esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In summary, we compared the performance of four rapid N-glycan analytical methods, and found they provide comparable N-glycan data. Moreover, the testing time and protein sample requirement were much lower than those for the conventional 2-AB method. The 2D-LC-MS method performed the best with the shortest testing time and the lowest protein sample requirement. These four rapid N-glycan analytical methods, especially the 2D-LC-MS method, showed great application potential in cell line development and will play an important role in the development of mAbs in the future.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14167320/s1](https://www.mdpi.com/article/10.3390/app14167320/s1)]([https://www.mdpi.com/article/10.3390/app14167320/s1](https://www.mdpi.com/article/10.3390/app14167320/s1)), Figure S1: Raw MS spectra of mAb A. (**A**) Raw MS spectra by reduction method. (**B**) Raw MS spectra by off-line IdeS digestion method. (**C**) Raw MS spectra by 2D-LC-MS method with on-line immobilized IdeS digestion; Figure S2: Comparison of five major glycans of mAb B by 2D-LC-MS method (n = 3) and rapid 2-AB method (n = 3); Table S1: The reproducibility of the major N-glycan data by reduction method in triplicate; Table S2: The reproducibility of the major N-glycan data by off-line IdeS digestion method in triplicate; Table S3: The reproducibility of N-glycan data by 2D-LC-MS method in triplicate; Table S4: The cosine value of the six major glycans of mAb A between 2D-LC-MS method and other methods; Table S5: The cosine value of the five major glycans of mAb B between 2D-LC-MS method and rapid 2-AB method.  
**Author Contributions:** Conceptualization, X.J.; investigation, X.J.; writing--original draft, X.J.; supervision, B.H.; writing--review and editing, J.C. and B.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was funded by the National Key Research and Development Program of China (2018 YFA0902000) and the Jiangsu Synergetic Innovation Center for Advanced Bio-Manufacture (No. XTC2206).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.  
**Acknowledgments:** The authors are grateful to Genovis Group for providing FabRICATOR-HPLC column in this study.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Stereo Digital Image Correlation in MATLAB
## 2 Framework Theory
### Calibration  
Calibration, explained in terms of a single camera since each camera is calibrated separately, determines the parameters of the camera model, which involves the four coordinate systems (CSs) depicted in Figure 1: (i) the three-dimensional (3D) world CS having arbitrary orientation and position; (ii) the 3D camera CS with its origin at the aperture of the camera and its z-axis aligned with the optical axis; (iii) the 2D ideal sensor CS which is coincident with the plane of the charge coupled device (CCD) representing the image in the absence of distortion; and (iv) the 2D distorted sensor CS which is coincident with the ideal sensor CS but represents the actual image by accounting for radial distortion. No symbols have been assigned to the camera CS since it is not directly involved in DIC.  
The camera model uses the pinhole camera model to project a 3D coordinate of a point on the specimen in the world CS to its corresponding 2D location in the ideal sensor CS before the radial distortion model determines its location in the distorted sensor CS. Although this camera model, and subsequent calibration process, is rather simple, it performs sufficiently well as shown in Section 4. However, the accuracy the displacements determined could be improved by using a more sophisticated camera calibration method which provides a more complete description of the camera optics such as the generic camera calibration method [58; 59].
## 4 Industry Perception Study
### Analysis of the Results of the Survey
#### 4.2.1 Belief in Defect Prediction  
The sample pool was asked about whether it believed that an artificial intelligence-based computer model, trained on software metrics, can classify software modules into two categories--defective or non-defective.  
About 66% of respondents believed that defective modules could be identified by an intelligent computer program such as DePaaS (\(\chi^{2}\) = 4.1724, CV = 3.84, \(\alpha\) = 0.05, \(p\) = 0.0411). Believers quoted advances in AI and machine learning as the primary reason to justify their belief whereas non-believers quoted poor-data, non-relation between metrics and defects, use of third-party software as main reasons not to believe in the ability of a computer program to predict defects. One respondent believed the defects are an inherent nature of the software and prediction is not as useful since each module is highly likely to be defective.
# Individuals Identification Based on Palm Vein Matching under a Parallel Environment
## 4 Palm Vein Recognition under Multi-Core Platform  
In the previous section, our proposed methodology was presented. In this work, we introduce some improvement with respect to the baseline method proposed in [45] for finger-vein recognition. Our approach aims to reduce the computation time for both feature extraction and matching processes, achieving a significant speed-up for massive individuals identification. In correspondence with this, we propose accelerating the process of comparison of finger-vein images by using a multi-core platform.  
In recent years, the popularization of multi-core processors, computer clusters, and computational grids have driven the design of parallel applications. Following this trend in the present work, a multi-thread parallel algorithm has been implemented by using OpenMP [65].  
An ideal parallelization of the system would be based on a scheme where each pair of images to be compared is related to a processor or processing thread. However, this approach would not be feasible in the problem to be treated, due to the number of images that are compared. Thus, in the proposed solution, each processing thread is responsible for a group of images. With the aim that no synchronization problems are generated between the different threads, a master-worker scheme is used. The master process is responsible for controlling the queries and collecting the results, and, on the other hand, the worker processes are used to estimate the similarity from the subset of vein images they manage. In Figure 4, we illustrate parallel queries under the aforementioned scheme with multiple arrows between storage and CPM process.  
In our approach, we use a task distribution scheme in Round-Robin fashion for the computation of similarity tests by worker processes. Let \(N\) denote the total number of similarity tests, and let \(P\) be the total number of processes. Each worker process computes one similarity test in Round-Robin fashion, as shown in Figure 6; then, each process computes \(N/P\) similarity tests.
## 3 Related Work
During the simulation, the traffic generation capacity, number of platoons, and channel load were measured on the given channels. The authors of the study [33] presented a wireless multi-channel architecture and protocol that can support safety and non-safety applications. The proposal aims to enable channel access for non-safety messages on SCH channels while ensuring that safety-related messages have priority on safety channels (CCH) without violating the QoS (Quality of Services) requirements. Priority classes were created for different kind of message types. The protocol and the simulation were implemented in NS-2, and a highway use case was presented. In the simulation, all messages are transmitted at a similar 6 Mbps data rate. Reception failure was measured during the simulation. Additionally, the NS3 discrete event network simulation environment supports IEEE 1609.4 Multi-Channel Operation protocols in the WAVE system [34]. Finally, the basic Multi-Channel Operation concept [35] is available in Artery as the European ITS-G5 protocol family simulation model. Users of this MCO extension can simulate and test static multi-channel scenarios with the implemented interfaces and data members [36]. A Service Announcement Message (SAM) model is also available for Artery;it was presented by the authors of [37]. In this work, different service deployment strategies were investigated, and initial results on details about the expected channel occupancy were provided. The authors also examined how the SAM, CAM, and CPM services could be deployed.
## 2 Mathematical Model
### Hydropneumatic Spring Model  
Several prior studies have provided mathematical expressions for independent hydropneumatic suspensions [13]. The expression for the output force of the hydropneumatic spring is as follows:  
\[F=F_{k}+F_{c}+F_{f} \tag{1}\]  
The output force of the hydropneumatic spring (Figure 2a) consists of an elastic force (\(F_{k}\)), a damping force (\(F_{c}\)), and a frictional force (\(F_{f}\)). Because there is a lubricating oil film between the surfaces of the piston and cylinder, the frictional force is minimal. Thus, the piston-cylinder friction is disregarded, and the hydropneumatic spring's external force consists of elasticity and damping.  
The expressions for the elastic force and damping force are as follows [14]:  
\[F_{k} =\frac{P_{b}V_{b}^{r}(A_{1}-A_{2})}{\left(V_{b}+(A_{1}-A_{2})(x_ {s}-x_{u})\right)^{r}} \tag{2}\] \[F_{c} =\frac{A_{2}^{3}\rho}{2}\Bigg{[}\frac{(\dot{x}_{s}-\dot{x}_{u})} {n\text{C}_{d}\Big{(}A_{u}+A_{d}\Big{(}\frac{1}{2}-\frac{1}{2}sgn(\dot{x}_{s} -\dot{x}_{u})\Big{)}\Big{)}}\Bigg{]}^{2}sgn(\dot{x}_{s}-\dot{x}_{u})\]  
\begin{table}
\begin{tabular}{c c c} \hline
**Symbol** & **Description** & **Value** \\ \hline \(m_{s}\) & sprung mass & 6764 kg \\ \(m_{u}\) & unsprung mass & 236 kg \\ \(k_{u}\) & tire stiffness & 1,433,500 N/m \\ \(C_{d}\) & check valve flow coefficient & 0.62 \\ \(C_{z}\) & orifice flow coefficient & 0.62 \\ \(\rho\) & density of hydraulic oil & 860 kg/m\({}^{3}\) \\ \(\gamma\) & polytropic exponent & 1.4 \\ \(D\) & rodless cavity diameter & 140 mm \\ \(A_{1}\) & barrel area & 0.0154 m\({}^{2}\) \\ \(d\) & diameter of piston rod & 56 mm \\ \(A_{2}\) & cavity area & 0.0025 m\({}^{2}\) \\ \(A_{u}\) & orifice equivalent area & \(2.83\times 10^{-5}\) m\({}^{2}\) \\ \(A_{d}\) & check valve equivalent area & \(2.83\times 10^{-5}\) m\({}^{2}\) \\ \(P_{b}\) & static pressure & \(1.8\times 10^{6}\) Pa \\ \(V_{b}\) & static balance volume & \(2.5\times 10^{-3}\) m\({}^{3}\) \\ \hline \end{tabular}
\end{table}
Table 1: Main parameters of the auxiliary transport vehicle.  
The stiffness and damping ratios are calculated as the rates of change in the elastic and damping forces about the piston rod's displacement as follows:  
\[\begin{split} k_{s}&=\frac{rP_{b}V_{b}^{\tau}(A_{1}-A_{ 2})^{2}}{(V_{b}+(A_{1}-A_{2})(x_{s}-x_{u}))^{\tau+1}}\\ c_{s}&=\frac{\rho A_{2}^{3}(\hat{x}_{s}-\hat{x}_{u})}{ \left[nC_{d}\left(A_{u}+A_{d}\left(\frac{1}{2}-\frac{1}{2}sgn(\hat{x}_{s}-\hat{ x}_{u})\right)\right)\right]^{2}}sgn(\hat{x}_{s}-\hat{x}_{u})\end{split} \tag{3}\]  
where \(sgn(\hat{x}_{s}-\hat{x})\) is the sign function.
# Local Batch Normalization-Aided CNN Model for RSSI-Based Fingerprint Indoor Positioning
## 4 Numerical Results
### Principal Component Analysis  
Figure 8 presents the visualization result of principal component analysis (PCA) carried out on both the training and test datasets, spanning four principal components. PCA [9; 37; 38; 39] was employed primarily for visualization, allowing us to analyze the spatial distribution of training and test datasets in a lower-dimensional space. This is instrumental in evaluating how various normalization techniques influence the data representation and, in turn, their potential implications for model generalization. In these plots, blue points represent RPs from the augmented training dataset, while orange points indicate unknown location points from the test dataset. In Figure 8a, which depicts the data distribution without the application of BN, LN, or LBN, there is significant overlap between the training data (blue) and test data (orange). This overlap highlights a high risk of overfitting, as the training and test datasets are not sufficiently distinct. Moreover, the test data are heavily concentrated in the center, which could negatively affect the model's ability to generalize to unseen data. In Figure 8b, after applying batch normalization (BN), the test data distribution becomes more dispersed, and the training data effectively span the test dataset. This broader spread reduces the risk of overfitting and improves alignment between the training and test sets. The training dataset also exhibits a more structured representation, improving clarity in the PCA transformation space. In Figure 8c, where layer normalization (LN) is applied, the data distribution improves compared to (a), with less overlap between the training and test datasets. However, the separation is not as effective as that in (b)  
(BN). This indicates that while LN contributes to improving generalization, its ability to mitigate overfitting is not as potent as that of BN. The test data show a more natural spread than (a), yet it does not cover as extensive a region as in (d) (LBN). In Figure 8d, which illustrates the distribution after applying localized batch normalization (LBN), the training dataset is evenly dispersed over a wider region in the PCA transformation space. This dispersion suggests that the model has reduced the risk of becoming trapped in local optima during training. The test dataset also demonstrates a more natural distribution, indicating improved generalization performance compared to BN (b) and LN (c).
# The Development of a Spaceborne SAR Based on a Reflector Antenna
## 3 Reflector Antenna Configurations in Spaceborne SAR Systems
### Feed Configurations for Reflector Antenna  
Specifically, in the transmission mode, the feed generates a primary beam that illuminates the reflector surface, which then shapes and directs the radiation toward the target region. In the reception mode, backscattered signals from the target are first collected and focused by the reflector before being received by the feed and transmitted to the receiver  
\begin{table}
\begin{tabular}{c c c} \hline
**Deployable Antenna Type** & **Characteristics** & **Spaceborne SAR Applications** \\ \hline Framework-Based & High deployment stiffness, excellent thermal stability, scalable modular assembly, precise surface control, but heavier structure & HJ-1-C (China, 2012), Qilu-1 (China, 2021), Kondor-FKA No.2 (Russia, 2024) \\ Perimeter Truss-Based & High deployment ratio, simplified structure, lightweight design & Acadia (USA, 2023), Tandem-L (Germany), NISAR (USA, India), EOS SAR (USA) \\ Radial Rib-Based & Simple structure, reliable deployment, high stiffness & TecSAR (Israel, 2008), RISAT-2 (India, 2009), QPS-SAR (Japan, 2019), Umbra (USA, 2021), Neptune-01 (China, 2024) \\ \hline \end{tabular}
\end{table}
Table 2: Summary of deployable antenna type classification.  
Figure 9: (**a**) Radial rib deployable antenna [35]; (**b**) TecSAR antenna [14].  
system. Generally speaking, the feeds of reflector antennas in spaceborne SAR systems are classified into the following three categories: single-beam feed, multi-beam feed, and phased array feed (PAF).  
Reflector antennas work with a feed on the focus or near the focus. Single-beam feed is commonly used due to its simple structure and ease of implementation, which suffices for many basic SAR applications. However, as SAR technology advances toward HRMS imaging and there are increasing demands for multi-polarization and multi-beam capabilities, the limitations of single-beam feeds have become apparent.  
To overcome these constraints, researchers have explored more complex feed architectures, such as multi-beam feed and phased array feed (PAF). Multi-beam feed can generate multiple independent beams simultaneously, enabling wider area coverage and parallel multi-channel reception, thereby improving mapping efficiency and supporting multi-beam SAR. One feed forms an antenna beam, so it needs to be switched to a phased array feed to achieve beam scanning. Phased array feed, leveraging electronic scanning technology, offers flexible beam steering and shaping, allowing for rapid beam scanning and dynamic beamforming. These features make PAF well suited for high-resolution, wide-swath SAR imaging and complex scene observations.  
The following sections introduce the three primary feed configurations used in space-borne SAR reflector antenna systems.
# Effective Bus Travel Time Prediction System of Multiple Routes: Introducing PMLNet Based on MDARNN
## 2 Related Work
### Impact Factor Selection  
One of the main challenges encountered in bus travel time prediction is that the bus is affected by various factors while traveling or dwelling [13]. The main impact factors selected by related studies can be categorized as macro impact factors and local impact factors based on their characteristics.  
Macro impact factors are defined as the features that are typically the same for all buses or do not change for a long time. For example, weather conditions (sunny or rainy) affect bus running state and passenger boarding time [5,6], making them common input features for prediction models in various studies. Previous researches and observations of bus travel data reveal different distribution patterns in peak and off-peak periods, weekdays, and weekends [14,15]. Therefore, some of the literature includes hour and date information as the influencing factors for predicting bus travel time [9,16]. Additionally, temperature [17,18] and air quality [19] have also been investigated as the impact factors for bus travel time prediction.  
Local impact factors are defined as features that vary over a short period of time and differ between buses. Bus running states, such as speed and acceleration, have real-time effects on bus travel time [7]. These states are often acquired from Automatic Vehicle Location (AVL) or a Global Positioning System (GPS) and utilized as input features for bus travel time prediction models [20,21]. While some of the literature focuses on analyzing the influences of real-time traffic conditions, other studies like [22] predict bus travel time by integrating traffic flow data. In one study [3], two traffic factors are defined to express real-time traffic state. Inspired by this, four impact factors are defined to correct four predicted time parts, which consider the recent bus traveling or dwelling status in the most recent period to map the real-time traffic conditions.  
Another factor to be considered is the construction of travel chains. Due to the sensitivity of individual passenger data, obtaining public transport transfer data is difficult. Therefore, it is necessary to build travel chains with transfer behavior. One study [9] generated sufficient (pseudo) travel chains from historical bus trajectories of multiple routes. Another study [10] reconstructed travel chains by merging data from different sections of different bus routes. It is easy to reconstruct travel chains from the travel data of different routes. In this paper, bus travel chains are constructed based on the bus trajectory data of multiple routes to compensate for the lack of transfer data.
# A Phase Bias Compensation Method for Anti-Interference Antenna Arrays in RTK Positioning
## 5 Conclusions  
This paper has proposed a two-step phase bias compensation method for anti-interference antenna arrays consisting of rough bias compensation (based on array weighted-coefficient conjugation) and fine bias compensation (applied to the anti-interference output signal). Compared to the conventional MVDR algorithm, our approach achieves a significant improvement in angular accuracy with a simplified structure and lower engineering cost. There are two key contributions in the paper. First, under the simulated conditions of JSR = 63 dB, CNR = 60 dB, and JNR = 50 dB, the mean phase bias is reduced to 0.25\({}^{\circ}\), and the variance phase bias is reduced to 4.62\({}^{\circ}\). At the situation where CNR \(\geq\) 60 dB, the method can compensate the phase bias successfully. Second, the two-step compensation architecture eliminates the need for complex real-time calibration, enabling high-precision positioning (via PI algorithm integration) while maintaining computational efficiency. However, this paper has not considered the error due to mutual coupling between array elements. In the applications of small arrays and receiver positioning, this omission would theoretically affect the accuracy of phase bias compensation. In the future  
Figure 14: The phase bias compensation at different JSR values. (**a**) JSR = 51 dB. (**b**) JSR = 54 dB. (**c**) JSR = 63 dB. (**d**) JSR = 73 dB.  
work, we will analysis the influence of mutual coupling and extend this framework to multi-band scenarios.  
Methodology, J.Z.; software, J.Z.; validation, J.Z.; formal analysis, J.Z. and W.F.; investigation, W.F.; resources, W.F.; data curation, W.F.; writing--original draft, J.Z. and H.W.; writing--review and editing. J.Z.; visualization, H.W.; supervision, H.W.; project administration, H.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China, grant number 2024 YFC3015605.  
The data presented in this study are available on request from the corresponding author due to privacy restrictions.  
The authors declare no conflicts of interest.