show the 'integral' to be less in Figure 10b than in Figure 10a by a factor of 2.5. This indirectly proves the curbing of camber, which is the goal of this R&D project.
## 3 Results  
### Identification of MSPL/TMPRSS13 as DC-SIGN ligands  
We previously identified Mac-2 BP as a DC-SIGN ligand expressed in COLO205 colon cancer cells based on recombinant DC-SIGN-Fc, a fused protein of the DC-SIGN extracellular domain and human IgG-Fc [18]. In the results of DC-SIGN-Fc affinity chromatography of the COLO205 membrane protein fraction, we found a sharp band at 100 kDa located above the Mac-2 BP band (90 kDa) (Figure 1a). To identify this new DC-SIGN ligand protein, we analyzed the 100-kDa and 90-kDa bands using LC-MS/MS. The results showed that the band at 100 kDa and the broad band at 90 kDa contained the peptide sequence NKPGVYTK, corresponding to TMPRSS13 isoform 1 (MSPL) while Mac-2 BP was detected at 90 kDa (Figure 1b) (see Tables S1 and S2). MSPL and TMPRSS13 are splicing variants of a single type II membrane serine protease, which was cloned from a human lung cDNA library [19]. When FLAG-tagged full-length MSPL and TMPRSS13 were expressed in HEK293 cells, each showed two bands (Figure 1d), presumably due to different post-translational modifications. Next, to validate the molecular interaction between DC-SIGN and MSPL/TMPRSS13, we conducted a DC-SIGN-Fc pull-down assay using cell lysates of MSPL and TMPRSS13 transfectants (Figure 1c-e). Western blot results indicated that both MSPL and TMPRSS13 were co-precipitated with DC-SIGN-Fc in the presence of Ca\({}^{2+}\) (Figure 1e). On the other hand, neither MSPL nor TMPRSS13 were co-precipitated by Fc only control, indicating the interaction was mediated by DC-SIGN and not by Fc domain. In addition, immunofluorescence staining for MSPL/TMPRSS13 showed intense cell-surface staining in HEK293 MSPL transfected cells (Figure 1f) and COLO205 cells (Figure 1g). Moreover, in COLO205 cells, MSPL/TMPRSS13 staining was overlapped significantly with DC-SIGN-Fc staining (Figure 1g). The chelation of Ca\({}^{2+}\) with 10 mM EDTA or the addition of mannose (50 mM) clearly abolishedDC-SIGN binding, suggesting that the interaction was mediated by the CRD of DC-SIGN. These results revealed that DC-SIGN binds to MSPL/TM PRSS13 expressed in COLO205 cells.
###### Abstract  
Platform chemicals, also known as chemical building blocks, are substances that serve as starting materials for the synthesis of various value-added products, which find a wide range of applications. These chemicals are the key ingredients for many fine and specialty chemicals. Most of the transformations of platform chemicals are catalytic processes, which should meet the requirements of sustainable chemistry: to be not toxic for humans, to be safe for the environment, and to allow multiple reuses of catalytic materials. This paper presents an overview of a new class of heterogeneous catalysts based on nanoparticles of catalytically active metals stabilized by a polymer matrix of hypercrosslinked polystyrene (HPS). This polymeric support is characterized by hierarchical porosity (including meso- and macropores along with micropores), which is important both for the formation of metal nanoparticles and for efficient mass transfer of reactants. The influence of key parameters such as the morphology of nanoparticles (bimetallic versus monometallic) and the presence of functional groups in the polymer matrix on the catalytic properties is considered. Emphasis is placed on the use of this class of heterogeneous catalysts for the conversion of plant polysaccharides into polyols (sorbitol, mannitol, and glycols), hydrogenation of levulinic acid, furfural, oxidation of disaccharides, and some other reactions that might be useful for large-scale industrial processes that aim to be sustainable. Some challenges related to the use of HPS-based catalysts are addressed and multiple perspectives are discussed.  
porous organic polymer; hypercrosslinked polystyrene; platform chemicals; heterogeneous catalysts; metal nanoparticles; biomass processing +
Footnote †: journal: molecules  
Review  
An Overview of Heterogeneous Catalysts Based on Hypercrosslinked Polystyrene for the Synthesis and Transformation of Platform Chemicals Derived from Biomass
## 2 Results
### Subcellular Localization of ZcTPS02  
GPP is a crucial precursor in the biosynthesis of monoterepenes, which are generally believed to be synthesized via the plastidMEP pathway [27]. In this study, Cell-PLoc 2.0 and WoLF PSORT II were employed to predict the subcellular localization of ZcTPS02. ZcTPS02 was predicted to be located mainly in chloroplasts. To further investigate the subcellular localization of ZcTPS02, ZcTPS02 was fused to green fluorescent protein (GFP) protein and expressed in _Nicotina. benthamiana_ leaves. The computational prediction tools and experimental analyses consistently indicated that ZcTPS02 is primarily located in the plastid, implying that ZcTPS02 is responsible for the production of monoterepenes in _Z. candidia_ (Figure 5).  
Figure 3: Sequence alignment analysis. Alignment of _Z. candidia_ TPS amino acid sequences with functionally characterized TPSs from other species. The numbers in the figure represent the amino acid sequences. 1, (+)-limonene synthase 1 (_Citrus limon_) (accession no. AAM53944); 2, (+)-limonene synthase 2 (_Citrus limon_) (accession no. AAM53946); 3, E-\(\beta\)-ocimene synthase, partial (_Arabidopsis thaliana_) (accession no. AAN65379); 4, \(\beta\)-ocimene synthase (_Phaseolus luntaus_) (accession no. ABY65110); 5, ZcTPS01; 6, ZcTPS02. The sequences were aligned by Genomenet and analyzed with ESPript 3.0. The red background shading represents 100% identity and light red represents 70% identity. The blue frame shows the similarity across groups. The terpen synthetases motifs RRx8W, RxR, DDxXD, and NSE/DTE are indicated by the black box.  
Figure 4: Biochemical characterization of enzymes encoded by ZcTPS. In vitro enzymatic assays of ZcTPSs were performed and the resulting enzyme products were collected by SPME and subsequently analyzed through GC-MS. (**A**) Chromatogram of terpen generated by the ZcTPS01 protein purified from BL21(DE3) pLysS cells in a reaction with GPP. (**B**) Chromatogram of terpen generated by the ZcTPS02 protein from BL21(DE3) pLysS cells in a reaction with GPP. (**C**) Chemical structure and mass spectra of \(\beta\)-cocimene. 7, \(\beta\)-ocimene; 9, allo-ocimene; 18, (E)-\(\beta\)-ocimene. *, impurities.
# Golgi Metal Ion Homeostasis in Human Health and Diseases
## 3 Zn\({}^{2+}\) Transporters
### Zn\({}^{2+}\) Homeostasis and Human Diseases  
The release of Zn\({}^{2+}\) from intracellular stores into the cytosol initiates early or late signaling pathways. The former is transcription-independent, in which Zn\({}^{2+}\) binds to target receptors; and the latter requires the transcription of Zn\({}^{2+}\) transport proteins that restore homeostasis [127]. Disruption of Zn\({}^{2+}\) homeostasis, either by Zn\({}^{2+}\) deficiency or overload, induces ER stress and UPR, leading to the activation of transcription factors, such as ATF4, ATF6 and XBP1, and upregulation of ZnT and ZIP expression [128; 129]. Thus, Zn\({}^{2+}\) homeostasis is maintained by transcriptional regulation of Zn\({}^{2+}\) transporters via the ER function.  
Disturbance of zinc homeostasis can result in diseases, including cancer [130], diabetes [131; 132], neuronal degeneration [133], degenerative disorders of aging [134], and infection and inflammation [135; 136]. A number of diseases have been identified to be caused by mutations in Golgi Zn\({}^{2+}\) transporter genes [18; 19] (summarized in Table 1). Some Zn\({}^{2+}\) transporters are abnormally expressed in Alzheimer's disease (AD) [137; 138], sporadic amyotrophic lateral sclerosis [139], and multiple types of cancer [140; 141; 142]. These observations reveal a hidden link between abnormal Zn\({}^{2+}\) accumulation in the Golgi lumen or cytoplasm and pathogenesis, indicating Zn\({}^{2+}\) transporters as potential therapeutic targets for disease treatment [143; 144; 145; 146].
# Towards Improved Inertial Navigation by Reducing Errors Using Deep Learning Methodology
## 3 Results
### Network Application  
To evaluate the performance of the proposed methodology, three CNNs (input sizes of \(10\times 10\times 1\), \(6\times 6\times 1\), and \(3\times 3\times 1\)) are applied to filter the data collected from two experimental setups: (1) a remote-control (RC) car (see Figure 6a) on a relatively straight path, but the motion path is uncontrolled; and (2) a toy train moving on an oval path, but using a controlled motion path (see Figure 6b). The IMU unit is mounted on the car body and the data-collection laptop is placed on a metal stand placed on top of the car  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Input Size** & **Test Accuracy** & **Validation Accuracy** & **Training Epoch** \\ \hline \(10\times 10\times 1\) & 98.90\% & 99.84\% & 240,000 \\ \(6\times 6\times 1\) & 96.13\% & 98.58\% & 650,000 \\ \(3\times 3\times 1\) & 95.72\% & 97.14\% & 755,000 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Test accuracy results for EPSON IMU gyroscope.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Input Step Size** & **Test Accuracy** & **Validation Accuracy** & **Training Epoch** \\ \hline \(10\times 10\times 1\) & 92.67\% & 94.23\% & 320,000 \\ \(6\times 6\times 1\) & 88.88\% & 89.35\% & 870,000 \\ \(3\times 3\times 1\) & 80.95\% & 84.10\% & 880,000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test accuracy results for EPSON IMU accelerometer.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Input Size** & **Test Accuracy** & **Validation Accuracy** & **Training Epoch** \\ \hline \(10\times 10\times 1\) & 91.61\% & 93.46\% & 444,000 \\ \(6\times 6\times 1\) & 89.19\% & 89.26\% & 840,000 \\ \(3\times 3\times 1\) & 83.77\% & 85.93\% & 1,190,000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test accuracy results for TDK IMU accelerometer.  
body. Cardboard barriers are placed to allow the car to run in a relatively straight path. The data-collection experiment is repeated at least ten times. The raw-data outputs from accelerometers in the IMU are collected and sent to the three networks mentioned above. Finally, the distances are computed by integrating the filtered acceleration results from each network. For the train, the IMU is mounted on top of one of the carriages driven by the battery-powered locomotive.
## 3 Proposed Theoretical Framework for the Identification of Deficient Source Code
When the majority function is used for calculating the majority within a certain quality aspect, \(y_{j}\) stands for that quality aspect, and \(x_{i}\) stands for a single software metric within the respected aspect. If the software metric value exceeds the threshold value, the \(x_{i}\) is 1, otherwise \(x_{i}\) is 0. Based on the calculated majority measure, the quality rate of a single quality aspect is determined by Equation (2), and can be evaluated as either 1 or 0:  
\[QualityRate_{y_{i}}=\left\{ \begin{array}{l}1;Majrity_{y_{i}}>0.5\\ 0;Majrity_{y_{i}}\leqslant 0.5\end{array} \right., \tag{2}\]  
where \(y_{i}\) stands for the quality aspect. In this manner, the quality rate 1 represents a poor and 0 represents a good quality aspect. If the calculated value of the majority function is more than 0.5 the final quality rate is evaluated as 1 (poor) and if the value is less than or equal to 0.5, the quality rate is evaluated as 0 (good). Similarly, for determining an overall quality of a program entity, which includes all different quality aspects, the same majority function (Equation (1)) and quality rate (Equation (2)) equationsare used. In this manner, when the majority function (Equation (1)) is used for calculating the overall quality of program entities, \(y_{j}\) stands for the specific program entity and \(x_{i}\) stands for the quality rate of an evaluated quality aspect, as determined based on Equation (2). In the end, the overall quality of the program entity is determined as poor if the quality rate of the program entity is 1 and as good if the quality rate of the program entity is 0. The detailed steps of the proposed approach are summarized by Algorithm 1. As proposed with the approach, the identification of potentially deficient program entities is done based on the calculated majority function and determined quality rate. First, for each program entity the majority function is applied within every quality aspect, considering single software metric values, and afterwards, the majority function is calculated for the program entity considering quality ratings of all quality aspects. Each evaluated program entity, the overall quality rate of which is determined as poor, is identified as potentially deficient. The list of such potentially deficient program entities constitutes a final output of the proposed identification approach. ```
1:_derive_ thresholds for selected software metrics \(M_{1\_j}\)
2:for each program entity \(E_{1\_k}\)do
3:gather software metrics
4:execute evaluation
5:for each quality aspect \(Q_{1\_i}\)do
6:calculate majority function based on software metrics \(M_{1\_j}\)
7:define quality rate
8:endfor
9:calculate majority function based on quality aspect \(Q_{1\_i}\)
10:define quality rate
11:if quality rate equals poor then
12:add program entity \(E_{1\_k}\) to list of potentially deficient program entities
13:endif
14:endfor
```  
**Algorithm 1** Detailed structure of the proposed approach for the identification of deficient program entities
# Hybrid Locomotion Evaluation for a Novel Amphibious Spherical Robot
## 4 The On-Land Locomotion Experiments
### The Lifting and Supporting Wheel Mechanism (LSWM)  
To reduce the load on the actuator and extend its working life, we developed an LSWM driven by two 360-degree servos. Before the robot moved, the mobile platform descended, powered by the two servos rotating synchronously in the same direction. The PWM control signals were sent to the servos by the motor control board. We used waterproof servos (model HDKJ D3009) with a working voltage of 4.8-7.2 V. To maximize force and torque, we selected 7.2 V as the working voltage. The stall torque was 10 kg\(\cdot\)cm and the operating speed was 0.14 s/60\({}^{\circ}\). The frequency of the control signal was 50 Hz, and the duty factor \(s\) of the PWM signal ranged from 6.9 to 8.1%. We set the step size of the PWM signal to 0.05%. Thus, we obtained 20 data groups (from the lower to the upper and from the upper to the lower states, as shown in Figure 9); the distance \(\Delta h\) between the upper and lower state was 40 mm. All data groups were collected 10 times and averaged. The rising and falling velocities were as Equation (11).  
\[v=\frac{\Delta h}{t} \tag{11}\]  
The nonlinear relationship between the rising and falling velocity \(v\) and the duty factor \(s\) is shown in Figure 10. We considered the falling velocity positive and the rising velocity negative. When the duty factor ranged between 7.41 and 7.51%, the servo torque was very low and the LSWM velocity was almost zero. When the duty factor was more than 7.9% or less than 7.05%, the servo angular velocity, rendering both the rising and falling velocity relatively stable. Finally, based on the variation in velocity as the duty factor ranged from 7 to 8%, the nonlinear relationship can be described by the sum of sines (Equation (12)).  
Figure 9: **(a)** The upper state of lifting and supporting wheel mechanism (LSWM); **(b)** the lower state of LSWM.  
\[v=\left\{\begin{array}{ccc}-2.1&s<7.1\\ 0&7.41<s<7.51\\ 2.1&s>7.9\\ 1.968\sin(3.448s+18.22)&+\ 0.1923\sin(9.059s+23.33)&else\end{array}\right. \tag{12}\]  
When the robot moves from flat, smooth terrain to rough terrain and vice versa, autonomous stable transition from sliding to walking (or vice versa) is achieved by varying the duty factor, dramatically improving performance on different substrates.
## 1 Introduction
After mapping the vibration signal into the angular domain, we propose filtering with an angle-varying transfer function to magnify failures' impulsive behavior when dealing with variable-speed records. Namely, we measured short-term spectral components by operating two sliding windows: a small one that measures the instantaneous amplitude level and tracks impulsive components, and a large interval that evaluates average background amplitude. Aiming to characterize cyclo-non-stationary processes with impulsive behavior, the emphasizing high-order-based estimator based on the principle of spectral entropy is introduced. For evaluation purposes, both visual inspection and classifier performance were assessed, contrasting the spectral-entropy estimator with the widely used spectral-kurtosis approach for dealing with impulsive signals. The validation of short-time/-angle spectral analysis, performed on three datasets at variable speed, showed that the proposed spectral-entropy estimator is a promising indicator for emphasizing bearing failures with impulse behavior. The rest of the paper is organized as follows: Section 2 briefly discusses squared-envelope-spectrum estimation and emphasizing impulsive components; Section 3 describes the experiment setup and the simulation framework of rolling-element bearing faults to test the methodology at instantaneous angular speed; Section 4 discusses the validation results performed by three vibration signal collections. Lastly, Section 5 outlines the main conclusions of the paper.