esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Study on Ultimate Bearing Capacity
### Comparison between Test Values and Calculated Values in Specifications of Bearing Capacity
Since the bearing capacity formulas in Chinese GB50936-2014 and European EC4 both consider the benefit of the confinement effect on bearing capacity, while American AASC360-10 does not, and the values of partial factors for materials vary from country to country, the percentage errors between the calculated values in specifications of different countries and the test values in this paper are different. It can also be seen from the comparison of the PT, BZ, and QZ samples in Table 5 that the calculated values of normal concrete in specifications are in the best agreement, with the smallest error and percentage dispersion between the calculated values and the test values. However, the full-high titanium slag concrete has a poorer degree of coincidence, with relatively large error and dispersion \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{2}{c}{**EC4 (2004)**} & \multicolumn{2}{c}{**AASC360-10**} & \multicolumn{2}{c}{**GB50936-2014**} \\ \cline{2-7}  & \(\mu\) & \(\sigma\) & \(\mu\) & \(\sigma\) & \(\mu\) & \(\sigma\) \\ \hline Total sample & 97.16\% & 1.93\% & 90.47\% & 1.43\% & 98.59\% & 1.56\% \\ PT Sample & 97.48\% & 1.27\% & 90.94\% & 1.16\% & 99.65\% & 1.19\% \\ BZ Sample & 96.99\% & 1.70\% & 90.41\% & 1.42\% & 98.34\% & 1.39\% \\ QZ Sample & 96.37\% & 2.60\% & 90.22\% & 2.02\% & 98.12\% & 2.23\% \\ \hline \hline \end{tabular} Note: PT Sample represents the normal CFST sample; BZ Sample represents the half-high titanium slag CFST sample; QZ Sample represents the full-high titanium slag CFST sample; \(\mu\) and \(\sigma\) are the mean and standard deviation of percentages of calculated values and test values, respectively.  
\end{table} 
Table 5: Percentage error analysis between calculated values and test values. Figure 9: Comparison of measured values of bearing capacity and values calculated by various specifications. degree. The half-high titanium slag concrete is in the middle. The reason is that the specifications of various countries all establish CFST bearing capacity formulas based on normal concrete, while high titanium slag concrete and normal concrete have obvious microscopic differences (Figure 2). Therefore, compared with normal concrete, the error and dispersion degree of high titanium slag concrete are slightly larger. In spite of this, there is no significant difference in the percentage between the calculated values of CFST bearing capacity with high titanium slag and those of normal CFST, indicating that the microscopic difference of the two kinds of concrete has little influence on the macroscopic mechanical behavior.
# Gaussian Dynamic Convolution for Semantic Segmentation in Remote Sensing Images
## 2 Related Work  
### General Semantic Segmentation  
Semantic segmentation, as one fundamental task in the area of computer vision and image processing, has a wide range of application prospects, and it can be said that accurate recognition cannot be produced without proper segmentation. Traditional semantic  
Figure 1: (**a**) The fixed receptive field of the dilated convolution; (**b**) The dynamic receptive field of the Gaussian dynamic convolution.  
segmentation methods mainly extract low-level features of images for segmentation by hand-made feature descriptors, which are very tedious and too dependent on the a priori knowledge of relevant experts, and often only two types of semantic segmentation can be achieved, and multiple operations have to be performed if the object being segmented has more than one target, which brings very great inconvenience to the segmentation task at that time. This situation did not end until the advent of the machine learning era.  
With the prosperity of deep convolutional neural networks (DCNN) [10], semantic segmentation has entered the era of deep learning. In 2015, [PERSON] et al. proposed an enabling end-to-end and pixel-to-pixel approach FCN [11], which first induced deep learning to semantic segmentation. Many subsequent approaches have adopted the \"encoder-decoder\" structure, such as U-Net [2], SegNet [12], RefineNet [13], and PSPNet [3]. The HRnet [14] reduced the semantic information lost during upsampling and downsampling by employing branches of different resolutions as parallel structures and maintaining the information interaction between different branches. It allows the whole structure to maintain high resolution from the beginning to the end. [PERSON] et al. [15] proposed Gaussian dynamic convolution, which can dynamically select sampling regions based on the offset of the Gaussian distribution, and they further constructed a lightweight network to handle the complex single-image segmentation task. [PERSON] et al. [16] introduced the embedded attention module to generate feature bases and continuously update them using contextual information to ensure accuracy while greatly reducing the computation and improving segmentation efficiency.  
Although these methods mentioned above are widely used in the segmentation task of natural scenes and have achieved remarkable results, they still have the problems of insufficient quality and accuracy in high-resolution remote sensing images. This is mainly because the objects on the remote sensing images show their large-scale variation compared with natural scenes. Moreover, the objects are densely arranged and the boundaries are blurred. Therefore, special segmentation methods in high-resolution remote sensing images are required to handle the problem of the large variety of objects.
# Three-Dimensional Guidance Laws for Spacecraft Propelled by a SWIFT Propulsion System
## 2 Thrust Vector Model for Trajectory Design in a 3D Mission Scenario
#### Maximization of the Projection of Vector \(\mathbf{a}\) along a Given Direction
In the special case of \(\alpha_{d}=180\,\text{deg}\), that is, when the direction of \(\mathbf{\hat{d}}\) is purely radial with \(\mathbf{\hat{d}}\cdot\mathbf{\hat{i}}_{\text{R}}=-1\), we assume that \(\mathbf{\hat{t}}^{\star}\) belongs to the \((\mathbf{\hat{i}}_{\text{R}},\mathbf{\hat{i}}_{\text{T}})\) plane; therefore, using Equations (4) and (7), the result is  
\[\mathbf{\hat{t}}^{\star}=\cos\alpha_{\text{max}}\,\mathbf{\hat{r}}+\frac{\sin\alpha_{ \text{max}}}{\|\mathbf{h}\|}\,(\mathbf{h}\times\mathbf{\hat{r}})\quad\text{if }\,\,\alpha_{d}=180\,\text{deg} \tag{16}\]  
The vector Equation (14) is quite general, and it can be used to obtain the optimal guidance law in a typical heliocentric orbit transfer by using a set of different forms of the spacecraft state vector, as discussed in the next section. Figure 5: Configuration of the unit vector \(\hat{\mathbf{z}}^{\star}\) that maximizes the scalar index \(P\) defined in Equation (10); see also Equation (14). The red cone indicates the forbidden zone related to the presence of the SWIFT conical mesh of conducting wires. (**a**) Case of \(a_{d}\leq a_{\text{max}}\). (**b**) Case of \(a_{d}>a_{\text{max}}\).
# Preparation of Polyacrylate Hollow Microspheres via Facile Spray Drying
## 1 Introduction  
Polymer hollow microspheres have drawn major attention because of their large specific surface area, relative low density, and high encapsulation capability [1; 2; 3; 4]. As a result, they have found a wide range of applications, including drug delivery, catalysis, and coatings [5; 6; 7]. Specifically, hollow microspheres can be used as drug carriers, improving the flowability and packability when compared with the raw crystals of drugs [8]. Single-hole hollow polymer microspheres with specific high-capacity uptake of target species may also provide new opportunities in the capsulation of drugs [9]. Silica/polymer hollow functional polymer microspheres prepared by reversible addition-fragmentation chain transfer (RAFT) polymerization were applied as a reservoir for nitric oxide (NO) [10]. Moreover, conductive polymer hollow microspheres with high specific surface areas and outstanding electrical properties exhibited superb microwave absorption performance [11]. Therefore, it is meaningful to develop a facile and scalable method to prepare hollow microspheres for various applications.  
Polymer hollow microspheres are mainly prepared via three approaches: self-assembly, template assisted synthesis, and emulsion polymerization [12; 13; 14; 15]. Self-assembly and template assisted synthesis are demanding on reaction system and processing procedures, while emulsion polymerization is more industry-friendly and scalable. In addition, this emulsion polymerization method can effectively control sphere size, structure, and composition. Therefore, emulsion polymerization is an ideal method to prepare polymer hollow microspheres, especially at a large scale [16; 17]. [PERSON] et al. prepared hollow polystyrene particles by seeded emulsion polymerization [18], which has many advantages,including being less dependent on the type of base polymer used, more friendly to environment, and more easy to scale up. However, further water absorption by the particles may occur during the polymerization, thus making it challenging to control the hollow structure.  
In recent years, preparation of polymer microspheres by spray drying has attracted attention, in which an atomizer is employed to make small droplets with subsequent flash evaporation by hot air to obtain microspheres. Compared with other methods, spray drying has the advantages of a facile process, high controllability, and ease of commercialization [19; 20]. However, preparing hollow microspheres with controllable size and morphology by spray drying remains a challenge. We aim to develop a facile and scalable method to prepare polymer hollow microspheres by combining emulsion polymerization and spray drying.
## 4 Results  
The trained model is applied to the testing dataset for classification tasks. A comparison is made to assess the impact of adding a second-stage classification model on the final classification results. The confusion matrices under the two scenarios are shown in Figure 8. It can be observed that after the second-stage classification, the confusion issue between the normal and outlier categories has improved. This framework not only resolves the confusion problem between the two abnormal data types but also extends the perceptual ability of the 1D CNN classification model to details, with clear principles and high result credibility.  
For the other types, after passing them through the 1D CNN model under the current bridge's data anomaly characteristics, they already exhibited high classification accuracy. There are a few misclassifications between the minor and outlier categories, mainly due to the coupling phenomenon of multiple abnormal types within data sample segments. This is influenced by subjectivity during manual labeling. In practice, as long as the normal category obtained from the classification has high credibility, it meets the requirements for data analysis. The exact classification of data anomalies into specific categories is not the primary concern.  
The performance evaluation metrics mentioned earlier were used to further explain the results for the two-stage approach, and the results are presented in Table 3. It can be observed that the calculations for normal, minor, and noise are all close to 100%, indicating a good classification performance. However, the results for the outlier and biased categories show a slightly lower performance. This discrepancy can be attributed to two main factors:  
Class Imbalance: The severe class imbalance in the dataset leads to insufficient training of the model. The presence of a majority class can dominate the learning process, resulting in less emphasis on the minority classes, like outlier and biased.  
Figure 8: The confusion matrix of a CNN model only and a two-stage classification model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Performance** & **Normal** & **Minor** & **Outlier** & **Biased** & **Noise** \\ \hline Precision & 99.92\% & 99.87\% & 85.71\% & 77.17\% & 100\% \\ Recall & 99.76\% & 98.48\% & 72.00\% & 97.26\% & 100\% \\ \(F_{\text{1}}\) & 99.84\% & 99.17\% & 78.26\% & 86.06\% & 100\% \\ Accuracy & & & 99.22\% & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance evaluation metrics of the model on the test set.  
Subjectivity in Labeling: The coupling of multiple types of data anomalies during manual labeling introduces subjectivity. This subjectivity might lead to ambiguous cases that are difficult to classify, particularly for the outlier and biased categories.  
Moreover, the limited size of the test set amplifies the impact of misclassifications, even if the number of misclassified samples is minimal. Therefore, while the overall classification performance seems satisfactory, the influence of misclassifications on the metrics is significant due to the small test sample size.
## Round-Off Noise of Multiplicative FIR Filters Implemented on an FPGA Platform  
**[PERSON] 1,*, [PERSON] 2 and [PERSON] 1**  
1 Department ESAT, KU Leuven Kulab, Zeedijk 101 Ostend 8400, Belgium;  
E-Mail: [EMAIL_ADDRESS]  
2 School of Engineering and Digital Arts, University of Kent, Canterbury Kent CT2-7 NT, UK;  
E-Mail: [PERSON]L_ADDRESS]  
* Author to whom correspondence should be addressed;  
E-Mail: [EMAIL_ADDRESS]; Tel.: +32-59-569-017; Fax: +32-59-569-001.  
_Received: 28 November 2013; in revised form: 7 February 2014 / Accepted: 19 February 2014 / Published: 25 March 2014_  
**Abstract:** The paper analyzes the effects of round-off noise on Multiplicative Finite Impulse Response (MFIR) filters used to approximate the behavior of pole filters. General expressions to calculate the signal to round-off noise ratio of a cascade structure of Finite Impulse Response (FIR) filters are obtained and applied on the special case of MFIR filters. The analysis is based on fixed-point implementations, which are most common in digital signal processing algorithms implemented in Field-Programmable Gate-Array (FPGA) technology. Three well known scaling methods, _i.e._, L\({}_{2}\) bound; infinity bound and absolute bound scaling are considered and compared. The paper shows that the ordering of the MFIR stages, in combination with the scaling methods, have an important impact on the round-off noise. An optimal ordering of the stages for a chosen scaling method can improve the round-off noise performance by 20 dB.  
**Keywords:** MFIR; FIR-filters; linear phase filters; FPGA; fixed point digital signal processing DSP; round-off noise; filter cascade structure
# A Connectivity-Based Clustering Scheme for Intelligent Vehicles
## 2 Literature Review
### Link Connectivity Metric for Cluster-Based Routing Schemes  
Link connectivity is an essential factor to measure the efficiency of network communications and users' satisfaction. Connectivity directly impacts channel contention and vehicle communications, particularly in an exceptionally dynamic changing network [21]. Unfortunately, in the creation of existing protocols for VANETs, link connectivity is not considered. The authors analyzed the connectivity features in [21] and introduced a connectivity-aware Media Access Control (MAC). protocol for a platoon of intelligent vehicles. Moreover, the authors also evaluated the probability of connectivity for the V2V and V2I communication scenarios in one way and two-way VANETs, respectively. In [21], the definition of connectivity was considered a function of node density and transmission range. Later, [PERSON] et al. [11] proposed a new mobility metric called generalized speed factor (GSF) by expanding an existing speed factor based on the assumption that all vehicles have the same speed at all times. The primary theme of GSF was to identify an actual relationship between inter-vehicle spacing and the relative velocity of consecutive vehicles. The work in [11] analyzed vehicle connectivity in terms of GSF-based mobile scenarios. The proposed definition of connectivity in [11] is significantly worthy because of the consideration of three factors, i.e., vehicles density, transmission range, and speed.  
The literature encompasses several metrics for cluster formation and cluster head selection [3; 6; 10]. Every performance metric has its pros and cons under various conditions. The link reliability metric has been recently used as a metric for CH selections [3; 20]. From the definition of link reliability, we can see that it only considers the speed and transmission range of vehicles. Thus, link reliability criteria will not be reliable in various traffic scenarios (i.e., sparse and dense), and as a result, it can affect a routing scheme's performance. On the other hand, link connectivity considered speed, transmission range, as well as node density. Thus, we used link connectivity as a metric for cluster formation and CH selection in our clustering scheme (i.e., connectivity-based clustering).
# Robustness of STIRAP Shortcuts under Ornstein-Uhlenbeck Noise in the Energy Levels
## 4 Conclusions  
We studied the performance of two shortcuts to adiabaticity for the STIRAP system, in the presence of dephasing by exponentially correlated noise. For the first shortcut the mixing angle was a polynomial function of time, while the second shortcut was derived from Gaussian pulses. From numerical simulations, we found that both shortcuts performed quite well and robustly even in the presence of relatively large noise amplitudes, while their efficiency was degraded when the noise correlation time increased. With similar pulse amplitudes and durations, the efficiency of classical STIRAP was highly degraded even in the absence of noise. We also found that, when using pulses with similar areas for the two STIRAP shortcuts, the shortcut derived from Gaussian pulses appeared to perform better. The current work is expected to have a wide range of applications, since STIRAP plays an important role in the implementation of many modern quantum technologies.  
Conceptualization, E.P. and D.S.; methodology, D.S. and K.B.; software, K.B.; validation, D.S.; investigation, K.B.; writing, original draft preparation, D.S., K.B., and E.P.; writing, review and editing, E.P., D.S., and K.B.; visualization, K.B. and D.S.; supervision, E.P.; funding acquisition, E.P. All authors read and agreed to the published version of the manuscript.  
The research is implemented through the Operational Program \"Human Resources Development, Education and Lifelong Learning\" and is co-financed by the European Union (European Social Fund) and Greek national funds (Project EABM34, Code MIS 5005825). The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
STIRAP Stimulated Raman adiabatic passage
# Laplacian Eigenmaps Dimensionality Reduction Based on Clustering-Adjusted Similarity
## 3 Experiment Setup
### Comparing Methods  
We compare LE-CAS against four different GDR methods: the original LE [21], GGLE [27], S-LE [28] and SCPLE [29]. The latter three comparing methods try to improve LE from different aspects and have close connection with our work. These comparing methods were introduced in Section 1. We first apply these dimensionality reduction methods to project the high-dimensional samples into a low-dimensional subspace. After that, the widely adopted \(k\)-means [22] clustering is applied to cluster the samples projected in the subspace by the respective comparing method.  
In the experiments, we specify (or optimize) the input parameters of these comparing methods as the authors suggested in the original papers. As to LE-CAS, \(k_{1}\) is determined by calculating the contour coefficient to obtain a better result of initial clustering. \(k_{2}\) =10 is used to construct \(k\)NN graph. The sensitivity of \(k_{2}\) for these \(k\)NN graph-based methods (LE [21], GGLE [27], and LE-CAS) and \(\sigma_{m}\) for Gaussian heat kernel function-based methods (GGLE [27], LE-CAS) will be studied later.
## 3 Description of Comprehensive Techno-Economic (CTE) Model
### Business Level in the CTE Model
#### 3.4.3 \"Human Resources (HR)\" Area  
Human Resources (HR) is a part of all companies in all business sectors. Human Resource Management (HRM) has many definitions [70; 71; 72]. HRM focuses on managing people at work or employees who make up an organization. Each company must establish goals for HRM. The main goals are as follows [73]: increasing the net assets of owners,enhancing employee development and satisfaction, maximizing customer satisfaction, achieving cooperate growth, increasing market share, fulfilling social responsibility, achieving financial stability and increasing the quality of products and services. According to another source [74], managerial functions of personnel management involve planning, organizing, directing and controlling (Table 3).  
The CTE model gives us definitions and equations for all items in this area. This article gives us an example of the item \"Quality of managers\". It is defined as:  
\[QoM=\left(\frac{QoM_{TL}\cdot F_{TL}}{QoM_{RefTL}}+\frac{QoM_{ML}\cdot F_{ML}}{ QoM_{RefML}}+\frac{QoM_{LL}\cdot F_{LL}}{QoM_{RefLL}}\right).0.1\]  
The equation consists of three parts and defines the assessment of three levels of management: top, medium and low. The equation can have more components, but through the performed analysis, this distribution provides the optimal approach.