esa-sceva/satcom-chunk-collection huggingface dataset:
# A Message Passing-Assisted Iterative Noise Cancellation Method for Clipped OTFS-BFDM Systems
## 2 OTFS-BFDM System
### CCDF of PAPR  
The OTFS-BFDM system extends the waveform flexibility by using long non-rectangular PWFs such as root raised cosine (RRC), raised cosine (RC), and Dirichlet. The problem, however, is that as the Doppler bins (\(N\)) increased, the PAPR of the OTFS-BFDM system becomes unbearably high. For convenience, OTFS using rectangular PWFs can be represented by \"OTFS-Rect\". Similarly, the terms \"OTFS-RRC\", \"OTFS-RC\", and \"OTFS-Dirichlet\" can also be obtained.  
The PAPR of the OTFS-BFDM signal \(s(\ell)\) on each frame is defined as:  
\[PAPR=10\log\frac{\max_{0\leq\ell\leq O\cdot(L-1)}\{|s(\ell)|^{2}\}}{E\{|s( \ell)|^{2}\}}, \tag{15}\]  
where \(O\) is the oversampling factor and \(E\{\cdot\}\) denotes the mathematical expectation. In this paper, \(O\) is assumed to be 4. The complementary cumulative distribution function (CCDF) is adopted to evaluate the PAPR performance of the OTFS-BFDM signal. The CCDF can be expressed by calculating the probability that the PAPR value of each sample exceeds the predefined threshold \(PAPR_{0}\) as follows [24]:  
\[C_{PAPR}(PAPR_{0})=\text{Pr}(PAPR>PAPR_{0}) \tag{16}\]  
Figure 1(a) reveals the PAPR performance of OTFS-BFDM with different \(N\), where \(M=32\) and the symbol mapping is 4-QAM. For any value of \(N\), when \(\alpha=0\) (\(\alpha\) is the roll-off factor of RRC or RC), the CCDF curve of PAPR of OTFS-BFDM system with any PWFs is almost the same since the peak values of these PWFs in the time domain are close. When \(N=4\), at a given CCDF \(=10^{-3}\), the \(PAPR_{0}\) of OTFS-Drichlet signal is 1 dB smaller than the OFDM signal. Furthermore, when \(N=16\), the \(PAPR_{0}\) of the OTFS-Dirichlet signal is almost the same as the OFDM signal. However, when \(N=64\), the \(PAPR_{0}\) of the OTFS-Dirichlet signal is 0.3 dB larger than the OFDM signal. This confirms that as \(N\) increases, the PAPR of the OTFS-BFDM signal becomes higher. When \(N\) increases to a certain extent, the high PAPR of the OTFS-BFDM signal will affect the system performance.  
Figure 2b illustrates the PAPR performance of OTFS-BFDM with different PWFs, where \(M=32\), \(N=31\), and the symbol mapping is 4-QAM. At any given CCDF, the \(PAPR_{0}\) of OTFS-BFDM with different PWFs is greater than OFDM. Moreover, the smaller the \(\alpha\) is, the better PAPR performance of OTFS-BFDM signal is. This is because as \(\alpha\) increases, the time-domain localization of the PWF becomes better and the peak value becomes larger.  
Thus, it is necessary to take measures to suppress the PAPR of OTFS-BFDM signals. In this paper, the OCF method is preferred without compromising the sparsity of the ECM.
## 6 Conclusions  
Experimental tests and extensive numerical analyses considering different factors have been performed to explore the influence of the overlying foundation excavation on the existing twin-tube shield tunnels. The following conclusions can be drawn:  
1. As a result of the excavation, the tunnels undergo heaves and horizontal displacement, and the tunnel linings are placed in compression at the side walls and in tension at crown and invert. The changes of the earth pressure at tunnel crown and invert are significantly greater than that at side walls.
2. Excavation also causes significant changes in the axial forces and the bending moments at tunnel side walls, especially the tunnel-liner side near the foundation pit. So, it is recommended for designers to consider these effects and a more reserved capacity of tunnel liner while designing the tunnels in possible excavation areas, so as to ensure that these tunnels operate safely.
3. The parametric analysis confirms that the factors, including the cover depth of the tunnel, the length and unloading ratio of excavation as well as the lateral pressure coefficient, impose significant effects on tunnel displacement responses. Therefore, it is also suggested that these influential factors need to be fully considered during the design and construction of, or countermeasures employed to, an excavation adjacent to existing tunnels.
4. In this study, a reduced-scale experimental test was carried out to analyze excavation-associated issues, which is meaningful work in developing the research for underlying tunnel stability. It should also be noted that the reported findings do not consider the axis distortion effects resulting from the tunnel-soil interactions. For this purpose, further research on this subject will be conducted in order to more effectively evaluate the deformational and structural response of tunnels due to excavation activities.  
Conceptualization, X.C. ([PERSON]) and T.H.; methodology, T.H.; software, X.C. ([PERSON]); validation, T.H. and X.C. ([PERSON]); formal analysis, X.C. ([PERSON]); investigation, X.C. ([PERSON]); resources, T.H.; data curation, X.C. ([PERSON]); writing--original draft preparation, X.C. ([PERSON]); writing--review and editing, Z.L.; visualization, X.C. ([PERSON]); supervision, T.H.; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 41807266; Natural Science Foundation of Anhui Province, grant number 1808085 QD112.  
**Institutional Review Board Statement:** Not applicable.  
Figure 19: Influence of \(K_{0}\) on tunnel displacement.
## 4 Conclusions  
In this paper, porous P(S-DVB) microparticles with high specific surface areas were successfully synthesized by the two-step activated swelling method. The effects of the porogens, swelling temperature and crosslinking agent were investigated in detail. The largest surface area is obtained when the toluene to DBP porogens ratio is 3:1, and porous P(S-DVB) microparticles have the roughest surface when a swelling temperature of 25 \({}^{\circ}\)C is applied. The obtained P(S-DVB) particles were modified by sulfonation and self-assembly positive charged photosensitive DR on the surface. After treatment with UV light, the ionic bonding between sulfonated P(S-DVB) and DR was converted into covalent bonding through a unique photochemistry reaction of DR. Depending on the chemical structure of DR and mobile phase composition, the P(S-DVB)-DR stationary phase performed different separation mechanisms, including reversed phase and hydrophilic interactions. Thus, a variety of baseline separations of benzene analogues and organic acids was achieved by using the P(S-DVB)-DR particles as packing materials in HPLC. According to the \(\pi\)-\(\pi\) interactional difference between carbon rings of fullerenes and benzene rings of P(S-DVB)-DR, C\({}_{60}\) and C\({}_{70}\) were also successfully separated in the HPLC column packed with P(S-DVB)-DR particles. Compared with bare P(S-DVB) packing material, the DR surface modification contributed to the high efficiency of the P(S-DVB) stationary phase for mix-mode separation.  
**Supplementary Materials:** The following are available online at www.mdpi.com/1996-1944/10/4/440/s1, Figure S1: Nitrogen adsorption-desorption isotherms of P(S-DVB)-DR. Inset is the distributions of pore size. Figure S2: Separation on C18 column for benzene analogues. Column, 75 mm \(\times\) 4.6 mm I.D.; injection size, 3 \(\mu\)L; mobile phase, methanol.  
**Acknowledgments:** This work is financially supported by the National Natural Science Foundation of China (21375069, 21574072, 21675091, 21404065), the Natural Science Foundation for Distinguished Young Scientists of Shandong Province (JQ201403), the Taishan Young Scholar Program of Shandong Province (tsqn20161027), the Key Research and Development Project of Shandong Province (2016 GGX102028, 2016 GGX102039), the Project of Shandong Province Higher Educational Science and Technology Program (J15 LC20), the Scientific Research Foundation for the Returned Overseas Chinese Scholars of State Education Ministry (2011568), the People's Lvivelihood Science and Technology Project of Qingdao (166257 nsh), and the Postdoctoral Scientific Research Foundation of Qingdao(2015128).  
**Author Contributions:** [PERSON], [PERSON] and [PERSON] conceived and designed the experiments. [PERSON] and [PERSON] conducted the experiments. [PERSON], [PERSON] and [PERSON] discussed the results. [PERSON], [PERSON] and [PERSON] wrote the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Performance Evaluation of the Active Support System for the 4m SiC Primary Mirror  
### Verification of the Bending Mode  
In Section 3.1.2, the theoretical values of 51 orders of valid bending modes of the 4m SiC primary mirror were obtained. To compare the theoretical and actual values of the bending modes, the bending mode forces \(\mathbf{F}_{Bi}\) were sequentially applied to the primary mirror, and the responses of \(\mathbf{F}_{Bi}\) were the actual values of the bending mode \(\mathbf{B}_{i}\). The results are shown in Figure 12. Figures 5 and 12 show that the theoretical and actual values of most \(\mathbf{B}_{i}\) values were very similar. However, certain differences existed between the theoretical and actual values of the first- and seventh-order bending modes. This was because the tilt was eliminated in Figure 5, but not in Figure 12. Overall, the designed support system for the 4m SiC primary mirror could accurately generate various bending modes.  
Figure 12: Actual values of partial bending mode \(B_{i}\) of the 4m SiC primary mirror. (**a**) Actual values of \(B_{1}\); (**b**) Actual values of \(B_{3}\); (**c**) Actual values of \(B_{5}\); (**d**) Actual values of \(B_{7}\); (**e**) Actual values of \(B_{9}\); (**f**) Actual values of \(B_{11}\); (**g**) Actual values of \(B_{13}\); (**h**) Actual values of \(B_{15}\); (**i**) Actual values of \(B_{17}\); (**j**) Actual values of \(B_{19}\).  
To verify the proposed compensation method for the force on the hardpoints, we attempted to use the first 40 orders of bending modes to correct the gravity deformation of the primary mirror at an elevation angle of 45\({}^{\circ}\). The surface shape before the correction is shown in Figure 13b, with a rms of 165.3 nm. If the force on the hardpoint was not compensated, the corrected result was as shown in Figure 14a. At the position where the hardpoints came into contact with the back plate of the primary mirror, the surface of the mirror showed obvious protrusions and dents. The rms of the surface shape was 50.4 nm. From both the data and image perspectives, the surface shape was very poor. Based on Figure 13b, we compensated for the force on the hardpoints and obtained the situation shown in Figure 14b, which had a surface shape rms of 129.1 nm. After the force was compensated for, the surface correction was performed again, and the first 40 orders of bending modes were also used. The results are shown in Figure 14c. The rms of the surface shape in Figure 14c was 12.4 nm, which was less than the \(\lambda/30\) (\(\lambda=632.8\) nm) requirement. A comparison of Figure 14a,c showed that compensating for the force on the hardpoints played an important role in improving the accuracy of the corrected primary mirror surface shape, with the rms of the surface shape reduced from 50.4 nm to 12.4 nm. This result also preliminarily proved the effectiveness of the surface shape correction based on the bending modes.
# Equivalent Dynamic Analysis of a Cable-Driven Snake Arm Maintainer
## 5 Simulation and Experiments
### Traction Force Experiments  
The effectiveness of the above equivalent dynamic control in engineering applications depends on the accuracy of the theoretical equivalent dynamics model. The higher the accuracy of the equivalent dynamics, the better the control of the controller. Therefore, we designed a SAM prototype, as shown in Figure 11, for experimental comparison of the accuracy of the theoretical equivalent dynamics model. The dimensions of each joint and drive box are 80 \(\times\) 80 \(\times\) 200 mm and 400 \(\times\) 300 \(\times\) 430 mm, respectively. The motor uses a 600 W AC servo motor with its own power-off brake and encoder, whereas the motion controller uses a high-end multiaxis motion control card.  
Two SAMs with different arm lengths were designed to compare and verify the dynamics and positional accuracy. See Supplementary Materials for videos of the experiments. Figure 11a,b shows the single-joint group test prototype of the SAM. Here, the single-joint group contains three rigid joints, and the drive cables of each joint are distributed according to a specific position, as shown in Figure 11b. The rotation angle of each joint in the joint group was measured by installing a gyroscope angle sensor in the foremost joint unit. The driving force of each cable in the joint group was measured by installing a tension sensor in the end drive box, as shown in Figure 11d. A calibration test of the end position accuracy of the SAM using a laser tracker is shown in Figure 11c.  
The structural rationality and dynamic characteristics were analyzed using the open-loop speed prospective control test. Here, the joint group was set to complete three cycles of sine motion trajectory in both the horizontal and vertical directions. The end-effector position of the SAM is first calibrated using a laser tracker. The angle sensor data of the end joint are shown in Figure 12a,c. The joints in the joint group can track the target trajectory effectively during the movement, with an average angle error of less than \(1\times 10^{-2}\) rad, which verifies the rationality of the underactuated continuum joint design. The cable traction forces of the end joint measured by the force sensor are shown in Figure 12b,d. During each joint angular velocity zero and reverse acceleration phase, a large change in cable traction force occurs. This is mainly caused by the increased friction of the cable in the joint through-hole. Next, a forcibly equivalent dynamic transformation of the experimentally obtained cable traction forces is performed based on the decoupling algorithm described in Section 3.  
Figure 11: SAM prototype test. (**a**) Single-joint group prototype. (**b**) Gyro angle sensor installation position. (**c**) Laser tracker end position calibration. (**d**) Force sensor installation position.
## 4 Results
### Dataset X3
#### 4.3.3. _Feature Contribution for Dataset X3_  
In Figure 13, the feature contributions for Dataset X3 are depicted, outlining significant features and their implications.  
The absolute loadings of the first principal component in Dataset X3 offer valuable insights into the relative importance of each feature requirement in the development of new sports earphones. A detailed analysis of the feature contributions reveals the following insights.  
Firstly, FR2 focusing on structure for support and stability, emerges as the most significant feature, with the highest loading indicating its pivotal role in product development. Moreover, FR1 (material for durability and quality) and FR7 (appearance for aesthetic appeal) are identified as highly significant features, exhibiting substantial loading values that highlight their importance in ensuring product quality and visual appeal.  
Figure 12: Scree plot of PCA result: Dataset X3.  
Additionally, FR10 (diaphrag design), FR9 (connection wire length), FR8 (weight for comfort and wearability), FR6 (power consumption for battery efficiency), and FR5 (battery capacity for usage duration) demonstrate notable loadings, indicating their significant impact on product performance and user experience. While exhibiting moderate loading, they are considered less significant than FR2, FR1, and FR7.  
On the other hand, FR3 exhibits a relatively lower loading, suggesting its lesser impact on product performance. Meanwhile, FR4 has the lowest loading, indicating its minimal impact on overall product quality in this context.  
This detailed feature analysis provides valuable guidance for prioritizing feature requirements in sports earphone development, enabling stakeholders to focus on factors that most significantly impact product performance, user experience, and market competitiveness.
## 3 Methodology
### Complex Network Analysis and Girvan-Newman Method
#### 3.2.6 Modularity  
Modularity can be considered to be one of the ways to identify community structures in a network. Suppose that a network is divided into two groups; the nodes in the group are fully connected and those of intergroup are not connected at all. Subsequently, it is obvious to separate the network into two groups, and modularity should have the maximum value. Otherwise, if all of the node pairs are randomly connected of a given probability, it would be difficult to separate the network into sub-groups. In this case, the modularity should have a minimum value. The modularity \(Q\) is defined, as following.  
\[Q=\sum_{u=1}^{NC}\left(e_{uu}-a_{u}^{2}\right)=tr(e)-\parallel e^{2}\parallel \tag{13}\]  
where \(NC\) is the number of communities, \(e_{uv}\) is the fraction of edges with one node is in community \(u\) and the other in community \(v\), and \(a_{u}\) is the fraction of edges that are attached to the vertices in community \(u\).
## 5 Experiments and Results
### Methodology
#### 5.2.1 Choosing the Best Embedding for Model A  
We consider the following pre-trained language models for word embedding: FastText, ELMo and BERT (see Section 4.2.4). FastText [64] has been taken from the open repository ([[https://fasttext.cc/docs/en/crawl-vectors.html](https://fasttext.cc/docs/en/crawl-vectors.html)]([https://fasttext.cc/docs/en/crawl-vectors.html](https://fasttext.cc/docs/en/crawl-vectors.html)), accessed on 12 December 2021), where it is available pre-trained on a large body of Russian texts from the CommonCrawl project ([[http://commoncrawl.org/](http://commoncrawl.org/)]([http://commoncrawl.org/](http://commoncrawl.org/)), accessed on 12 December 2021), and then has been pre-trained on reviews from Otzovik.com ([[https://otzovik.com/health/](https://otzovik.com/health/)]([https://otzovik.com/health/](https://otzovik.com/health/)), accessed on 12 December 2021) from the categories \"medicines\" (2,555,833 texts, 15.36 words in a text on average, 39,256,947 words total) and \"hospitals\" (3,290,912 texts, 15.04 words in a text on average, 49,500,274 words total).  
The ELMo model was taken from the DeepPavlov [65] open-source library ([[https://deeppavlov.readthedocs.io/en/master/intro/pretrained_vectors.html](https://deeppavlov.readthedocs.io/en/master/intro/pretrained_vectors.html)]([https://deeppavlov.readthedocs.io/en/master/intro/pretrained_vectors.html](https://deeppavlov.readthedocs.io/en/master/intro/pretrained_vectors.html)), accessed on 12 December 2021), where it is available pre-liminarily trained on the Russian WMT News [66]. The multilingual BERT model, pre-trained on Wikipedia texts, was taken from the Google repository ([[https://github.com/google-research/bert/](https://github.com/google-research/bert/)]([https://github.com/google-research/bert/](https://github.com/google-research/bert/)), accessed on 12 December 2021), and subsequently trained on the above-mentioned drug and hospital reviews.  
Each of these pre-trained models is used as the input to our neural network Model A described in Section 4.3.1. The dataset RDRS-1660 (the first version of our corpus which contains 1660 reviews) is split into 5 folds for cross-validation. On each fold, the training set is split into training and validation sets in the ratio 9:1. Training is performed for a maximum of 70 epochs, with early stopping by the validation loss. Cross entropy is used as the loss function, with nAdam as the optimizer and cyclical learning rate mechanism [67].  
The results of embedding comparison experiments are given in Table 10 and demonstrate the superiority of the ELMo model. BERT leads to lower F1 values with larger deviation ranges, and with the FastText model, the F1 score is the lowest. Combining ELMo with BERT by concatenating their output vectors worsens the accuracy. As a result, we use ELMo in the next section when comparing different input feature combinations.
# Image Classification for Automated Image Cross-Correlation Applications in the Geosciences
## 2 Methods
### Analysis of Shadow Impact in ICC  
Shadow lengths that change in relation to the sun's position during the course of the day can affect the CC results by producing apparent motion. However, their influence has been rarely quantified [13; 16]. For example, [PERSON] et al. [13] observed that changes in illumination can lead to an average CC offset \(\mu>1\)px for images acquired over a time lag of 1-3 h, and also noticed that the correlation coefficient diminishes at a low sun elevation angle.  
In this study, the effects of shadows on the results of the CC are investigated by considering four groups of images that have different illumination conditions: (i) Diffuse illumination; (ii) direct illumination in images acquired at the same hour in the day (12:00), when the sun azimuth and elevation were almost the same; (iii) direct illumination in images taken at different times during the day (i.e., when the sun was in different positions); and (iv) synthetic images produced from an existing digital surface model using a set of shaded reliefs. In the latter, the position of the lighting point was changed, thereby simulating the motion of the sun during the course of a day.  
For each dataset, ICC was conducted for each possible pair of images and 45 CC maps were produced. The mean and standard deviation of the shifts for each map were then analysed, as these are associated with measurement accuracy and precision, respectively.  
The possibility that a shadow's presence could have differing effects on the CC results performed in phase or space domains was investigated, and the same analysis described above was conducted using both DCC and PCC. Results showed no relevant differences between the two techniques.