esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Research Purpose and Methodology
### Model Prediction Efficiency Evaluation  
This article compared the prediction results to the actual outcomes to evaluate the performance of the models. An external test dataset was built consisting of multiple different cases. The percentage of the cases whose technology utilization levels were correctly predicted (i.e., correctness ratio) was used as the evaluation metric. In each case, the corresponding industry practitioner was asked the questions about their company background and career profiles, experience with industrialized construction, and utilization level of each technology through the questionnaire and interviews. The background of the selected practitioners should be as diverse as possible to cover different company background and occupation categories. Then, the collected practitioners' company backgrounds and career profiles data were input into the model to obtain the predicted utilization level of each technology type. Finally, the predicted utilization levels were compared with the actual utilization level, to evaluate the accuracy of the model.
## WT1 Immunohistochemical Expression in Human Embryonal/Fetal and Neoplastic Tissues
### Malignant Mesothelialioma  
Malignant mesothelioma is a primary tumor of serosal membranes, including pleura, peritoneum, pericardium and tunica vaginals of the testis. The majority of these tumors arise from pleura followed by peritoneum, and are related to asbestos exposure. Histologically, mesothelioma are classified in three forms: epithelioid, sarcomatoid, or mixed (biphasic). The majority of these tumors are predominantly epithelioid, with tumor cells forming papillae, pseudoacini or solid epithelial nests [70]. These tumor cells show abundant and acidophilic cytoplasm with round nuclei and occasionally prominent nucleoli. Early forms can be difficult to distinguish from reactive mesothelial hyperplasia. Infiltration of deep tissues, obvious cytologic atypia, prominent cell groupings and necrosis favor the diagnosis of malignant mesothelioma [71]. Sarcomatoid mesothelioma [72] is composed of spindle cells with oval nuclei, scant amphiphilic cytoplasm and occasionally prominent nucleoli, arranged in a fascicular pattern, sometimes with a fibrosarcoma-like appearance.  
Epithelialoid mesotheliomas may show a wide variety of morphologies, which can mimic numerous metastatic carcinomas, especially adenocarcinoma of the lung. The correct diagnosis of mesothelioma is usually achieved by the application of a panel of immunohistochemistry, including WT1 antibodies. Early studies on WT1 expression in mesotheliomas were published in 2000, reporting a positivity of 45-75% of the cases using a polyclonal antibody [73; 74]. With the advent of new, commercially-available antibodies (6F-H2), a higher percentage of expression has been reported. Currently, WT1 is considered a highly sensitive and relatively specific marker for distinguishing mesothelioma from lung adenocarcinoma [75]. However, in daily diagnostic practice, WT1 must be used in combination with other, usually positive, markers for mesothelioma (calretinin, Keratin5/6).  
In mesotheliomas, WT1 protein expression is restricted to the nucleus (Figure 3), even if it can be focally observed in the cytoplasm of the cells of sarcomatoid mesothelioma [74; 75; 76; 77]. As normal/  
Figure 2: [PERSON] tumor showing blastematous, epithelial and stromal components (**A**). Blastematous component showing diffuse and strong nuclear WT1 (_N-terminus_) expression (**B**).  
hyperplastic mesothelial cells lining pleura show nuclear staining for WT1 [37], this marker is not helpful in distinguishing benign from malignant lesions. Nuclear WT1 expression in mesothelioma is not surprising if we consider that a similar expression has been documented in embryonal/fetal mesothelial cells covering cavities. The diagnosis of mesothelioma continues to rely on a multimodal approach that incorporates clinical features, gross, microscopic and immunohistochemical features [79].
## 2 BiCA-LSTM-Informer Framework
### Dual-Branch Encoder
#### 2.2.2 Informer Branch  
To address the need for modeling long-range dependencies in time series, the proposed model incorporates an Informer encoder branch based on sparse attention mechanisms, which significantly improves both the efficiency and effectiveness of long-sequence modeling. As a key variant of the Transformer architecture tailored for time series tasks, Informer enables efficient perception and prediction of extended temporal patterns through several structural improvements [11].  
Given an input sequence \(x_{1},x_{2},\ldots,x_{T}\), the Informer first processes it through a sparse attention layer. In contrast to standard dot-product attention:  
\[\text{Attention}(Q,K,V)=\text{softmax}\bigg{(}\frac{QK^{T}}{\sqrt{d_{k}}} \bigg{)}V \tag{8}\]  
where \(Q,K,V\) denote query, key, and value matrices, and \(d_{k}\) is the dimension of the key vector, the Informer employs **ProbSparse Attention**, which reduces computational complexity from \(O(T^{2})\) to \(O(T\log T)\) by selecting only the most informative queries.  
Specifically, ProbSparse Attention identifies top-\(u\) queries based on score variance statistics, where  
\[u=O(\log T) \tag{9}\]  
This strategy focuses on maximizing the variance of attention scores between each Query and all Keys, enabling efficient approximation of the full attention matrix.  
Within the encoder, Informer employs stacked components including multi-head ProbSparse attention layers, feedforward networks (FFNs), residual connections, and layer normalization modules. The multi-head attention mechanism captures features across different subspaces, while FFNs perform local nonlinear transformations. Residual connections and layer normalization together improve training stability and convergence speed.  
After \(n\) stacking layers, the Informer encoder produces global feature representations \(z_{1},z_{2},\ldots,z_{T}\), which are used in downstream modules to facilitate joint modeling of local and global temporal characteristics.  
By incorporating the Informer branch, the model effectively compensates for the limitations of BiLSTM in capturing long-range dependencies. This dual-path architecture enhances the overall modeling capability and improves the quality of temporal feature representation.
# A Through-Transmission Ultrasonic Method for the Detection of Ferrite Tile Defects
## 5 Conclusions  
A water-immersion through-transmission ultrasonic testing method is proposed for the simultaneous detection of internal and external defects in metals and ferrite tiles. The principle of ultrasonic wave transmission is analyzed, and numerical approximations are used to calculate the energy variation of ultrasonic waves propagating through samples containing internal defects, providing a theoretical basis for defect analysis and probe positioning in subsequent simulations and analyses. The simulation results show that the variation of transmitted wave sound pressure can be used to determine the presence of defects and analyze their equivalent size. A measurement system is constructed, and experiments are conducted to further validate the capability of the water-immersion focused ultrasonic transmission method in detecting micro-cracks on ferrite tile surfaces and circular hole defects in processed aluminum alloy samples. Compared with the commonly used pulse-echo method, this approach exhibits a smaller blind zone and higher sensitivity, enabling effective detection of surface and internal defects, and significant potential is demonstrated for various applications.  
However, this method also has some certain limitations, such as the inability to measure the depth of the defect. Further improvement will be carried out as follows: a combined ultrasonic transmission method and traditional pulse echo method to realize full detection of internal and external defects as well as the depth of internal defects.  
Conceptualization, K.H. and Q.L.; Methodology, Q.L. and K.Z.; Software, K.Z.; Formal analysis, Q.L.; Investigation, K.H.; Resources, X.W.; Data curation, B.C.; Writing original draft, K.H.; Writing; Review & Editing, Q.L. and X.L.; Visualization, X.Q.; Supervision, Q.L. and X.W.; Project administration, X.L.; Funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.  
Figure 11: Results of calibration and measurement.  
**Funding:** This research was funded by Tsinghua University Shenzhen International Graduate School Research Start-up Fund, grant number QD2020001N; Tsinghua University Shenzhen International Graduate School Interdisciplinary Research and Innovation Fund, grant number JC2021003.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are available upon request from the corresponding author.  
**Acknowledgments:** The author would like to thank Zhihan Intelligent Manufacturing Co., Ltd. for providing samples for the experiment, and thank [PERSON] for taking X-ray pictures of the magnetic tile samples.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Simulation and Prediction Methods  
### Finite Element Model  
The finite element (FE) model of Hunan Road Bridge was established using the ANSYS program as shown in Figure 5. The hunger forces and cable alignments were set based on the field measured data in the bridge completion state [45; 46]. The Timoshenko beam element (No. 188) in ANSYS was adopted to simulate the tower, crossbeam, and girder. The link element (No. 10) that only considers the tension deformation was used to simulate the cable, hunger, and prestressed tendon. The mass element (No. 21) was adopted to simulate the  
Figure 4: Layout of the structural health monitoring (SHM) system: (**a**) Deformation; (**b**) Internal force; and (**c**) Vibration characteristic (Unit: m). Figure 5: Finite element model: (**a**) Global model; and (**b**) Girder finite element of Beam 188.  
weights of the deck pavement, saddle, clip, approach bridge, and the decorative segment of tower. The beam 188 element with no mass was used to establish the rigid arms connecting the girder and cable, and the tower' top and cable. The compression amounts of the girder and tower were simulated through changing the material temperatures of the rigid arms. The internal forces of the cable, hanger, and prestressed tendon were exerted by defining the initial strains. The bottom sections of towers were completely consolidated. The DOF between the girder, tower, and approach bridge were constrained according to the actual bearing types. The FE model contained 711 nodes and 960 elements, and the static equilibrium analysis required 3 s.  
The simulation results of the girder longitudinal stresses at mid-span section CS5 in bridge completion state are shown in Figure 6, which demonstrates the distributions of the stresses of the top and bottom plate along transverse direction. The stresses of the hotspots were obtained through controlling the section mesh of beam 188 element. In addition, the torsional effects of the extra-wide concrete girder were captured by the beam element adopted, while the shear lag effects were neglected, which may only be caught with a refined FE model using the solid of shell finite element. However, the refined FE model is not suitable for the prediction analysis of long-time concrete S&C effects for such a complicated self-anchored suspension bridge because of the huge computational time that is required. To verify the reliability of the beam FE model, the simulated results of the natural frequencies, deflections, and stresses obtained from the initial equilibrium analysis of the model were compared with the measured data as shown in Table 1. The coincidences show that the beam FE model can accurately realize the actual final state of Hunan Road Bridge. Figure 6: Simulated girder longitudinal stress at mid-span section CS5: (**a**) Top plate; and, (**b**) Bottom plate.
## 2 Requirements of 5G-Based V2X Use Cases
### QoS Requirements
#### 2.1.4 Jitter Requirements  
Jitter is another fundamental component of the QoS. It is a form of packet delay variation and is closely related to latency characteristics. Low jitter can be easily achieved while increasing latency with some form of buffer or shaper. However, the real challenge is to achieve low jitter with a low latency, as many real-time applications require it, including many V2X applications. The 3 GPP standards [28; 29] do not address jitter explicitly in their V2X use case requirements, but the formulation of the latency requirements implicitly includes constraints on jitter as \"Max end-to-end latency (ms)\". Also, there are several C-V2X use cases that require low max end-to-end latency--sub 10 ms--such as sensor information sharing for a high degree of vehicle automation, remote driving, emergency trajectory alignment, and platooning. However, few comprehensive measurement campaigns or research studies focus on the jitter characteristics of C-V2X use cases [18].
## 4 The Proposed TransTLA Method
### Temporal Convolutional Network Layer
#### 4.1.1 Dilated Causal Convolution  
Causal convolutions in TCN maintain temporal causality by using only past and present data, preventing information leakage and enhancing feature extraction capabilities effectively. Traditional CNNs face challenges with increased parameters and overfitting risks as the network depth grows. TCN can overcome the issues by introducing dilated convolutions, which incorporate \"dilation\" into the convolutional kernel by placing gaps between its elements. Therefore, dilated convolutions can expand the receptive field without increasing the parameters. By combining causal convolution with dilated convolution, TCN constructs dilated causal convolution, significantly enhancing the network's ability to understand and model long-term dependencies in time series data. The network structure is shown in Figure 2.  
Mathematically, for a one-dimensional time series input \(X=\{x_{1},x_{2},\cdots,x_{n}\}\in\mathbb{R}^{n}\), the dilated causal convolution operation \(F\) at the element \(s\) in the sequence with a convolutional filter \(f:\{0,1,\cdots,k-1\}\rightarrow\mathbb{R}\) can be represented as  
\[F(s)=(X*_{d}f)(s)=\sum_{k=0}^{K-1}f(k)\cdot x_{s-d\cdot k}, \tag{2}\]  
where \(*_{d}\) denotes the dilated convolution, d refers to the dilation factor, \(K\) is the kernel size, and \(s-d\cdot k\) represents the use of past data. Notably, when \(d\) is set to 1, the dilated  
Figure 2: The structure of the dilated causal convolutional network.  
convolution degenerates into a regular convolution. The dilation factor controls kernel sparsity, allowing the network to integrate information over a wider range. In practice, the dilation factor typically grows exponentially by a factor of 2 with each additional layer in the network.
# On Invertibility of an Interconnected System Composed of Two Dynamic Subsystems
## 5 Numerical Simulations
### Invertibility Checking  
As mentioned above, one of the key points in computing dynamic inverses through system inverses is the reversibility of systems. The above discussed algebraic criteria were employed. Then, the system inputs can be expressed as a function of the output and its derivative. In order to test the invertibility of the interconnected systems modeled by (25) and (26), the output differential rank of each subsystem is required to equal the number of the inputs.  
First, the invertibility of the high-level subsystem should be checked. As shown in (25), there are two inputs, \(u_{L1},u_{L2}\), representing the fluid flow rates \(Q_{H}\), \(Q_{C}\) of both fluids, respectively. In order to verify the invertibility characteristics, the output differential rank should be equal to the input number 2. To achieve this purpose, the explicit expressions of input and output \(y_{H}\) are derived by calculating the derivative \(y_{H}\). Corresponding to (26), there are two outputs, \(y_{H1},y_{H2}\), representing the temperature of both fluids \(T_{H}\) and \(T_{C}\), differentiating all two outputs, the following equation is obtained:  
\[\left\{\begin{array}{l}\dot{\mathrm{y}}_{\mathrm{H1}}=\frac{\mathrm{h}_{ \mathrm{H}}\mathrm{A}}{\mathrm{p}_{H}\mathrm{c}_{\mathrm{p}_{H}}\mathrm{v}_{ \mathrm{H}}}\mathrm{v}_{\mathrm{H}}\left(\mathrm{y}_{\mathrm{H2}}-\mathrm{y}_ {\mathrm{H1}}\right)+\frac{\mathrm{u}_{\mathrm{L1}}}{\mathrm{v}_{\mathrm{H}} }\left(\mathrm{T}_{\mathrm{H}}^{\mathrm{in}}-\mathrm{y}_{\mathrm{H1}}\right)\\ \dot{\mathrm{y}}_{\mathrm{H2}}=\frac{\mathrm{h}_{\mathrm{H}}\mathrm{A}}{ \mathrm{p}_{C}\mathrm{c}_{\mathrm{P}\mathrm{C}}\mathrm{c}}\left(\mathrm{y}_{ \mathrm{H1}}-\mathrm{y}_{\mathrm{H2}}\right)+\frac{\mathrm{u}_{\mathrm{J}}}{ \mathrm{v}_{\mathrm{C}}}\left(\mathrm{T}_{\mathrm{C}}^{\mathrm{in}}-\mathrm{y }_{\mathrm{H2}}\right)\end{array}\right. \tag{27}\]  
From Equation (27), all the output differential equations are dependent on states \(x_{H}\) and inputs \(u_{L}\), thus there are \(r=0\) independent relations, outputs number \(p\) is 2, then the output differential rank is as follows:  
\[\rho=p-r=2 \tag{28}\]  
According to Theorem 3, the invertibility of the high-level subsystem can be verified.  
After that, the invertibility of the low-level subsystem should be checked. There are two local inputs \(u_{1}\), \(u_{2}\), in (25), representing the pneumatic pressure \(p_{c1}\), \(p_{c2}\) of both fluids. By differentiating the two outputs \(u_{L1}\), \(u_{L2}\) and finding all possible independent relations, it is easy to check that the low-level subsystem is invertible.
## 3 Design of the Learning System and Content Structure
### Answering Process  
The answering process of this system is designed to guide students to master _Verilog_ syntax and digital circuit design concepts through a step-by-step learning mechanism. Students will learn and take the test through to the following steps:  
1. Concept Acquisition: Students first browse the Reference Material Section and learn the background knowledge, sample codes, or digital circuits and timing waveforms related to the question to grasp the core concepts covered in the question. This stage aims to help students establish basic cognition and lay the foundation for subsequent program reading and problem-solving.
2. Code Analysis: After understanding the basic concepts, students move to the Source Code Section to observe and analyze the provided _Verilog_ code. In some questions, the code will include fill-in-the-blank sections, requiring students to infer the missing program fragments to further deepen their understanding of _Verilog_ syntax and circuit operation logic.
3. Answer and Submission: Students fill in their answers in the Answer Section. After completing all questions, they click the \"Answer\" button to submit their answers, as shown in Figure 2. The system will automatically compare the answers entered by students with the correct answers, which are stored in the system in the background, and output the accuracy rate in real time. If there are wrong answers, the system will mark them in red to help students identify and correct them.
4. Iterative Learning and Mastery: Students can make multiple attempts until all answers are correct. This system provides instant feedback and correction opportunities, helping students consolidate their learning through repeated practice and ensuring a complete understanding of _Verilog_ syntax and digital circuit concepts.
5. Section Score and Tracking: When students have completed all questions in a section, they can enter their personal ID to record their learning outcomes, and the system will output the student's final answers for the section, as shown in Figure 3. This  
Figure 1: User interface for Problem 1.  
mechanism can be used for students' self-assessment and can also serve as a reference for teachers to monitor learning progress and effectiveness.  
Through the above design, this system provides an interactive and autonomous learning environment. Through conceptual understanding, program analysis, real-time evaluations, and multiple attempts, it will assist in students' mastery of _Verilog_ syntax and digital logic design, and cultivate their program reasoning and error correction capabilities, achieving the best learning effect.
# Energy and Delay Aware Data Aggregation in Routing Protocol for Internet of Things
## 2 Related Works
It proposed cooperative RPL (C-RPL), which creates multiple instances based on the cooperative strategy. The simulation has been conducted in the Contiki Cooja simulator. The C-RPL provides better performance by means of energy consumption and the cost of the nodes in the networks. However, it takes additional time to choose the parent node present in the multiple DODAG. [PERSON] and [PERSON] [27] proposed a fuzzy set based cluster routing (FC-RPL) protocol for IoT. The FC-RPL consists of three phases, namely cluster formation, CH selection, and CH parent selection. The cluster is formed from the Euclidean distance. The CH selection is performed by considering the fuzzy logic over the routing metrics residual energy, centrality, and nearest neighbor node. The CH parent node is selected from the CH parent rank. The simulation has been conductedusing the Contiki COOJA simulator. The performance of FC-RPL is compared to RPL. The proposed FC-RPL extends the network lifetime. However, it forms an excess number of clusters in the network. [PERSON] et al. [28] proposed a cluster-based routing (MUCRR-RPL) protocol for LLN. The MUCBR protocol divides the entire network into multiple clusters. In each cluster, the MUCBR selects the cluster head based on the residual energy. The CH node collects the data packets from cluster members and performs the data aggregation. Finally, the aggregated data is forwarded to the Sink node. The simulation has been conducted using the COOJA simulator. The performance of MUCBR-RPL provides better performance in terms of network lifetime and reliability. However, initially, it takes time to form the cluster. The literature review of Data aggregation on routing protocols is given in Table 1.