esa-sceva/satcom-chunk-collection huggingface dataset:
# Form-Finding Analysis of the Rail Cable Shifting System of Long-Span Suspension Bridges
## 5 Conclusions  
With the application of the basic assumptions of flexible cables, an analytical form-finding analysis model of the rail cable shifting system is proposed. The rail cable and main cable are seen as segmental linear cable elements and catenary elements, respectively. The elastic elongation of the sling is considered to determine the analytical equation of each member and the iterative solutions. Besides the experimental data of the girder construction of the Aizhai suspension bridge, the compared results verify the accuracy and effectiveness of the presented method. The main conclusions are drawn as follows.  
1. The global mechanical analytical model for the main cable, slings, and rail cables of the RCS system under the non-loading condition is established through the theoretical derivation. The equilibrium equation established from the position after structural deformation can be used to perform the geometrical nonlinear analysis of the structural large deformation. The simplified calculation and analysis process of the RCS system achieve sufficient accuracy, which thus can be applied in engineering.
2. Under the condition that the initial geometric state of the RCS system is unknown, the true internal force and geometry of the rail cable can be iteratively computed by assuming the initial force or configuration of the rail cable.
3. The designed rail cable tensioning and testing device meets the requirements of the model test. The measured results of the main cable line shape and main cable force in the model test are in good agreement with the theoretical results.  
Q.P.: Conceptualization, Data curation, Formal analysis; D.Y.: Funding Acquisition; Z.Y.: Software, Writing-Review and Editing.  
The National Natural Science Foundation of China (Grant no. 51678069); the Key Discipline Fund Project of Civil Engineering of Changsha University of Sciences and Technology (Grant no. 15 KB03).  
The model test of Aizhai suspension bridge is performed in the Changsha University of Science and Technology, the workplace supported by the key laboratory for safety control of bridge engineering, ministry of education oh Hunan province is acknowledged.  
The authors declared that they have no conflicts of interest to this work.
## 2 Related Works
### The NER Model
#### 3.1.2 The Bi-LSTM-CRF Model  
In this paper, we apply Bi-LSTM to carry out sequence encoding. Bi-LSTM utilizes a bidirectional LSTM network, which can take full advantage of the long-term dependencies from both input directions. The LSTM network is composed of several memory cells (Figure 3).  
The memory cell is implemented as follows:  
\[\begin{split} i_{t}&=\sigma(W_{i}\cdot[h_{t-1},X_{ t}]+b_{i})\\ f_{t}&=\sigma(W_{f}\cdot[h_{t-1},X_{t}]+b_{f})\\ c_{t}&=f_{t}*c_{t-1}+i_{t}*tanh(W_{c}\cdot[h_{t-1},X _{t}]+b_{c})\\ o_{t}&=\sigma(W_{o}\cdot[h_{t-1},X_{t}]+b_{o})\\ h_{t}&=o_{t}*tanh(c_{t})\end{split} \tag{4}\]  
where \(i,f,c\) and \(o\) are input gate, forget gate, cell state and output gate; \(\sigma\) is the sigmoid function. \(W\) and \(b\) is the parameter matrix; \(h_{t}\) is the hidden state vector. The output of each LSTM layer can be represented as \(H=\{h_{t}|t=1,2,3.\ldots.,n\}\); \(n\) is the length of the input sequence. The hidden state sequences of forward LSTM and backward LSTM are concatenated to form the final sequence representation \(H=[\overrightarrow{H_{f}},\overleftarrow{H_{b}}]\)[41].  
For the last layer of the NER model, we add a CRF (Conditional Random Field) layer to perform label prediction of the input sequence. CRF combines the characteristics of MEMM and HMM [42]. It is a typical sequence labeling algorithm. In this experiment, the Bi-LSTM layer will process the character representation of the input sequence generated by the SoftLexicon method, and the CRF layer will predict the label according to the output of the Bi-LSTM layer.  
Figure 3: The general structure of the LSTM memory cell.
# Evaluation of Radio Access Protocols for V2X in 6G Scenario-Based Models
###### Abstract  
The expansion of mobile connectivity with the arrival of 6G paves the way for the new Internet of Verticals (6G-IoV), benefiting autonomous driving. This article highlights the importance of vehicle-to-everything (V2X) and vehicle-to-vehicle (V2V) communication in improving road safety. Current technologies such as IEEE 802.11p and LTE-V2X are being improved, while new radio access technologies promise more reliable, lower-latency communications. Moreover, 3 GPP is developing NR-V2X to improve the performance of communications between vehicles, while IEEE proposes the 802.11 bd protocol, aiming for the greater interoperability and detection of transmissions between vehicles. Both new protocols are being developed and improved to make autonomous driving more efficient. This study analyzes and compares the performance of the protocols mentioned, namely 802.11p, 802.11 bd, LTE-V2X, and NR-V2X. The contribution of this study is to identify the most suitable protocol that meets the requirements of V2V communications in autonomous driving. The relevance of V2V communication has driven intense research in the scientific community. Among the various applications of V2V communication are Cooperative Awareness, V2V Unicast Exchange, and V2V Decentralized Environmental Notification, among others. To this end, the performance of the Link Layer of these protocols is evaluated and compared. Based on the analysis of the results, it can be concluded that NR-V2X outperforms IEEE 802.11 bd in terms of transmission latency (L) and data rate (DR). In terms of the packet error rate (PER), it is shown that both LTE-V2X and NR-V2X exhibit a lower PER compared to IEEE protocols, especially as the distance between the vehicles increases. This advantage becomes even more significant in scenarios with greater congestion and network interference.  
6G; V2X; URLLC; IEEE 802.11p; IEEE 802.11 bd; LTE-V2X; NR-V2X Article
## 2 Materials and Methodology
### Fracture Mechanics Analysis under Monotonic and Cyclic Loading Conditions  
#### 2.3.1 Under Monotonic Loading Conditions  
Within the framework of linear elasticity, under monotonic loading conditions, to quantify the severity of a crack, the stress intensity factor \(K\) was used. This parameter was estimated as follows:  
\[K=Fs\sqrt{\pi a}\ ;\ [K]=\text{MPa}\sqrt{\text{m}} \tag{1}\]  
where \(s\) is the nominal stress applied on the structure, \(a\) is the crack length, \(F\) is a coefficient related with the specimen geometry and loading conditions. There are three different modes of cracking--Mode I-opening, Mode II-shearing, and Mode III-tearing. Since the cortical bone shows the least resistance to fracture under the Mode I loading conditions, this loading mode received the most attention in the literature [16].  
Under the elastic regime [30], the energy release rate (denoted by \(G\)) was the energy associated with the creation of a unit crack surface. This energy was estimated as follows:  
\[G=-\frac{\partial W}{\partial A};\ [G]=\text{Jm}^{-2} \tag{2}\]  
where \(W\) is the potential energy, and \(A\) is the crack area. For an isotropic linear elastic material and when the crack grows straight ahead, the energy release rate related to the stress intensity factors by  
\[G=\frac{1}{E^{\prime}}\left(K_{I}^{2}+K_{II}^{2}\right)+\frac{1+\
u}{E}K_{III} ^{2} \tag{3}\]  
where \(K_{I}\), \(K_{II}\), and \(K_{III}\) are the stress intensity factors, respectively, for the Mode I, Mode II, and Mode III, and  
\[E^{\prime}=\left\{\begin{array}{c}E\ Plane\ stress\ state\\ \frac{E}{1-\
u^{2}}\ Plane\ strain\ state\ and\ 3D\end{array}\right. \tag{4}\]  
Under monotonic loading conditions, a crack was assumed to propagate instantly when \(K\) or \(G\) reached a critical value denoted as \(K_{Ic}\) or \(G_{c}\), respectively, which define the fracture toughness--a characteristics of the material to resist crack propagation. The critical values for human tibia bone, i.e., \(K_{Ic}\), \(G_{c}\), respectively, in the ranges of 2.2 \(\div\) 5.7 (MPa\(\sqrt{\text{m}}\)) and 350 \(\div\) 900 (Jm\({}^{-2}\)) as reported by [PERSON] et al. [16].
# An Approach to Incorporating Implicit Knowledge in Object Detection Models
## 4 Experiment
### Datasets and Evaluation Metrics
#### 4.1.2 Evaluation Metrics  
In the experiments, precision (P), recall (R), average precision (AP), and mean average accuracy (mAP) were employed as performance evaluation metrics. Precision is defined as the ratio of the number of true positive samples to the total number of positive samples predicted, serving to assess the model's accuracy. Precision (\(P\)) can be computed using the following formula:  
\[P=\frac{TP}{TP+FP} \tag{10}\]  
where \(TP\) represents the true positive cases where the actual condition is positive and the predicted condition is also positive, and \(FP\) represents the false positive cases where the actual condition is negative, but the predicted condition is positive. Recall, also known as sensitivity, is defined as the ratio of the number of true positive samples to the total number  
Figure 5: UA-DETRAC dataset. (**a**) Example images; (**b**) the distribution of labels in the dataset.  
Figure 6: KITTI dataset. (**a**) Example images; (**b**) the distribution of labels in the dataset.  
of positive samples in the ground truth, which is used to evaluate the comprehensiveness of the model's detection. \(R\) can be calculated using the following formula:  
\[R=\frac{TP}{TP+FN} \tag{11}\]  
where \(FN\) represents the false negative cases where the actual condition is positive but the predicted condition is negative. Average precision (\(AP\)) is defined as the area enclosed by precision, recall, and axis. \(AP\) can be calculated using the following formula:  
\[AP=\int_{0}^{1}P(x)dx \tag{12}\]  
where \(P(x)\) in Formula (11) represents the smoothed precision-recall curve.  
To calculate the mean average precision \(mAP\), sum the \(AP\) values for each class and divide by the number of classes, as follows:  
\[mAP=\frac{\sum_{i=1}^{n}AP_{i}}{n} \tag{13}\]  
In the formula, \(n\) represents the number of classes and \(AP_{i}\) represents the average precision for the \(i\)-th class.  
In the experiments, mAP50 and mAP are adopted as evaluation metrics to assess the effectiveness of the model. mAP50 calculates the average AP at IoU [24] = 0.5. mAP is computed across IoU values ranging from 0.5 to 0.95, with a step size of 0.05, and mAP is calculated at these IoU thresholds.
## 2 Datasets and Methods
### Analysis of LST Variations between and within LCZ
#### 2.5.2 LST Variations within LCZs  
The quantitative relationship between LST and surface properties was also determined using DEM retrievals (Elevation and Aspect) and five land cover indices (MNDWI, NDBaI, NDBI, NDVI, and UI). Using the same 1000 random points per LCZ, additional values were extracted from the seven layers. The correlation between LST and each of the factors was determined for each LCZ category. The strengths of the correlations were used to identify the major causes of LST variations within an LST, while \(p\)-values were used to assess the statistical significance of each effect. Analysis was also used to determine if the effects of these factors changed from one LCZ type to another. This was necessary in order to identify the type of development or adjustments needed to avoid excessive LST in each LCZ.
# Iterative Decoding of LDPC-Based Product Codes and FPGA-Based Performance Evaluation
## 4 Hardware Evaluation for Iterative Decoding of Product Codes
### Hardware Evaluation Platform
#### 4.1.2 The Decoder Architecture  
For our proposed iterative scheme, the prior information and the intermediate information bits after LDPC decoding should be updated. Thus, we use two groups of block RAM for storing these information for global iterations.  
Specifically, as shown in Figure 11, the decoder is composed of 162 block RAMs, six parallel LDPC decoders and nine parallel outer decoders. These block RAMs are used to store the referred two parts of information. Some RAMs are for the prior probability imformation storage. We use 108 block RAMs to store the prior probability imformation, each of which has the word width of \(Q\) bits and the depth of \(n_{1}/9\times k_{2}/6\). Other 54 block RAMs store the intermediate decoding results of LDPC codes during the global iterations. These block RAMs are organized in the same way as those in the encoder.  
The specific iterative decoding process is presented as follows.  
Step 1: Update \(n_{2}\) prior probability information for each LDPC codeword according to the received channel observation values.  
Step 2: Perform the inner decoding using multiple parallel LDPC decoders and store the intermediate bits in decoding results in 54 block RAMs.  
Step 3: Perform error-trapping decoding using nine parallel outer decoders.  
Step 4: Update the prior probability information according to the intermediate outer decoding results until the maximum number of iterations has been reached.  
Step 5: Output the results of outer decoders in the final iterative round. Otherwise, update the prior probability information and go to step 2.  
Figure 11: The decoder architecture of product codes.
## 2 Methods
### Assessment and Analysis Method  
#### 2.3.1 Design Rainfall  
The total yearly runoff of several design scales (60%, 70%, 75%, 80%, 85%, 90%) was regulated using 0.5-year, 1-year, 3-year, 5-year, and 10-year return period 2 h rainfall data. The simulation results are processed using the control ratio idea, and the regional model  
Figure 3: Runoff volume capture measures: (**a**) Source Facilities, (**b**) Terminal Storage Tank.  
of various control facilities is simulated. The current Chinese standard is used in rainfall intensity calculation [28]. The local rainfall intensity can be calculated as follows:  
\[\text{i}=\frac{12.004(1+0.81 lgP)}{(t+8)^{0.711}},\ P\leq 10,\ t\leq 120\,\text{min} \tag{1}\]  
\[\text{i}=\frac{8.265(1+1.047 lgP)}{(t+8)^{0.642}},\ P>10,\ t\leq 120\,\text{min} \tag{2}\]  
\[\text{i}=\frac{13.878(1+1.091 lgP)}{(t+10)^{0.759}},\ P\leq 10,\ t>120\,\text{min} \tag{3}\]  
\[\text{i}=\frac{11.479(1+1.321 lgP)}{(t+10)^{0.744}},\ P>10,\ t>120\,\text{min} \tag{4}\]  
where i is the intensity of the rainfall (mm/min), \(P\) is the return period (years), and \(t\) is the duration of the rainfall (h).  
The \"Guidelines for the Construction of Sponge City\" has set divisions for the total annual runoff control rate, which are I: 85% to 90%, II: 80% to 85% III: 75% to 85%, IV: 70% to 85%, V: 60-85% [29]. As a consequence, the primary control scales used in this study were 60%, 70%, 75%, 80%, 85%, 90%. At the same time, different rainfall intensities were set in order to evaluate the control impact of sponge city amenities on runoff pollution under varied rainfall intensities. Due to the obvious relative research and real rainfall frequency, 0.5 years, 1 year, 3 years, 5 years, and 10 years were chosen.
# Experimental Evaluation of Display Field Communication Based on Machine Learning and Modern Design
## 4 Laboratory Experimentation of the Proposed DFC Scheme  
In this section, we describe a laboratory experiment of the proposed DFC scheme and evaluate its performance under channel coding. Table 1 shows the default specification of the experimental parameters. For the experiments, we use the [PERSON] image because it is a standard and widely accepted test image used in the field of image processing. In addition, from a compositional standpoint, [PERSON] in particular was an ideal image: feathers in her hat provided great detail, a human face provided a recognizable subject, and a variety of gradations and textures proved to be a useful challenge for processing algorithms. In the experiment, a \(256\times 256\) pixel color [PERSON] image was displayed on the Samsung monitor of resolution \(1920\times 1080\) pixels. Then, the display was captured at a distance of 28 cm with an iPhone camera at 1080p (Full HD) using the 12-megapixel main camera. Moreover, the experiment was performed indoors under the ambient LED lighting condition including natural light. While capturing the display, no focal distortion is observed as all the display pixels were in the focus of the camera pixels. Furthermore, since the frame rate of the camera is the same as the refresh rate of the display, the problem of image quality distortion due to the rolling shutter effect [33] does not occur. For data transmission, 500 random binary data bits were considered and after the channel coding, 2518 data bits were finally embedded in the input image. In the case where we do not use channel coding, a total of 2560 binary data bits were embedded into the input image. Additionally, the data are embedded in the lower sub-band, i.e., from rows 5 to 15 of the input image. Besides, no camera angle or image rotation is considered. For details, refer to Table 2.
# Digital Twin in the Maritime Domain: A Review and Emerging Trends
## 6 Scientometric Analysis of Digital Twin in the Maritime Domain
### Methodology and Tools  
It is thoroughly synthesized to obtain the exact outcome of the dataset derived from the large database obtained from Web of Science (WoS) [122] and refined the scientometric indicators to obtain the intended research data. In this study, the WoS inbuilt analytical tools were also used to provide a basic overview of the selected database of past research with a graph-based visualization. To obtain the visual analytics of the data set, computation tools of CiteSpace and VOSviewer are used in understanding how DT has evolved over the past passing intellectual turning points that are evident along the critical path, and the topics have attracted attention, thus synthesizing the development of DT closely and extensively [123; 124].  
Using the two analytical software, the database obtained from WoS is analyzed to generate the outcomes such as top authors, articles, journals with emerging literature, development, the impact of collaboration and intellectual dynamics to understand the vast subject area of DT in the maritime domain (Figure 9). In this study, research publications available in WoS Core Collection were considered from the years 2002 to 2022 (20 Years) as the DT concept was initially brought out in the year 2002. This comprehensive database includes all dominant journals and publications including IEEE Explore, MDPI, Elsevier, Springer, Emerald, Wiley online library, Taylor & Francis, etc.  
Keywords were selected based on the target data set using required Booleans as stated below. The search was performed in WoS Core collection targeting the documents with the search equation of  
\[\text{TS}= \{(\text{DigitalTwinORDigitalizationORDigitalThreadORDigitalImage})\text{AND}\] (MarineORMaritimeORShippingOROffshoreORSeaOROceanOR \[\text{PortORHarbour})\}\]  
Search results provided 955 multilingual publications related to the above-narrowed research area targeting the exact timespan (Table 1).  
Based on the search results, visualizations of (1) Publication History Analysis, (2) Countries/Region Analysis, (3) Authors Contribution Analysis, (4) Cited Authors Analysis, and (5) Keyword Analysis were carried out using the analytical tools available in WoS, VOSviewer and CiteSpace. Respective analysis data and logical discussion against each step of the scientometric analysis process are described below.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sr No** & **Document Type** & **Record Count** & **Percentage \%** \\ \hline
1 & Articles & 650 & 79.075 \\
2 & Meeting & 234 & 28.467 \\
3 & Other & 32 & 3.893 \\
4 & Reviews & 26 & 3.163 \\
5 & Early Access & 13 & 1.582 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Documents Extracted in Search Results of WoS Dataset.  
Figure 9: The Flowchart Demonstrating the Adopted Scientometric Analytical Process.
## 1 Introduction
[PERSON] et al. [14] proposed a new deep migration learning method based on Wasserstein distance that minimizes the distribution distance between the source domain and target domain through confrontation training. [PERSON] et al. [15] introduced the multi-layer MMD method to transfer laboratory bearing-fault diagnosis knowledge to the actual bearing environment and to transfer knowledge learning using different equipment. [PERSON] et al. [16] determined fault diagnosis in the thermal system of a power plant under variable operating conditions by introducing CORAL (COR relation alignment) loss to reduce the difference in the characteristic distribution of the thermal system under different operating conditions. Most of the above research is based on the problem of variable-condition fault diagnosis using only single-source domain data. In practical applications, the source domain data may come from a variety of different working conditions [17; 18]. Reference [19] indicates that some combinations of single-source domains can provide more information. [PERSON] et al. [20] proposed a cross-domain classification algorithm, and selected the MMD method to model domain adapters from multiple sources. [PERSON] [21] proposed the FTD-MSDA (feature-level and task-specific distribution alignment multi-source domain adaptation) framework, which uses MMD loss to realize fault diagnosis of rotating machinery. The measurement method of the above research only considers the differences between different classes in two domains, and loses the fine-grained information of each class. In order to make up for the above shortcomings and more effectively transfer diagnostic knowledge from multiple source domains to the target domain, this paper proposes a new multi-source deep subdomain adaptive transfer learning method. The research in this paper makes two contributions in methodology. (1) This paper proposes a deep multisource variable condition bearing fault diagnosis method based on sub-domain adaptation. The local maximum mean difference is used to fully learn the multi-source domain fault feature information to solve the problem of fine-grained information loss. (2) The Ranger optimization algorithm is introduced to optimize the network parameters, which makes the network training more stable while maintaining the accuracy.