esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This work aims to investigate the blast-resistant performance hybrid fiber-reinforced concrete panels. A control specimen with plain concrete, PP-fiber, PVA-fiber and Steel fiber reinforcement were prepared and tested. HFRC specimen with PP + Steel fiber, and PVA + Steel fiber were further analyzed with uniaxial compression, 3-point bending tests and contact detonation tests. The comparative analyses offer conclusions as follows:  
1. The significant improvement of compressive strength, flexural strength and blast resistance can be achieved by fiber reinforcement. The fiber reinforcement transfers the mechanical responses from brittleness to ductility.
2. Steel fiber addition can better improve the compressive strength, flexural strength and blast resistance than PP or PVA-fiber counterparts. The SEM test suggests that the steel fiber tends to pull out from the matrix with few cementitious particles adhering.
3. Contact detonation induces both front and rear surface craters. The rear crater dimension tends to be larger than the front crater because of the reflected tensile stress wave, resulting in severer damage on the rear free surface.
4. Compared with Steel FRC control specimen, PP-Steel and PVA-Steel HFRC cannot improve blast-resistant performance except for 1.5% steel + 0.5% PP hybrid fiber reinforcement.
5. For both PP-Steel and PVA-Steel HFRC, the best blast-resistant performance is achieved at around 1.5% steel + 0.5% polymer-fiber hybrid.
6. All the hybrid fiber mixes exhibit negative hybrid effect. PP-fiber has an advantage over PVA-fiber in terms of fiber-hybrid effect on blast-resistance improvement.  
Figure 16: Fiber-hybrid effect for HFRC.  
**Author Contributions:** Data curation, Z.S.; visualization, B.C.; Writing--original draft preparation, W.Y. and J.F.; Writing--review and editing, L.C.; supervision, W.S.; funding acquisition, J.F. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China grant number 11602111 and 11902161. The APC was funded by the Natural Science Foundation of Jiangsu Province (No. BK20170824).  
**Acknowledgments:** The SEM experiments were performed at the Materials Characterization Facility of Nanjing University of Science and Technology. Special thanks are given to [PERSON], [PERSON] for their timely helps in conducting detonation tests.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
(**a**) Different polarization conditions; (**b**) the variation trend of PCE with polarization conditions; (**c**) the variation trend of J\({}_{\text{Sc}}\) with polarization conditions; (**d**) variation trend of Voc with polarization conditions; and (**e**) the variation trend of FF with polarization conditions. explored with present PFM investigation. It can be seen from the PFM image that HDA-BiI5 has good ferroelectric property. When the polarization of positive and negative 10V voltage is applied, the domain has obvious inversion, as shown in Figure S6. Therefore, we could think that it is polarization that improves the performance of the device. The energy of sunlight excites electron hole pairs inside ferroelectrics, and it is the separation and transportation of these electron hole pairs that produce a photocurrent. As shown in Figure 6a, when no voltage polarization is applied to the inverted solar device, the built-in electric field generated mainly by the P-N junction formed at the interface between NiO\({}_{\text{x}}\)/HDA-BiI5 plays a role in the separation and transportation of electron hole pairs. After the addition of voltage polarization, a depolarization electric field is generated in the ferroelectric, and its direction is consistent with the direction of the built-in electric field formed on the heterogeneous interface. The superposition of these two electric fields greatly promotes the separation of light-generated carriers within the light absorption and accelerates the transport of carriers, thus reducing the recombination of carriers. As a result, the photocurrent short-circuit density is greatly enhanced after polarization, and the performance of photovoltaic devices is also significantly improved. Figure 5: The performance parameters of the optimized device vary with the polarization condition. (**a**) Different polarization conditions; (**b**) the variation trend of PCE with polarization conditions; (**c**) the variation trend of Jsc with polarization conditions; (**d**) variation trend of Voc with polarization conditions; (**e**) the variation trend of FF with polarization conditions.
## 3 Results
### Spatial Distribution of Surface Water Quality  
The experimental data for each surface water quality indicator were used in a one-sample Kolmogorov-Smirnov (KS) test before use. The results showed that the experimental data all have a normal logarithmic distribution, except for the DO values from the first in situ determination during the drought period and NH\({}_{3}\)-N values from the second laboratory experiment during the ordinary period. The experimental data were averaged to obtain the values of the surface water quality indicators at each sampling site. IDW was used to interpolate the surface water quality indicators in ArcMap 10.3 (Esri Inc., Redlands, CA, USA) (Figure 6).  
Figure 5: Statistics of landscape indices in buffer zone with different radiuses: (**a**) Largest path index (_LPI_), (**b**) number of patches (_NP_), (**c**) contiguity index (_CONTIG_), (**d**) patch cohesion index (_COHESION_), (**e**) aggregation index (_AI_), and (**f**) landscape division index (_DIVISION_) values of different landscape composition types.  
After this, we used the Kauser-Meyer-Olkin (KMO) test on the five indicators. The results indicate that the KMO value was 0.753 and the correlating factors between any two surface water quality indicators were mostly above 0.3, which meant that factor analysis could be applied. PCA was used after standardizing the experimental data of the surface water quality indicators, which had a variance contribution rate of 85.4% (Table 2). Therefore, two principal components were extracted. We calculated the scores of the two principal components of each sampling site using Equation (4) to represent the general surface water quality in the study area:  
\[F=(42.98\times F_{1}+42.42\times F_{2})/100 \tag{4}\]  
Figure 6: Spatial heterogeneous distribution of surface water quality indicators in the Yi River watershed: (**a**) Chemical oxygen demand (COD), (**b**) ammonia nitrogen (NH\({}_{3}\)–N), (**c**) total phosphorus (TP), (**d**) dissolved oxygen (DO), (**e**) electrical conductivity (EC), and (**f**) the spatial distribution of the score of the comprehensive principal components, which can indicate general surface water quality.  
After this, we used the same method to visualize the spatial distribution of general surface water quality using ArcMap 10.3 (Esri Inc., Redlands, CA, USA) (Figure 6).  
The results indicate that, in general, the surface water quality around Song County is the best. In the southwest, the precipitous landform causes pollutants from the soil and surrounding environment to flow into water bodies, especially in places near Luchuan County. In the northeast, a greater area is covered by urban area and farmland, which are sources of increased pollution that worsen the surface water quality.
## 1 Introduction
[PERSON] and [PERSON] applied the morphologically filtering sparse feature to the matrix decomposition method to improve the accuracy of sparse representation in multi-focus image fusion [20]. [PERSON] and [PERSON] proposed an approximate K-SVD based sparse representation method for multi-focus fusion and exposure fusion to reduce the computation costs of sparse-representation based image fusion [22]. [PERSON] and [PERSON] proposed K-SVD dictionary-learning based sparse representation for the decision map construction of multi-focus fusion [6]. However, these aforementioned sparse-representation based methods do not take the high computation costs into account as K-SVD, and online dictionary learning. In recent years, many researchers have been devoted to speeding up dictionary learning for image fusion. [PERSON] and [PERSON] [23] proposed a joint sparse-representation-based image fusion method. Their method had lower complexity than K-SVD. However, it still required a substantial amount of computations. [PERSON] and [PERSON] [14] proposed a joint-clustering-based dictionary construction method for image fusion. The proposed method used K-means clustering to group the image patches before dictionary learning. The K-means clustering needed a number of cluster centers before clustering. However, in most cases, the number of cluster centers is difficult to estimate accurately before clustering. This paper proposes a novel stochastic coordinate coding (SCC)-based image fusion framework integrated with local density peaks clustering. The proposed multi-focus image fusion framework consists of three steps. First, a local density peaks clustering method is applied in order to cluster image patches. The local density peaks based algorithm increases the accuracy of clustering, and does not need any presetting value for the input image data. Second, an SCC-based dictionary construction approach is proposed. The constructed dictionary not only obtains accurate descriptions of input images, but also dramatically decreases the costs of dictionary learning. Finally, the trained dictionary is used for the sparse representation of image patches, and max-L1 theory is implemented in the image fusion process. The key contributions of this paper can be elaborated as follows:  
1. An integrated sparse representation framework for multi-focus image fusion is proposed that combines the local density peaks based image-patch clustering and stochastic coordinate coding. 2. An SCC-based dictionary construction method is proposed and applied to sparse representation process, which can obtain a more accurate dictionary and decrease the computation cost of dictionary learning. The rest of this paper is structured as follows: Section 2 presents and specifies the proposed framework; Section 3 simulates the proposed solutions and analyzes experiment results; and Section 4 concludes this paper.
## 2 Modeling and Research Methods
### Temporally Topological Measures  
The aggregated temporal network and snapshot-based temporal network are two methods for mapping dynamic temporal networks into static networks. Scholars have proposed numerous concepts and models for studying the topological properties of static networks. These methods can, to some extent, reflect the topological structure of the temporal network and have been extensively employed for various forms of air transportation networks [26; 27; 28; 29]. A novel technique has been introduced for researching the structure of temporal topology, which is based on a static graph. A network is an undirected weighted graph \(G(t)\) with nodes and edges varying across time periods of \(t\in\{1,2,\cdots,t,\cdots\}\). \(V(t)=\{v_{1}(t),v_{2}(t),\cdots,v_{n}(t)\}\) represents the set of nodes in time period \(t\), and each node represents an airport, with a total of \(N=|V(t)|\). \(E(t)=\{e_{1}(t),e_{2}(t),\cdots,e_{m}(t)\}\) represents the set of routes between airports, and there are \(M=|E(t)|\) routes during \(t\). Each edge \(e_{i}(t)\) connects airports \(a\) and \(b\). Thus, an \(N\times N\) adjacency matrix, \(A(t)\), is associated with the undirected weighted graph, in which \(A_{ij}\) is equal to 1 when there is a route between nodes \(i\) and \(j\); otherwise, \(A_{ij}\) will be equal to 0.
# Foot Insole Pressure Distribution during the Golf Swing in Professionals and Amateur Players
## 4 Discussion
### Top Backswing--Left Foot  
At the top of the backswing on the left foot, we have found that the highest pressure occurs in the toes, medial metatarsal (METmed), and medial heel (HEELmed), with the differences between them not being statistically significant. The second level of pressure corresponds to the lateral metatarsal (METlat) and the lateral heel (HEELlat). The lowest significant pressure occurs at the lateral and medial arches (ARClat and ARCmed), with less than 3% of the total pressure. These data coincide with [PERSON] et al. [19], who also found that the highest pressure appearing in the medial metatarsal. Again, it is very interesting to consider the difference between the information obtained from the center of pressure and the pressure distribution in the insole. [PERSON] et al. [10] and [PERSON] et al. [11] report that the center of pressure moves forward during the backswing, reaching the most advanced position in the medial metatarsus of the lead foot at the top of the backswing. However, this information can be confusing for players and coaches because, according to our results and those of [PERSON] et al. [19], the pressure on the medial heel (HEELmed) is similar to that on the medial metatarsal (METmed). Furthermore, it has been found that lower handicap players have higher pressure on the heel of the front foot during the backswing than players with higher handicap [16].  
When comparing the groups of players at the top backswing for the lead foot, we have found a statistically significant difference in the medial metatarsal (METmed), indicating that the professionals have less pressure (Dif \(=-9.6\%\), \(p=0.016\)) than the players with higher handicap (Table 2). This can be interpreted as follows: professionals do not concentrate all the pressure on the medial metatarsal at the top backswing, shifting some pressure to the heels, possibly to anticipate the transmission of the pressure to the lateral part of the foot during the downswing.
# A Review of Current Trends, Techniques, and Challenges in Large Language Models (LLMs)
## 9 Future Directions and Development Trends
### Multimodal LLMs  
LLMs currently primarily deal with large amounts of text data. Research is underway to enhance LLMs with image data. However, integrating diverse data modalities, including text, images, graphics, audio, and videos seamlessly, is required to realize the full potential of LLMs. With the ubiquity of camera-equipped mobile devices, more and more images and videos are produced every day. Some estimate that about 3.7 million new videos are uploaded to YouTube daily. For LLMs to comprehensively understand the content in diverse media, generating content that includes all the relevant elements from diverse media is essential. This is a challenging task and requires groundbreaking research. The current research in this direction includes multimodal preprocessing and feature extraction, fine-grained multimodal representations, spatiotemporal understanding in videos, semantics and contextual understanding, multimodal fusion architectures, cross-modal pretraining and transfer learning, alignment and cross-modal correspondence, real-time multimodal inference, and multimodal pretraining datasets and benchmarks.  
The greatest challenge for realizing multimodal LLMs is in developing effective preprocessing techniques and feature extraction methods specific to each modality. The next stepis to integrate the different modalities within the model architecture. Creating fine-grained multimodal representations involves capturing the complex relationships between diverse modalities. One approach to this is to learn joint/multimodal contextual embeddings. Spatiotemporal understanding in videos involves extracting temporal relationships, detecting motion patterns, and synthesizing spatial information. An LLM's ability to understand semantics and context across diverse modalities is essential for generating contextually relevant multimodal outputs.  
New architectures for LLMs are required to integrate information from multiple modalities. These multimodal fusion architectures require integrating cross-modal embeddings with attention mechanisms. Advances in cross-modal pretraining are required for learning shared representations across modalities. Also, transfer learning from pretrained models is required for better performance on downstream multimodal tasks. Approaches for aligning information across modalities require new research investigations. For example, cross-modal alignment through attention or similarity measures is required to establish the correspondences between elements in different modalities. Some downstream applications require efficient processing of multimodal inputs. For this scenario, real-time multimodal inference is required. Lastly, the availability of curated large-scale multimodal datasets and associated benchmarks for evaluating multimodal models is essential to advance multimodal LLMs.
# Damage Detection Method Based on Continuous Wavelet Transformation of Lamb Wave Signals
## 3 Implementation Using the Difference Signals
### Multiple Damage Scenario  
To test the method for the multiple damage scenario, an additional damage D2 of 12 mm diameter was formed as a through-hole in the model shown in Figure 4. The damage D2 was located at (\(-30,\,50\)) mm from center of the plate, as shown in Figure 16.  
The dynamic response signals for sensing path A8B8 are presented again in Figure 17 for the multiple damage scenario. The undamaged signal shown in Figure 17a only contained the direct excitation signal S\({}_{0}\), whereas the damaged signal shown in Figure 17b contained two additional damage-scattered signals. The damage-scattered peaks are more prominent in the difference signal shown in Figure 17c. The CWT envelopes of difference signal with and without time compensation are shown in Figure 17d. The damage possibilities for sensing path A8B8 are presented in Figure 18 along with the actual size and location of damage D1 and D2. The possibilities of damage existence without time compensation are presented in Figure 18a and with time-compensation in Figure 18b. Similarly, the possibilities from the other seven sensing paths were plotted and fused for damage imaging.  
Figure 16: Multiple damage scenario.  
Figure 15: Time-compensated aggregate damage image for single damage scenario in pulse-echo configuration based on (**a**) compromised fusion scheme; (**b**) conjunctive fusion scheme.  
The aggregate images based on damage index, \(DI_{m}(x_{i},y_{i})\), without time compensation and with time compensation are shown in Figure 19a,b, respectively. The results show the importance of time compensation. The time-compensated signals could accurately image both the damage, whereas the localization of damage without compensation was inaccurate.  
Figure 19: Aggregate damage image for the multiple damage scenario in pulse–echo configuration: (**a**) without time compensation; (**b**) with time compensation.  
Figure 17: Signals for sensing path A8B8 in multiple damage scenario: (**a**) undamaged; (**b**) damaged; (**c**) difference; (**d**) coefficient of CWT with and without time compensation.  
Figure 18: Possibilities of damage existence in multiple damage scenario for sensing path A8B8: (**a**) without time compensation; (**b**) with time compensation.
# A Framework for Diagnosing Urban Rail Train Turn-Back Faults Based on Rules and Algorithms
## 2 Literature Review
### Urban Rail Transit  
Research on urban rail has mainly focused on modeling of the communication-based train control (CBTC) system of urban rail transit communication. [PERSON] and [PERSON] proposed the design of the communication subsystem of the urban rail transit CBTC system, which transmits information to trains through two-way channels in real time to ensure the safety of urban rail transit trains [13]. [PERSON] and [PERSON] also studied the CBTC system of urban rail transit. They reordered the weights of various indicators through fuzzy decision trajectory and evaluation laboratory analysis of the process and the factors' network, finally improving the service quality of urban rail transit [14]. [PERSON] et al. used the deep learning approach to classify the transmission signal of the CBCT system [15]. [PERSON] and [PERSON] studied system information security issues by quantifying CBTC system signals and external attack signals [16]. [PERSON] and [PERSON] analyzed and compared the request to send/clear to send (RTS/CTS) media access mechanisms, noting that they are suitable for signal transmission in the CBTC system [17].  
Furthermore, other scholars have focused on research of the vehicle on-board controller (VOBC), which is one of CTBC's subsystems. [PERSON] et al. proposed a cloud sharing idea for real-time diagnostic data based on the diagnostic data of the VOBC system in urban rail transit, which provided a basis for solving the problem of sharing and analyzing these real-time diagnostic data [18]. [PERSON] et al. developed a hybrid online model-based testing (MBT) platform and tested it with real VOBC data [19].  
The goal of other researchers was to investigate and analyze the passenger volume of urban rail transit. [PERSON] et al. established a traffic flow prediction model using the seasonal autoregressive integrated moving average model (SARIMA) and a support vector machine (SVM). They concluded that the SARIMA-SVM model can fully characterize traffic flow changes and is suitable for the passenger flow prediction of urban rail transit [20]. [PERSON] and [PERSON] used a hybrid logit model to construct an optimization model for collaborative control of urban rail transit network passenger flow, which describes the online distribution of passenger flow in the urban rail transit network [21].  
Our research is based on the above content. The focus of this study is on the analysis and identification of three different kinds of turn-back of urban rail transit trains: automatic end change (AEC), automatic turn-back (ATB), and point mode end change (PEC). We aimed to combine structured data and text data to analyze the return method and the characteristics of the communication code when a failure occurs.