esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Basic Steps of CDP Synthesis Process
Figure 1: **(A)** depicts a schematic of the iCVD reactor, including a thermoelectric cooling (TEC) module for enhanced nanodrops formation and a digital microscope for real-time monitoring of the condensation-polymerization process. **(B)** illustrates the chemistry of the substrate surface, referred to as the “base layer,” and the polymerization initiator. **(C)** within the dashed box, the two steps of CDP are illustrated. In Step 1, vapor-phase monomers condense on the base layer placed on the TEC module, forming nanoscale droplets. **(D)** in Step 2, tert-butyl peroxide is delivered as vapor and undergoes thermal decomposition to tert-butoxyl (TBO) and methyl radicals, which occurs at a filament array maintained at 300 \({}^{\circ}\)C. These impinging free radicals initiate the polymerization and solidification of the condensed monomer droplets [29]. Figure 2: SEM images of poly(glycidyl) methacrylate nucleation (Pm/Psat = 1.45) in iCVD; each figure is 5 \(\upmu\)m \(\times\) 5 \(\upmu\)m: **(a)** 5 min, **(b)** 10 min, **(c)** 15 min, and **(d)** 20 min [29]. Inspired by [PERSON], [PERSON] [30] also adjusted the \(\rm P_{m}/P_{sat}\) ratio to be greater than one, resulting in PNPs instead of a polymer film. Monomer droplets were deposited on the surface of pPFDA. In the left image (at a 400 nm scale), taken during the first step of iCVD, the monomer vapor deposited and condensed to form PNDs that exhibited a noticeable hemispherical shape, as shown in Figure 3. This could be attributed to [PERSON] not directly depositing monomers on a silicon wafer substrate, but instead synthesizing a hydrophobic pPFDA film [31] first and then depositing the monomers, leading to polymer particles that were closer to a dome shape. For depositions conducted beneath monomer saturation (\(\rm P_{m}/P_{sat}<1\)), smooth films were observed from the cross-sectional view of the polymer film (\(\rm P_{m}/P_{sat}=0.72\)) in Figure 4. [PERSON] [32] also mentioned that if the monomer pressure is kept below the saturation pressure, polymerization results in dense polymer films. Figure 4: Cross-sectional SEM micrographs of vapor-deposited poly(glycidyl methacrylate) films: (**a**) \(\rm Pm/Psat=0.72\), (**b**) \(\rm Pm/Psat=0.99\), and (**c**) \(\rm Pm/Psat=1.45\)[30]. Figure 3: The SEM image of ndHEMA (2-hydroxyethyl methacrylate) nanodomes synthesized by the CDP technique shows the nanodome scale. A reactor chamber pressure of 300 mTorr; a filament array temperature of approximately 270 \({}^{\circ}\)C; and a stage temperature of 30 \({}^{\circ}\)C [29]. When monomer deposition occurs under gentle pressure conditions, the enhanced diffusion of monomers towards the substrate surface expands the spatial scope for aggregation or deposition. Decreasing monomer pressure reduces intermolecular interactions, facilitating improved control over film homogeneity and smoothness. Lower monomer partial pressure generally promotes a smoother, denser film morphology with diminished defects, owing to the prolonged surface diffusion time that allows the monomers to arrange into more orderly structures.
## 3 Advantages and Disadvantages of UHT and HTST  
It is emphasized that the UHT and HTST methods have smaller impacts on the sensory and physicochemical qualities of fruit products compared to traditional pasteurization. This is possible because of the use of high temperatures and shorter processing times. Moreover, direct heating of the product prior to packaging increases the efficiency of the process, because the resistance associated with the packaging material is omitted and the direction of the heat flow during this process changes. These techniques also ensure a high degree of preservation by ensuring the commercial sterility and microbiological safety of the product, allowing for a relatively long shelf-life while maintaining the desired sensory characteristics. However, high temperatures still affect the degradation of bioactive ingredients, such as vitamin C and phenolics, as well as aromatic compounds, and, therefore, reduces antioxidant activity, as well as changes in sensory and nutritional characteristics [21; 24; 25; 26; 27]. The color changes in HTST and UHT products result not from the activity of enzymes but because high temperatures inactivate them. This is the consequence of nonenzymatic browning reactions (Maillard reaction and caramelization), degradation, or oxidation of pigments due to exposure to heat [27]. It is emphasized that heating at high temperatures may lead to unfavorable changes in aroma, such as an unacceptable off-flavor or odor when cooked [28]. Thermal treatment may also lead to decreases in the quality of proteins and lipid oxidation, as well as depreciation of the biological viabilities of the natural color pigments, influencing consumers' preferences and loss of product marketability [29]. Compared to nonthermal preservation methods, such as high-pressure processing (HPP), high-pressure homogenization (HPH) or ultrasound (US), HTST and UHT may lead to much greater changes in color due to the thermal degradation of color substances or nonenzymatic browning reactions [30; 31]. Also, because of thermally induced reactions in the products, the profiles of the aroma compounds may change, as well as consumer acceptance and assessments of quality characteristics, such as taste, smell and color. Samples preserved under high pressure are better in quality in these aspects [25; 32]. Therefore, it is important to appropriately select the processing parameters (i.e., time and temperature) in order to maintain product quality as much as possible and, at the same time, ensure sufficient shelf-lives [24]. Over the years, researchers have tried to optimize the process conditions, including by shortening processing times and minimizing unfavorable losses, but this is not always fully possible [21].
# AVO-Friendly Velocity Analysis Based on the High-Resolution PCA-Weighted Semblance
## 3 Experiments
### Field Data  
We used a field seismic data set to verify the effectiveness of the proposed method. Firstly, we used a 2-D real-world seismic gather (Figure 7a) to apply the velocity analysis, and the corresponding semblance spectra are shown in Figure 7b-d. It can be seen that the proposed method has a high sensitivity in velocity scanning (Figure 7d) and the energy clusters in our new semblance were easy to be picked out, thus reducing the uncertainty of velocity analysis. The experiment with the 2-D real-world seismic gather proved that the proposed method is effective in the field data processing.  
Figure 5: Picked velocity values from different semblance, where the black curve represents the true values of velocities, the green curve represents the picked velocities from the conventional semblance, the cyan curve represents the AB semblance, and the red curve represents our new method.  
Figure 6: NMO-corrected gathers using different picked velocities from the above semblance. From left to right: (**a**) uncorrected CMP gather, (**b**) NMO-corrected CMP using the picked velocities from the conventional semblance spectrum, (**c**) NMO-corrected CMP gather from the AB semblance spectrum, and (**d**) NMO-corrected CMP gather from the PCA-weighted semblance spectrum.  
To further verify the performance of the new method, we use a historic 2D line from the Gulf of Mexico [9] to analyze the resolution of different semblances. As shown in Figure 8, the real-world data had 250 CMP points. We obtained different semblances under the same scanning parameters. Figure 9. shows the obtained semblances displayed with a 3-D form, it can be seen that the conventional semblance lost some energies in the shallow layer (Figure 9a), but the corresponding energies were recovered in the AB semblance (Figure 9b). Unfortunately, the peak broadening of the AB semblance spectrum led to picking uncertainty. Figure 9c shows the result of our new method, it can be seen that the resolution of the new method was significantly improved, and the intensity of energy cluster was stronger than that of the conventional method.  
Figure 8: A field data with 250 CMP gathers from the Gulf of Mexico.  
Figure 7: 2-D field data and the corresponding semblance spectra, where the solid curves in the semblance represent the picked velocities. From left to right: (**a**) uncorrected CMP gather on which all methods of semblance were applied, (**b**) conventional semblance, (**c**) AB semblance, and (**d**) PCA-weighted semblance.  
Figure 9: Semblances of the field date from the Gulf of Mexico: (**a**) conventional semblance, (**b**) AB semblance, and (**c**) PCA-weighted semblance.
# BWLM: A Balanced Weight Learning Mechanism for Long-Tailed Image Recognition
###### Abstract  
With the growth of data in the real world, datasets often encounter the problem of long-tailed distribution of class sample sizes. In long-tailed image recognition, existing solutions usually adopt a class rebalancing strategy, such as reweighting based on the effective sample size of each class, which leans towards common classes in terms of higher accuracy. However, increasing the accuracy of rare classes while maintaining the accuracy of common classes is the key to solving the problem of long-tailed image recognition. This research explores a direction that balances the accuracy of both common and rare classes simultaneously. Firstly, a two-stage training is adopted, motivated by the use of transfer learning to balance features of common and rare classes. Secondly, a balanced weight function called Balanced Focal Softmax (BFS) loss is proposed, which combines balanced softmax loss focusing on common classes with balanced focal loss focusing on rare classes to achieve dual balance in long-tailed image recognition. Subsequently, a Balanced Weight Learning Mechanism (BWLM) to further utilize the feature of weight decay is proposed, where the weight decay as the weight balancing technique for the BFS loss tends to make the model learn smaller balanced weights by punishing the larger weights. Through extensive experiments on five long-tailed image datasets, it proves that transferring the weights from the first stage to the second stage can alleviate the bias of the naive models toward common classes. The proposed BWLM not only balances the weights of common and rare classes, but also greatly improves the accuracy of long-tailed image recognition and outperforms many state-of-the-art algorithms.  
long-tailedimage recognition; balanced weight; weight decay; Balanced Weight Learning Mechanism; reweighting loss +
Footnote †: journal:  
_Applied Sciences_
## 2 Wireless B5G Difficulties with DL-Based Mm-Wave
### DL-Based mMIMO  
DL-based massive MIMO in the mm-wave is essential for managing high-dimensional data and dynamic channel circumstances during future B5G training. It facilitates effective learning of beamforming, channel estimation, and RA techniques, guaranteeing flexibility and optimization for the high-capacity, and low-latency [61]. One important mMIMO communication technology is beamforming, which focuses the sent signal's energy on the target recipient. Through its ability to comprehend the intricate links between channel circumstances, antenna array layout, and performance measures like throughput, latency, and EE, DL can be used to optimize the beamforming process [62]. RA in mMIMO systems, the allocation of resources like frequency, power, and time can be optimized with the application of DL algorithms. Increasing reliability, reducing power consumption, and optimizing network capacity across the board, can help to enhance system performance overall [10]. Reducing loss improved based on achieving perfect CSI, which has a major influence on the benefits that could be realized using mMIMO, and so it makes sense for the high-accuracy channel estimation challenge as shown in Figure 1. We investigate frameworks that completely utilize geographical data to integrate DL approaches for channel estimations and direction of arrivals in mMIMO systems. For super-performance mMIMO channel estimation, the DNN is a good option for performing accurate CSI reconstruction [35].  
Figure 1: Channel estimation in OFDM systems using a DNN [61].
## 3 Variable Selection and Methodology
### Methodology
#### 3.2.2 Panel Regression Model  
The use of the panel regression model, which uses two-dimensional data obtained in terms of time and location, can greatly increase the sample size. The estimation of the panel regression model can effectively alleviate multicollinearity and improve the accuracy of model parameter estimation, especially when there is unobservable heterogeneity that causes an endogeneity bias. A panel regression model can be used to eliminate heterogeneity and obtain a consistent estimation of regression coefficients. The panel regression method can also settle the condition in which the research sample is limited. The processis assessed directly via the EViews 8 software, and it may easily produce an estimation parameter. Therefore, the model is defined as the following equation:  
\[Y_{\text{it}}=\beta X_{it}+a_{i}+\varepsilon_{it} \tag{2}\]  
where \(Y\) and \(X\) denote the dependent variable and the independent variables, respectively: \(a\), \(\beta\), and \(\varepsilon\) denote the constant, regression coefficient, and random error, respectively, and \(i\) and \(t\) denote the city and time, respectively.
# A Robust \(H_{\infty}\) Application for Motor-Link Control Systems of Industrial Manipulators
###### Abstract  
\(H_{\infty}\) control approaches are widely investigated in various application fields and in the robotics area, too, for their robustness properties. However, they are still rarely adopted in the industrial context for the control of robot manipulators, mainly due to the lack of predefined procedures to build weighting functions able to automatically guarantee the fulfillment of the control objectives. This paper reports the first results of an academic-industrial research activity aimed at investigating the adoption of an \(H_{\infty}\) approach in the control software architecture of industrial manipulators, equipped with standard sensors on the motor side only. The design of the control system for a single-axis of an industrial manipulator is developed, showing that the construction of the weighting functions according to standard procedures can provide a satisfying behavior only on the motor side, leaving unacceptable oscillations of the link. A different procedure is then developed for the definition of the weighting functions with the specific aim of eliminating the possible vibrations of the mechanical structure. The proposed new form of such functions, including the main dynamic characteristics of the plant, ensures a robust, satisfying behavior on both the motor and the link side, as proven by simulation and experimental results.  
industrial manipulators; robust control; weighting functions design Article  
A Robust \(H_{\infty}\) Application for Motor-Link Control Systems of Industrial Manipulators  
1 Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Torino, Italy 1
# Towards a Digital Twin Warehouse through the Optimization of Internal Transport
## 2 Materials and Methods
### Logics Implemented  
Different logics can be implemented in the model, considering, for the analysis, the two most representative ones. The first of these, which is called \"row storage-S1\", logic S1 (see Figure 2a), tries to fill the different zones in such a way that the number of locations occupied in each of their rows is as small as possible. This logic achieves a uniform distribution of the material in rows. Under normal operating conditions, following this type of logic requires that the forklift operator should be provided with precise information about where to place a bag since the field of vision will normally be restricted in configurations with more than one longitudinal aisle by the bags that already have been stored.  
The second logic used, logic S2 (see Figure 2b), is based on filling complete zones so that each of the columns enabled in that zone is completed before continuing with the adjacent one. This storage system does not necessarily require one to indicate to the operator the location in which to place a bag coming from the raw material unloading zone since there is better visual control of the filled/unfilled zones.  
The operating conditions of the forklift are set for the analysis at the following values:  
* Lifting/lowering speed of blades: 0.1 m/s;
* Maximum speed on travel paths: 2.00 m/s;
* Maximum speed in loading/unloading travel: 1.00 m/s;
* Acceleration: 1.00 m/s\({}^{2}\).  
The operating conditions, the dimensions of the warehouse, the space occupied by the sack-pallet assembly, and the width of the aisles have not been the subject of analysis in this study, focusing on identifying which storage logic and which warehouse configuration may be the most appropriate depending on the storage needs.  
The model allows, as is obvious, the alteration of those conditions that remain constant while providing information on occupancy times and, fundamentally, on the number of relocations produced in the process of extracting batches of bags for their subsequent use in the production process.  
The unloading of raw material is integrated into the simulation based on the schedule agreed with the supplier, with delivery batches of 15-20 bags per truck.
# Dempster-Shafer Fusion Based on a Deep Boltzmann Machine for Blood Pressure Estimation
## 5 Discussion and Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Tests**} & \multicolumn{3}{c}{**SBP**} & \multicolumn{3}{c}{**DBP**} & \multicolumn{3}{c}{**Standard (SBP/DBP)**} \\  & \multicolumn{3}{c}{**Absolute Difference (\%)**} & \multicolumn{3}{c}{**Absolute Difference (\%)**} & \multicolumn{3}{c}{**BHS**} \\  & \(\leq\)**5 mmHg** & \(\leq\)**10 mmHg** & \(\leq\)**15 mmHg** & \(\leq\)**5 mmHg** & \(\leq\)**10 mmHg** & \(\leq\)**15 mmHg** & **Grade** \\ \hline MAA & 47.86 & 76.90 & 92.38 & 52.38 & 82.86 & 94.05 & C/B \\ SVM & 52.29 & 90.71 & 97.14 & 71.43 & 93.57 & 98.10 & B/A \\ SVMDS & 56.24 & 90.80 & 97.41 & 72.00 & 94.12 & 98.59 & A/A \\ DBN & 62.35 & 88.94 & 97.18 & 72.00 & 94.12 & 98.59 & A/A \\ DENDS & 64.98 & 91.06 & 97.88 & 74.12 & 94.82 & 99.06 & A/A \\ DBM & 64.52 & 90.82 & 97.18 & 72.24 & 94.38 & 98.59 & A/A \\ DBMDS & 66.12 & 92.47 & 98.82 & 77.20 & 97.19 & 99.81 & A/A \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Grading of the fusion scheme according to the BHS (Britishh hypertension society) [27] standard employing the results of MAA, SVM [28], SVMDS, DBN, DBNDS, DBM, and DBM based DS fusion schemes on (\(5\times 85=425\)) measurements. Figure 5: Summary of the SDE (ME) obtained utilizing the DBMDS estimator vs. DBM single estimator as the number of test increases according to the AAMI (American association of medical Instruments) standard protocol, where mean (5.34) and standard deviation (STD) (0.08) of SDE obtained from the DBMDS estimator vs. mean (5.96) and STD (0.47) of SDE obtained from the DBM estimator for the SBP, and mean (4.36) and STD (0.11) of SDE obtained from the DBMS estimator vs. mean (4.85) and STD (0.22) of SDE obtained from the DBM estimator for the DBP. Figure 6: Performance comparison between new fusion technique and stethoscope nurse measurements using the Bland–Altman method [5; 12; 19]. (**a**) Bland–Altman plot for the SBP; (**b**) Bland–Altman plot for the DBP. **Author Contributions:** Conceptualization, S.L. ; methodology, S.L. ; software, S.L. ; writing--original draft preparation, S.L. ; writing--review and editing, J.-H.C.; funding acquisition, J.-H.C.  
**Acknowledgments:** This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) (No. 2017R1A2A1A17069651). **Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Chromatographic Immunoassay (CIA) and Real-Time RT-PCR  
#### 2.2.1 Chromatographic Immunoassay (CIA)  
For antigen detection, rapid CIA tests were used for rotavirus, adenovirus, and norovirus, according to the manufacturers' protocol. The Acro Biotech Rotavirus and Adenovirus Combo Rapid Test Cassette (Feces) USA (Acro Biotech, Inc., Rancho Cucamonga, CA, USA), a lateral flow chromatographic immunoassay, was used to simultaneously detect rotavirus and adenovirus, providing qualitative results within 10 min. This is a rapid, lateral flow, chromatographic, one-step immunoassay for the qualitative detection of rotavirus and adenovirus in human feces specimens to aid in the diagnosis of rotavirus or adenovirus infection. In this test, the membrane is pre-coated with anti-rotavirus antibody and anti-adenovirus antibody on different regions of the test. During testing, a mixture of the specimen and reagent migrates upward on the membrane chromatographically by capillary action to react with the anti-rotavirus and anti-adenovirus antibodies on the membrane and generate a colored line. An internal procedural control is included in the test. According to the manufacturer, the test has been previously compared with the unknown latex agglutination method, demonstrating an overall accuracy of \(\geq\)97.0%.  
The JusChek Norovirus Rapid Test Cassette (Hangzhou AllTest Biotech Co., Ltd., Hangzhou, China), was used to detect norovirus genogroups I and II. This is a rapid, lateral flow, chromatographic immunoassay for the qualitative detection of norovirus in human feces specimen to aid in the diagnosis of norovirus infection. The test utilizes monoclonal antibodies specific for norovirus genogroup I and II, coated on the test membrane, to selectively detect norovirus from human feces specimens. During testing, the stool specimen reacts with the conjugate antibodies; the mixture migrates upward on the membrane chromatographically by capillary action to react with norovirus antibodies on the membrane. An internal procedural control is included in the test. A limitation is that stool samples from infants under one year old can produce a false positive result. The performance of this test has been compared with the RT-PCR method with 70 clinical specimens, demonstrating that the relative accuracy is 94.3%.