esa-sceva/satcom-chunk-collection huggingface dataset:
# Domain Adaptation Network with Double Adversarial Mechanism for Intelligent Fault Diagnosis
## 1 Introduction
In addition, in the feature extraction stage, maximum mean discrepancy (MMD) [38] is used to reduce the distance between the extracted features of two domains, which avoids the degenerate learning caused by adversarial, so as to improve the accuracy of model diagnosis to a certain extent. Figure 2: Classification comparison of various domain adaptation methods. The main contributions of this paper are as follows:  
1. In this paper, a new fault diagnosis method is proposed, which adopts a double adversarial mechanism to realize domain-level alignment and class-level alignment at the same time. 2. The proposed method is a novel domain adaptation method to solve the problem that distribution of training data and test data is not the same in a real state. Therefore, the unlabeled target domain samples are the same as the labeled source domain samples, which can be correctly distinguished. 3. The proposed method was verified by multi-group transfer experiments and compared with other mainstream intelligent fault diagnosis methods. It can be seen from the experimental results that the DAN-DAM model has a better diagnostic effect for the domain shift samples and the diagnostic accuracy is generally higher than other mainstream diagnostic methods, which more strongly proves the superiority of the DAN-DAM model. The rest of the paper is organized as follows: Firstly, Section 2 introduces the theoretical background of the basic knowledge involved in the proposed method. Secondly, Section 3 mainly introduces the framework of the proposed method in detail. In Section 4, the proposed model is verified experimentally to prove the diagnostic effect of the proposed method. Finally, the Section 5 gives a brief summary of the proposed method.
## 3 System Model
### Optimal Relay Selection  
#### 3.2.1 Mobile Reliable Model  
Due to the mobility of trains, the train \(T_{n}\) may move out of the communication range of the wayside relay \(R_{m}\), resulting in an outage event; hence, the distance between the train and wayside nodes should be limited. The candidate wayside node locations are denoted as \(\left(x_{R_{1}},y_{R_{1}}\right)\), \(\left(x_{R_{2}},y_{R_{2}}\right)\), \(\left(x_{R_{m}},y_{R_{m}}\right)\dots\left(x_{R_{M}},y_{R_{M}}\right)\). In the transmission delay \(T_{c}\) between train and wayside node, the channel SNR must satisfy the SNR threshold condition, whether the train is in the initial position \(\left(x_{R_{1}},y_{R_{1}}\right)\) or at the end of transmission position \(\left(x^{\prime}{}_{T_{1}},y^{\prime}{}_{T_{1}}\right)\). Meanwhile, the speed of packet transmission in the channel is much faster than the speed of the train; thus, we assume that the train drives at initial speed \(v(t)\) during the transmission. In the transmission time delay \(T_{c}\), the distance of the train moving in the \(x\)- and \(y\)-directions can be calculated as follows [15]:  
\[S_{x}=v(t)\times\ T_{c}\times e_{x}(t), \tag{9}\]  
\[S_{y}=v(t)\times T_{c}\times e_{y}(t), \tag{10}\]  
where \(e_{x}(t)\) and \(e_{y}(t)\) are the \(x\)- and \(y\)-directions of the train. Therefore, the location where the train ends its transmission is \(\left(x^{\prime}{}_{T_{1}},y^{\prime}{}_{T_{1}}\right)=\left(x_{T_{1}}+\ S_{x},y_{T_{1}}+\ S_{y}\right)\). The conditions that candidate wayside nodes need to satisfy are  
\[d_{T1,Rm}=\sqrt{\left(x_{T1}-x_{Rm}\right)^{2}+\left(y_{T1}-y_{Rm}\right)^{2}} <R_{\max}\, \tag{11}\]  
\[d^{\prime}{}_{T1,Rm}=\sqrt{\left(x^{\prime}{}_{T1}-x_{Rm}\right)^{2}+\left(y ^{\prime}{}_{T1}-y_{Rm}\right)^{2}}<R_{\max}. \tag{12}\]  
A node can only be a candidate transmission node for a train if its location is within the transmission range of the train at the beginning and end of the transmission.
## 3 Results  
### Concept Direction to Achieve 90-Degree Tilt  
#### 3.1.1 Enhance the Existing Lift  
Initially, the objective was to enhance the functionality of the existing lift by enabling it to tilt up to a 90-degree angle. While this concept was considered cost-effective, attempts to achieve the full 90-degree tilt without extensive redesigning proved unsuccessful.
###### Abstract
Unlike EPSM, this method allows for imaging of a multi-layered medium with lateral velocity variations, such as a medium that is not perpendicular to the surface of the arrays. The main advantage of the algorithm is a much higher efficiency than time-domain algorithms. Nevertheless, this algorithm cannot achieve a spatial resolution as high as the standard TFM, and the signal-to-noise ratio (SNR) is lower than for the TFM. Thus, a high-efficiency and high-accuracy algorithm for ultrasonic full-matrix imaging in oblique incidence detection on multi-layered structures is intensely desired. In this paper, a modified wavenumber method for full-matrix imaging of multi-layered structures with oblique array incidence is proposed. The proposed method performs a coordinate rotation in the frequency domain to extrapolate the original wave field, which is parallel to the linear array, to the virtual measurement line that is parallel to the object's surface. Then both transmission and reception wave-field extrapolation are performed in the rotated coordinate system, and an imaging condition is applied to obtain a total focused image of the multi-layered structure. The proposed method introduces an accurate coordinate transformation relation, so that it can deal with any incident angle without precision loss. It can be used in many applications, such as immersion detection of large objects with slightly curved surfaces and oblique incidence inspection of welds with an angled wedge. The simulations and experimental results show that the proposed method has high accuracy and efficiency. The remainder of this paper is organized as follows. The method and the derivation of the proposed method are provided in Section 2. The results and discussion illustrating the method performance are presented in Section 3. Finally, the conclusions are given in Section 4.
## 3 Engineering Example
### Project Parameters
#### 3.1.1 Soil Parameters  
According to the geotechnical engineering survey report for the Changjiang Nuclear Power Plant site in Hainan, the site's strata primarily consist of intrusive rocks and the Quaternary system. The Quaternary system, predominantly distributed across the coastal plains and plateau geographic units of the plant area, mainly comprises marine and residual deposits, with a maximum thickness of 41.20 m as revealed by this survey. The site's intrusive rocks predominantly consist of biotite granite. Existing geological data indicate the presence of small areas of lamprophyre, granite porphyry, and other vein rocks. However, due to their limited distribution and minimal impact on the foundation, these formations are not considered in this study. For research convenience, rock bodies sharing similar mechanical properties and indistinct boundaries are amalgamated into a single layer.  
By considering the submerged tunnel and stiffer rock foundation as elastic materials, the physical parameters are presented in Table 1. The remaining soil materials must account for their nonlinear characteristics, and the aforementioned equivalent linear method is utilized for dynamic analysis calculations. The necessary equivalent linear parameters are illustrated in Figure 7.  
Figure 5: Housner system for hydrodynamic pressure.  
Figure 6: Cross-section view of immersed tunnel.
# On Modeling Antennas Using MoM-Based Algorithms: Wire-Grid versus Surface Triangulation
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{\begin{tabular}{c} **Implementation** \\ **Complexity** \\ \end{tabular} } & \multirow{2}{*}{**Speed**} & \multirow{2}{*}{**Memory**} & \multirow{2}{*}{**Computing**} & \multirow{2}{*}{**Speed**} & \multirow{2}{*}{**Memory**} & \multirow{2}{*}{**Computing**} & & \multicolumn{5}{c}{**Antennas Types**} & & & \\ \cline{4-14}  & & & & & & & & & & & & & & & & & & \\ \hline \multirow{3}{*}{MoM} & Wire-Grid & Easy & Fast & Very low & \begin{tabular}{c} Current and \\ charge density \\ \end{tabular} & * 1 & \(-\)1 & 0 & 0 & 1 & 0 & 0 & \(-\)1 & \(-\)1 \\ \cline{2-14}  & Triangle-Grid & Medium & Medium & Medium & Medium & \
\begin{tabular}{c} Current and \\ charge density \\ \end{tabular} & 0 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 0 & 1 \\ \hline \multirow{3}{*}{FDTD} & FDTD & Medium & Fast & Low &
\begin{tabular}{c} Electrical and \\ magnetic fields \\ \end{tabular} & 0 & 1 & 0 & 0 & \(-\)1 & 1 & 1 & 0 & 0 & 0 \\ \hline \hline \end{tabular}  
* (1) denotes that the method is very appropriate, (0) is suitable, (\(-\)1) is not suitable for modeling such antenna types modeling.  
\end{table} 
Table 17: Qualitative comparison of FDTD and MoM-based approaches used in this work. Moreover, we believe that embedding other acceleration algorithms in our codes can raise their efficiency and expand their features. An example can be enhancing the Wire-Grid and Triangle-Grid graphical user interfaces and their ability to model dielectric bodies. This in it is turn, can enable us to investigate the characteristics of an arbitrary antenna structures using our own software system, saving our resources, and concentrating more on the scientific aspects of the numerical methods used.
## 2 Advancements in Ultrafiltration for Protein and Peptide Separation
### Integration of UF with Other Processes
Cascaded UFs with different stream configuration are sought to bring better performance. [PERSON] et al. [172] applied three configurations of cascaded UF as displayed in Figure 5a-c to separate \(\alpha\)-lactalbumin from \(\beta\)-lactoglobulin in a whey protein isolate. Under constant protein concentration in feed 2 g L\({}^{-1}\), pH 7.2, NaCl 5 mM, and cross-flow velocity 0.1 m s\({}^{-1}\), higher ratio of P1/W (product/waste) was integral to excellent separation performance. In one side, configuration C cannot manage the desired trade-off between yield and purity. Meanwhile, configuration A and B executed a more promising recovery and purity [172]. Following cascaded UF, other ways of UF-DF integration has been employed for several processes in protein separation. One of them was even elaborated more with EDBM [113], which is discussed in Section 2.3. Figure 5: Cascaded UF with (**a**) non-constrained ideal; (**b**) adapted; and (**c**) countercurrent product recycle configuration. F: feed; P: permeate; R: retentate; W: waste. Integration of jet-cooking with enzyme-assisted UF was revealed to increase the protein content of soy protein isolate (SPI) and nitrogen solubility index (NSI) and reduce isoflav and phytic acid for infant formula [173]. UF membrane of 80 kDa was selected attributed to (1) increase of permeate flux from 4 to 12 L m\({}^{-2}\) h\({}^{-1}\), (2) slightly lower protein retention rate increase (<5% decline), (3) higher protein concentration in permeate (changed from 35 to 70 \(\upmu\)g mL\({}^{-1}\)), and (4) decline of phytic acid from 2.1 to 1.2 \(\upmu\)g mL\({}^{-1}\) (all value compared between 30 kDa and 80 kDa, respectively). Jet-cooking temperature positively enhanced protein retention rate by aggregation of soluble proteins (higher MW), which was crucial for the efficiency of UF. Owing to energy optimization, 130 \({}^{\circ}\)C was considered to be the best.
## 4 Results
### SWE's Control on the ROD  
In the previous section, we find that an increase in the SWE leads to a delay in the ROD. To further explore the control of the SWE on the ROD, we firstly fit the relationship between the SWE and ROD (Figure 9). The results for all four land cover types show a significant positive correlation (\(p<0.05\)) between the two. In other words, as the SWE increases, the ROD consequently exhibits a delay. The extent of vegetation cover also affects the magnitude of the correlation between them. In tree cover areas, the correlation is the weakest with R\({}^{2}\) being 0.22. In grassland and bare land areas, the R\({}^{2}\) are 0.23 and 0.3, respectively. In shrubland areas, the R\({}^{2}\) reaches 0.4, while further validation will be necessary in the future by increasing the samples considering that there are only 14 samples in the area. Tree cover and shrubland areas, which are more heavily vegetated, have a greater rate of delay than grassland and bare land. The delay rates for the four land cover types are 0.2, 0.3, 0.1, and 0.1, respectively.  
Figure 8: Interannual variation in average SWE under different land cover types.  
Figure 9: Correlation between the ROD extracted from stations and the SWE under different land cover types (**(a)** tree cover, **(b)** shrubland, (c) grassland, and **(d)** bare land).  
The fitting relationship between the ROD extracted from Sentinel-1 data and the SWE shows similar results (Figure 10). As the SWE increases, the ROD for all four land cover types is significantly delayed (\(p<0.05\)). Except for the shrubland area with fewer samples, the correlation is weakest in the tree cover area and greatest in the bare land. Areas covered with vegetation also showed greater ROD delay rates.
###### Abstract  
The use of massive multiple-input multiple-output (MIMO) techniques for communication at millimeter-Wave (mmW) frequency bands has become a key enabler to meet the data rate demands of the upcoming fifth generation (5G) cellular systems. In particular, analog and hybrid beamforming solutions are receiving increasing attention as less expensive and more power efficient alternatives to fully digital precoding schemes. Despite their proven good performance in simple setups, their suitability for realistic cellular systems with many interfering base stations and users is still unclear. Furthermore, the performance of massive MIMO beamforming and precoding methods are in practice also affected by practical limitations and hardware constraints. In this sense, this paper assesses the performance of digital precoding and analog beamforming in an urban cellular system with an accurate mmW channel model under both ideal and realistic assumptions. The results show that analog beamforming can reach the performance of fully digital maximum ratio transmission under line of sight conditions and with a sufficient number of parallel radio-frequency (RF) chains, especially when the practical limitations of outdated channel information and per antenna power constraints are considered. This work also shows the impact of the phase shifter errors and combiner losses introduced by real phase shifter and combiner implementations over analog beamforming, where the former ones have minor impact on the performance, while the latter ones determine the optimum number of RF chains to be used in practice.  
massive MIMO; analog beamforming; hardware impairments; millimeter-wave +
Sensors
# State of the Art in Metal Matrix Composites Research: A Bibliometric Analysis
## 1 Introduction
### Data Selection and Extraction  
The web of science (WoS) repository was selected as the primary database for the bibliometric analysis carried out in the current study. This database is a premium collection of high quality and high impact research articles published in the most prestigious journals across the globe. It catalogues manuscripts published in indexed journals since the year 1990. The WoS core collection includes eight citation indices viz. Emerging Science Citation Index (ESCI), Science Citation Index Expanded (SCIE), Social Sciences Citation Index (SSCI), Arts and Humanities Citation Index (AHCI), Conference Proceedings Citation Index--Social Sciences and Humanities (CPCI-SSH), Book Citation Index--SocialSciences and Humanities (BCI-SSH), Conference Proceedings Citation Index--Science (CPCI-S) and the Book Citation Index--Science (BCI-S). This entire WoS core collection was searched for the keywords: (\"nano\" OR \"aluminium\" OR \"machining\" OR \"fabrication\" OR \"characterization\" OR ((\"mechanical' OR \"electrical\" OR \"tribological\") In addition, \"properties\") OR \"mechanisms\" OR \"modeling\" OR \"preparation\" OR \"novel\" OR \"in situ\" OR \"scanning electron microscopy\" OR \"advanced\" OR \"carbon nanotube reinforced\" OR \"particle reinforced\" OR \"safety aspects of nano\" OR \"applications\" OR \"aerospace\") AND \"metal matrix composites\" for two decades spanning from 2001 to 2020. The search options were set to 'full record and cited references' with regard to the aforementioned keywords. The search yielded a total of 10,018 research articles which were exported from the database in plain text forms on 3 June 2021. The most exact records matching the specified MMC keywords were obtained from the ESCI, SCIE, CPCI-S, CPCI-SSH and BCI-S indices. The following sections present and discuss the different MMC research insights revealed by the bibliometric analysis.
# A Vision-Based Micro-Manipulation System
## 2 Related Work  
### Micro-Manipulation Systems  
In the field of micro-manipulation, recent developments have contributed to the refinement and advancement of techniques for handling microscopic objects. These developments reflect the ongoing efforts to improve precision, adaptability, and automation in micro-manipulation systems. For example, [PERSON] et al. explored the possibilities of vision-based manipulation of micro-parts through simulation-based experiments. The study resulted in successful grasp, hold, and release manipulations of micro-parts (400-600 \(\upmu\)m size) with a force-sensing resolution of less than 6 \(\upmu\)N, even when softness variation was introduced on the micro-object (\(\pm\)20% around the average value) [7]. [PERSON] et al. mechanically stimulated muscle cell structures using a vision-based micro-robotic manipulation system, emphasizing the importance of vision-based force measurement and correction techniques to enhance precision [8]. In the domain of biomedical microelectrode implantation, [PERSON] et al. automated the hooking of flexible microelectrode probes with micro-needles, employing visual guidance and a robotic hooking control system that operated under varying microscope magnifications [9]. Another contribution came from a robotic framework for obtaining single cells from tissue sections, incorporating an attention mechanism improved (AMI) tip localization neural network, a transformation matrix for camera-to-robot coordination, and model predictive control (MPC) to enable precise single-cell dissection from tissues, with the error of autonomous single-cell dissection being no more than 0.61 \(\upmu\)m [10]. Additionally, a 3D-printed soft robotic hand with computer-vision-based feedback control provided a novel approach to micro-manipulation, offering a remarkable degree of accuracy and precision in micro-scale object manipulation [11].
# Sustainable Cold Chain Management: An Evaluation of Predictive Waste Management Models
## 2 Literature Review  
### Leveraging AI, IoT, and Predictive Models in Cold Chain Logistics  
This literature review examines the integration of Artificial Intelligence (AI), the Internet of Things (IoT), and predictive modeling techniques in cold chain logistics, emphasizing their roles in demand forecasting, inventory management, waste reduction, and sustainable operations. The Resource-Based View (RBV) and Supply Chain Resilience Theory provide a theoretical foundation, contextualizing these technologies as strategic resources that enhance efficiency, adaptability, and resilience in cold chain systems.  
AI and machine learning (ML) applications have demonstrated significant value in optimizing cold chain logistics by forecasting demand, managing inventory, and ensuring product quality. [PERSON] et al. [4] highlighted how AI-driven tools reduced spoilage by predicting high-risk temperature fluctuations during peak summer months in food logistics. Predictive analytics, powered by ML algorithms like ARIMA and Multiple Linear Regression (MLR), enables dynamic stock level adjustments, mitigating overstocking and reducing waste [7,8]. For instance, [PERSON] and [PERSON] [9] demonstrated that ARIMA's time-series capabilities helped a dairy company reduce spoilage by 10% through effective seasonal demand forecasting. MLR complements ARIMA by incorporating multiple variables, such as weather conditions and consumer behavior, to provide a comprehensive approach to demand forecasting [7].  
The Internet of Things (IoT) is transforming cold chain logistics through real-time monitoring of environmental parameters, including temperature, humidity, and CO\({}_{2}\) levels. IoT sensors facilitate immediate responses to deviations in storage conditions, reducing spoilage risks and maintaining product quality [3]. Continuous monitoring provides end-to-end visibility, enabling data-driven decisions that protect product integrity and improve operational efficiency. When integrated with AI-driven predictive models, IoT data further enhances decision-making by enabling proactive responses to quality risks. [PERSON] et al. [1] reported that coupling IoT with AI significantly reduced waste in cold chain operations by aligning inventory levels with real-time demand fluctuations.  
Machine learning techniques like ARIMA and MLR also play a critical role in inventory management and waste reduction. ARIMA is particularly effective for analyzing cyclic demand patterns, which are common in cold chains, while MLR accounts for multiple demand drivers, such as environmental and economic factors, to provide nuanced predictions [9]. [PERSON] and [PERSON] [10] found that combining these models reduced waste in a multi-regional food distribution network by aligning inventory levels with precise demand forecasts. These findings underscore the importance of integrating ML and IoT technologies to achieve sustainable, efficient, and resilient cold chain systems.