esa-sceva/satcom-chunk-collection huggingface dataset:
# Entity-Alignment Interaction Model Based on Chinese RoBERTa
## 4 Experiments
### Experimental Results
According to the Hits@1 metric, its performance is 5.4% higher than that of the second-highest performer, BERT-INT, which can be attributed to the fact that (1) as a pre-trained language model, the Chinese RoBERTa that we used is better than the BERT in terms of semantic modeling on the processing datasets; (2) in terms of model input, we used entity name information as input to RoBERTa, while BERT-INT prioritizes entity description information. The MED-BBK-9K datasets used in the experiments was constructed from medical resources and Baidu Encyclopedia, which may not have good descriptive information, while entity name information is something that all entities must have; (3) in a difference from BERT-INT, we introduced the Attention Interaction Module, which captures complex contextual relationships and augmented feature representations and thus improves the performance of entity alignment, as is verified in Section 4.4. Compared with the previous optimal model OntoEA, our model is 3.5% lower in the Hits@5 metrics but 0.021 higher in MRR metrics. We believe the possible reasons are as follows: (1) OntoEA utilizes the ontology information from the knowledge graph, enhances the semantics of the entity representations by jointly learning the entity embedding and the ontology embedding, and introduces the class-conflict-loss module and iterative collaborative-training strategy, which effectively avoids incorrect entity alignment and improves the quality of entity alignment; (2) OntoEA ignores the attribute information associated with entities. In this regard, our model includes the Attribute Interaction Module, which rationally utilizes attribute information; (3) OntoEA relies on manual annotation or automatic alignment systems for ontology part. It relies on high-quality ontologies for datasets, and for datasets with fewer entity categories, the ontology information is less useful and cannot fully utilize the model's advantages. Our model needs only to utilize the existing additional information associated with entities without additional processing in the alignment and does not rely too much on the datasets.
## 3 The Proposed Method
### CNN-Transformer Module
#### 3.2.1 Feature Pyramids Network  
In our method, we use a CNN as a backbone to extract feature maps. Then, we place the rich semantic feature maps \(\mathbf{X}_{0}\in\mathbb{R}^{H_{0}\times W_{0}\times D_{0}}\), where \(D_{0}\) is the number of color channels, \(W_{0}\) represents the width and \(H_{0}\) represents the height. Our FPN is shown in Figure 4. The FPN learns a feature map \(\mathbf{P}^{out}\) from different resolution feature maps \(\mathbf{P}^{in}\), where \(\mathbf{P}^{in}=(\mathbf{P}^{in}_{1},\mathbf{P}^{in}_{2},\ldots)\), and feature map \(\mathbf{P}^{in}_{i}\) is obtained by the encoder at layer \(i\). The size of the feature is \((\text{W},\text{H})\), and the feature map size \(\mathbf{P}^{in}_{i}\) is \((\frac{W}{2^{i}},\frac{h}{2^{i}})\). In the network, we designed this as \(\mathbf{P}^{out}=\text{upsample}_{\times 2}(\mathbf{P}^{in}_{i})+Conv_{i}(\mathbf{P}^{in}_{i -1})\). As described above, \(\mathbf{P}(\mathbf{X}_{0})=1/16\) of the origin. In this case, segmentation cannot be done accurately due to the coarseness of the data. Thus, we obtain upsampled feature maps \(\mathbf{P}^{out}\) by upsampling the **input FPN**.  
When feeding the input to the transformer, due to its characteristics, the spatial resolution of the feature map can be kept unchanged, and thus the output feature map is also \(1/8\) the size of the input images. The problem is that segmentation is not possible at this spatial resolution; therefore, we have to upsample the feature maps and find the **output FPN**. The process is as follows:  
\[\mathbf{P}^{out}=upsample_{\times 2}(\mathbf{P}^{in}_{i})+Conv_{i}(\mathbf{P}^{in}_{i-1}), \tag{3}\]  
\[\mathbf{g}^{out}=upsample_{\times 4}(\mathbf{g}^{in})+Conv_{i}(\mathbf{P}^{out}). \tag{4}\]  
Figure 4: An overview of the CNN-transformer module.
## 3 Methodology
### Constraint-Handling Mechanisms  
In order to make heterogeneous fleets composed of fixed-wing drones and multirotors operational, our optimization procedure considers various constraints in order to make the operations more flexible and efficient.  
Flight capabilities are a key issue since the fixed-wing UAV is designed for large scanning given its speed and endurance, whereas the multirotor UAV is designed to hover over specific critical points for close surveillance. This difference in operation ensures that the fleet is able to comprehensively cover large areas and provide high-resolution monitoring of critical points.  
Energy constraints are also built into decision-making. Drone batteries are continuously monitored, and plans of movement are generated to maximize mission duration. This is carried out by minimizing unnecessary repositioning and not sending drones out of range of operational flight without taking return vectors to charging stations or safe landing zones into account.  
Network flexibility is essential in dynamic and uncertain environments. Real-time reconfiguration capabilities are integrated into the system in order to handle an abrupt loss of signal, new obstacles, or weather changes. Drones adapt their positions dynamically to ensure always-optimal connectivity within the fleet while communications and data exchange continue undisrupted. As a result of reinforcement learning, the system proactively detects and resists interference and keeps the network strong and running in changing conditions.
# Optimizing Assembly in Wiring Boxes Using API Technology for Digital Twin
## 3 Overview of the System Architecture
### Methodology
#### 3.3.2 Tools and Software Used  
* Onshape is a SAS (Software as a Service);
* PyCharm IDE (Python) 3.12.2;
* Ufactory Studio 1.0.1;
* Open CV 4.10.0.84;
* Depth AI 3.5.0.  
The workflow in Figure 2 outlines the key phases of the process for automating the insertion of fuses into wiring boxes, utilizing API technology for digital twin integration and robotic automation. The steps are organized as follows:  
1. Creating the digital twin model using API technology: In this initial phase, a virtual replica of the wiring box assembly is created. The digital twin model is crucial for simulating and monitoring the assembly process in real time. Tools like Onshape are used for 3D modeling, enabling the reverse engineering of the physical wiring box to generate its digital counterpart.
2. Configuring and integrating the Ufactory Lite 6 robot: In this phase, the robotic arm is set up, including the calibration of the custom gripper and the initialization of the arm's position. The precise configuration of the robot is essential to ensure the accurate assembly of fuses. Software like Ufactory Studio is used to configure the robot, while Python is employed to develop control programs that synchronize the robot's movements with the digital twin.
3. Testing and calibrating the system using computer vision and digital twin: After configuring the robot and digital twin, this phase focuses on testing the system's accuracy and efficiency. OpenCV and DepthAI are used for image processing, allowing the robot to leverage computer vision to identify and correctly position the fuses. Calibration ensures that the robot applies the right amount of force during insertion, preventing errors.  
Each of these steps is organized sequentially to progressively develop and refine the automated process, starting with virtual modeling and setup, followed by real-world  
Figure 2: Tools and software: (**a**) digital twins created using Onshape [20], (**b**) our case study.  
integration and testing. These tools are crucial for simulating the workflow, controlling the robotic arm, and ensuring precision during the assembly process.
# A Simplified Query-Only Attention for Encoder-Based Transformer Models
## 3 Materials and Methods
### Dataset  
The proposed method was evaluated with two widely used EEG datasets, BCI competition IV dataset 2a and 2b. Details of the data can be found in paper [30]. The sampling rate was 250 Hz for both datasets. The signals were bandpass filtered between 0.5 and 100 Hz and notch filtered at 50 Hz in both cases.  
Dataset I: BCI competition IV 2a was acquired from nine subjects performing a cue-based motor imagery task with four classes (left hand, right hand, both feet, and tongue). The EEG signals were recorded using 22 Ag/AgCl electrodes. The left mastoid was a reference, and the right mastoid was a ground. Two sessions were conducted on separate days for each subject. Each session included six runs, with 48 trials per run (12 trials per class), resulting in 288 trials per session. Each trial began with a fixation cross on a black screen and a brief auditory cue (t = 0 s), as shown in Figure 1a. After two seconds (t = 2 s), a directional cue (arrow) appeared for 1.25 s, indicating the desired motor imagery. The subjects performed the imagery task until the fixation cross disappeared at t = 6 s. No feedback was provided during the trials.  
Dataset II: BCI competition IV 2b consists of EEG data from nine subjects performing a cue-based motor imagery task with two classes (left and right hand). Three bipolar electrodes (C3, Cz, and C4) were recorded. The left mastoid was a reference, and the Fz electrode was a ground. For each subject, five sessions were recorded. The first two sessions were acquired without feedback, and the last three sessions were measured with online feedback.  
The two sessions without feedback measured six runs, with 20 trials per run (60 trials per class) for each person. A fixation cross was presented on a screen at the beginning of each trial (t = 0 s), as shown in Figure 1b. After two seconds, a beep sound was given (t = 2 s). After three seconds from the beginning (t = 3 s), a directional cue (arrow) appeared for 1.25 s, indicating the desired motor imagery. The subjects were instructed to imagine the corresponding hand movement for 4 s.  
The three sessions with online feedback recorded four runs, with 40 trials per run (80 trials per class) session for each person. A gray smiley was presented on a screen at the beginning (t = 0 s), as shown in Figure 1c. A deep sound was given after two seconds (t = 2 s). The directional cue was presented from 3 s to 7.5 s, instructing the motor imagery.
# Influence of Polypropylene Fiber on Tensile Property of a Cement-Polymer Based Thin Spray-On Liner
## 5 Conclusions  
The addition of polypropylene fiber increased the viscosity of the cement-polymer based TSL slurry distinctly in this study, which may be detrimental to the bubble expelling and therefore the integrity of the specimens. The influence of fiber addition was insignificant on water loss. Consequently, fiber addition could not affect the tensile strength through changing the water loss rate. Examination from the SEM showed that most fibers lying parallel to the loading direction might be pulled out from the matrix or break at the fracture surface, which was beneficial to the tensile strength because of the high stiffness of the fibers. In contrast, these fibers reduced the deformation and were detrimental to the elongation. Meanwhile, an amount of fibers was fully or partly in the fracture surface (that is perpendicular with the load direction), aggravating the propagating of the cracks therefore reducing the elongation at break as well as the tensile strength of the specimens. The cementitious polymer TSL tested in this study was of obvious plasticity judging from the stress-strain curves. The influences of PP fiber varied on different tensile performances of the TSL. With respect to the tensile strength, the enhancement might be dominating, thus the tensile strength was improved at all curing periods by fibers. In contrast, fibers were completely detrimental to the elongation at all curing periods. The influence of fibers on tensile toughness was uncertain at different curing periods, for it was determined by both the stress and deformity of the specimens.  
All of the authors contributed extensively to the work. [PERSON], [PERSON] proposed key ideas. [PERSON], [PERSON] conceived and designed the experiment schemes. [PERSON] conducted the experiment. [PERSON] and [PERSON] analyzed the data. [PERSON] wrote the paper. [PERSON] and [PERSON] modified the manuscript.  
This research was funded by the Fundamental Research Funds for the Central Universities (2017 CXNL01).  
We thank [PERSON], Edanz Editing China (www.liwenbianji.cn/ac), for editing the English text of a draft of this manuscript.  
The authors declare no conflict of interest.  
Figure 14: Failure process of plain TSL. (**a**) Tiny cracks occurred, (**b**) cracks propagated, (**c**) rupture occurred.
## 2 Geological Settings  
Wudun sag is located in the middle of Andun depression in the Dunhuang basin, with the Yumenguan slope in the north, the Sanweishan uplift in the south, Nanhu basin in the West and Tianshuiquan High in the East. It is characterized by a southern fault and a northern uplift in profile. Structurally, it can be divided into three structural units: a northern slope zone, a central depression zone and southern fault zone [3,11] (Figure 1).  
The main structural pattern of Wudun sag was initially formed in the Indonesian period, which experienced a fault\(-\)depression stage under the background of a near\(-\)SN strike slip extension structure in the Yanshan period and a depression stage under the background of overall subsidence in the Himalayan period, forming the current structural pattern [11]. In the Early and Middle Jurassic period, the north\(-\)south depression belt of Sanweishan was formed. Affected by the southern boundary fault activity, the fault depression of Wudun sag was obvious. In the late Jurassic, the eastern part of the Dunhuang basin was simultaneously subjected to the left strike slip of the Altun fault and the continuous compression of North Qilian orogenic belt. Many small fault depressions gradually developed into the whole basin depression, and the nature of the Wudun depression gradually changed from fault\(-\)depression type to depression type. At the end of the late Cretaceous period, the middle Tethys ocean in the southern Dunhuang basin were closed, and the Lhasa block collided with the Eurasian plate, resulting in the uplift of the Altun orogenic belt, resulting in the Cretaceous sedimentary discontinuity of the Dunhuang basin, the overall uplift of the Jurassic formations and severe denudation; In the Neogene, the Tethys residual sea continued to expand, the Dunhuang basin declined as a whole, and the Wudun sag continued to receive sedimentation [11].  
According to the analysis of the structural evolution process, Wudun sag is characterized by an obvious faulted lacustrine basin in the early Jurassic period, and the southern boundary fault controls the development and evolution of the sag.  
The strata in Wudun sag are underdeveloped. The Sinian (PT), Jurassic (J), Neogene (N) and Quaternary (Q) layers are developed from bottom to top. The Jurassic is the main exploration target, and the Dashankou Formation (J\({}_{1}\)d), Zhongjiangou Formation (J\({}_{2}\)z), Xinhe Formation (J\({}_{2}\)x) and Buluo Formation (J\({}_{3}\)b) are also developed. The J\({}_{2}\)z\({}_{1}\) formation contains the most active oil and gas display, mainly exhibiting gray sandy conglomerates and sandstone with medium sorting, along with locally developing mudstone. The mudstone color changes from gray to gray black from bottom to top. Well XC1 and Well D1 obtained low oil production flow, while Well D2 encountered good oil and gas display. Previous studies propose that the J\({}_{2}\)z\({}_{1}\) Formation develops four main sedimentary facies types: braided river, braided river delta, fan delta and lacustrine [3; 4].
## 3 Error Model and Cross-Identification Algorithm
### Geometric Parameter Error Model
#### 3.1.2 Differential Transformation Matrices \(\delta_{m}^{\;0}T\) and \(\delta_{n}^{\;t}T\)  
The pose of the BCF relative to the MCF can be divided into translation \([x,y,z]\) and rotation \([\alpha,\beta,\gamma]\). Its matrix form is shown in Equation (3). When its calibration is not accurate enough, the error between its actual and nominal value is as follows:  
\[d_{m}^{\;0}T=\frac{\partial_{m}^{\;0}T}{\partial x}\Delta x+\frac{\partial_{m }^{\;0}T}{\partial y}\Delta y+\frac{\partial_{m}^{\;0}T}{\partial z}\Delta z+ \frac{\partial_{m}^{\;0}T}{\partial x}\Delta\alpha+\frac{\partial_{m}^{\;0}T }{\partial\beta}\Delta\beta+\frac{\partial_{m}^{\;0}T}{\partial\gamma}\Delta\gamma \tag{12}\]  
where \(\Delta u=[\Delta x,\Delta y,\Delta z,\Delta\alpha,\Delta\beta,\Delta\gamma]\) represents the translation and orientation errors of the BCF relative to the MCF.  
Taking the derivative of Equation (3) with respect to \(x\), we can obtain the following:  
\[\frac{\partial_{m}^{\;0}T}{\partial x}=\left[\begin{array}{cccc}0&0&0&1\\ 0&0&0&0\\ 0&0&0&0\\ 0&0&0&0\end{array}\right]=C_{x}\;^{\;0}T \tag{13}\]  
where  
\[C_{x}=\left[\begin{array}{cccc}0&0&0&1\\ 0&0&0&0\\ 0&0&0&0\\ 0&0&0&0\end{array}\right] \tag{14}\]  
Similarly, by taking the derivative of Equation (3) with respect to \(y,\;z,\;\alpha,\;\beta,\;\gamma\), we can obtain \(C_{y},\;C_{z},\;C_{\alpha},\;C_{\beta},\;C_{\gamma}\), as shown in Table 2. Thus, Equation (12) can be rewritten as follows:  
\[d_{m}^{\;0}T=\left(C_{x}\Delta x+C_{y}\Delta y+C_{z}\Delta z+C_{\alpha}\Delta \alpha+C_{\beta}\Delta\beta+C_{\gamma}\Delta\gamma\right)\cdot_{m}^{\;0}T= \delta_{m}^{\;0}T\cdot_{m}^{\;0}T \tag{15}\]  
Therefore, the differential transformation matrix from the BCF to the MCF is as follows:  
\[\delta_{m}^{\;0}T=\sum_{u}C_{u}\Delta u,\;u\in\{x,y,z,\alpha,\beta,\gamma\} \tag{16}\]  
Similarly, the differential transformation matrix from the TCF to the FCF is similar to \(\delta_{m}^{\;0}T\) and is given by  
\[\delta_{n}^{\;t}T=\sum_{v}C_{v}\Delta v,\;v\in\{x^{\prime},y^{\prime},z^{ \prime},\alpha^{\prime},\beta^{\prime},\gamma^{\prime}\} \tag{17}\]  
where \(\Delta v=[\Delta x^{\prime},\Delta y^{\prime},\Delta z^{\prime},\Delta\alpha^ {\prime},\Delta\beta^{\prime},\Delta\gamma^{\prime}]\) represents the translation and orientation errors of the TCF relative to the FCF.
## 1 Introduction
In [8; 9], sampling and quantification are added to the evaluation framework, which increase the fidelity and sophistication of the performance evaluation simultaneously. The evaluation model is refined in [10; 11], considering the effects of bandlimiting, signal modulation, white Gaussian noise (WGN) and non-white Gaussian interference, which are regarded as key parameters in tracking evaluation. Some of the literature considers the local receiving strategies. Theoretical assessment on tracking and simulation results are provided for Composite BOC (CBOC) in [12]. Tracking jitter under thermal noise in the representation of correlation function is provided in [13]. However, an evaluation method that both supports the various local receiving strategies and decouple from unrelated factors is still lacking in the existing methods. To solve the abovementioned problems, this paper proposes a theoretical code tracking evaluation method considering various receiving strategies, particularly the mismatch of the code chip waveform of the local despreading signal. The paper also provides a decoupled evaluation criterion termed the \"anti-interference rate\". The anti-interference rate, along with equivalent Gabor bandwidth [14], is applied to evaluate modern signals, including MBOC, ACE-BOC, where constructive conclusions are derived for both signal design and receiver configuration. The reminder of the paper is organized as following. In the Section \"Basic Principles of Code Tracking\", the mathematical model of delay locked loop (DLL) for the code tracking of GNSS receiver is formulated. Then, the \"Code Chip Waveform Mismatch\" Section gives a theoretical depiction of the unmatched receiving strategies. Based on the model, in the Section \"Signal evaluation approach under the local despreading waveform mismatch\", the theoretical performance bound of GNSS signal is obtained considering unmatched receiving. Then in the Section \"Decoupled Criteria for GNSS Code Tracking Evaluation\", a series of generalized performance evaluation criteria are derived, which considers the essential factors and decoupled from the less related factors associated with specific implementation and scenarios. Subsequently, in the Sections \"The evaluation of unmatched receiving of ACE-BOC\" and \"The evaluation of BOC-like Receiving of MBOC\", theoretical evaluation is conducted for two modernized GNSS signals, under diverse receiving strategies, from which some useful factsare revealed that will guide the signal design and receiving configuration. Finally, concluding remarks are provided in the Conclusions section.
# Toward Long-Term Implantable Glucose Biosensors for Clinical Use
## 3 Continuous Glucose Monitoring Systems
### Fully-Implantalbe CGMS with Fluorescence Sensors
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Company** & **Medtronic** & & **Dexcom** & & **Abbott** \\ \hline \multirow{3}{*}{**Product**} & **MiniMed 670G** & \multirow{3}{*}{**G4 Platinum**} & **G4 Platinum with SW 505, G5 Mobile** & \multirow{3}{*}{**G6 Mobile**} & **FreeStyle** & **FreeStyle** \\  & **Guardian Sensor** & & & **G5 Mobile** & & **Libre Pro** & **Libre** \\ \hline FDA approval & September 2016 & October 2012 & October 2014 & March 2018 & September 2016 & September 2017 \\ \hline \multirow{3}{*}{Accuracy (MARD \%)} & 10.55\% (abdomen, age 14+) & 13.3\% & \multirow{3}{*}{9\% (age 18+)} & \multirow{3}{*}{9\% (age 18+)} & \multirow{3}{*}{12.1\% (age 18+)} & \multirow{3}{*}{9.7\% (age 18+)} & \multirow{3}{*}{12.1\% (age 18+)} & \multirow{3}{*}{9.7\% (age 18+)} \\  & February 2018 & 17.4\% & 10.4\% (age 2+) & & & (age 18+) (age 18+) \\ \hline \multirow{3}{*}{FDA approval for non-adjunctive device} & No & No & No & Yes & No & Yes \\  & (Class III) & (Class III) & (Class III) & (Class II) & (Class III) \\  & Requires & Requires & Requires & Replaces & (Class II) & (Class III) \\  & fingerstick test for & fingerstick test & fingerstick test & fingerstick & (Class II) & (Class III) \\  & diabetes treatment & for diabetes & for diabetes & for diabetes & for diabetes & (Class IV) & (Class IV) \\  & decisions & treatment & treatment & treatment & treatment & (Professional \\  & decisions & decisions & decisions & decisions & use only) & \\ \hline \multirow{3}{*}{Sensor size} & 9.5 mm long (90 & 12 mm long (45 & 12 mm long (45 & Not & 5 mm long (90 degree insertion) & 5 mm long (90 degree insertion) \\  & degree insertion) & degree insertion) & disclosed & (90 degree insertion) & (90 degree insertion) \\ \hline Calibration frequency per day & Min: 2, (3–4 & \multirow{3}{*}{2 (every 12 h)} & \multirow{3}{*}{2 (every 12 h)} & \multirow{3}{*}{2 (every 12 h)} & \multirow{3}{*}{0 (factory calibration)} & \multirow{3}{*}{0 (factory calibration)} \\  & Recommended) & & & & & \\ \hline Sensor & Glucose oxidase & Glucose oxidase & Glucose oxidase & Glucose oxidase & Glucose oxidase & Glucose oxidase \\ \hline \multirow{3}{*}{Sensor lifespan} & 7 days (including 2 h warm-up) & 7 days & 10 days & \multirow{3}{*}{14 days} & \multirow{3}{*}{10 days (including 2 h warm-up)} & \multirow{3}{*}{14 days} \\  & 2 h warm-up) & (including 2 h warm-up) & & & (including 2 h warm-up) & & 12 h warm-up) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of commercial “continuous glucose monitoring” sensors. still needs two calibrations a day, this type of CGMS can remove weekly sensor self-insertions. Further technical improvement (e.g., enhancing sensor-biostability and production reproducibility) can reduce calibration number to maintain high accuracy (MARD \(<\) 9%).