esa-sceva/satcom-chunk-collection huggingface dataset:
# STOD: Towards Scalable Task-Oriented Dialogue System on MultiWOZ-API
## 5 Experiment and Analysis
### Cross-Domain Dialogue Modeling
#### 5.2.1 Experiment Setting  
Dataset  
In order to evaluate the cross-domain scalable task-oriented dialogue model CSTOD, we extract five single-domain data including attraction, hotel, restaurant, taxi, and train from the MultiWOZ dataset for cross-domain evaluation.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Precision** & **Recall** & **F1** \\ \hline Whether Call API & 86.45 & 100 & 92.73 \\ API Correctness & 84.45 & 85.13 & 84.79 \\ Args Accurateness & 78.13 & 56.00 & 65.24 \\ \hline \hline \end{tabular}
\end{table}
Table 4: API query accuracy evaluation.  
Figure 3: (**a**) The ratio of API call prediction error types. (**b**) The ratio of API query parameters prediction error types of different API types.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Prev** & **Cur** & **Inf.** & **Suc.** & **BLEU** & **Comb.** \\ \hline Gen & Gen & 89 & 78.6 & 18.4 & 102.2 \\ GT & Gen & 91.7 & **80.5** & 19.5 & 105.6 \\ GT & GT & **92.2** & 79.1 & **20.8** & **106.4** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Different API information evaluation result.  
The evaluation metrics for the cross-domain task-oriented dialogue are the same as for the multi-domain task-oriented dialogue. For cross-domain task-oriented dialogue, we compare our CSTOD with several baseline models for cross-domain evaluation, mainly, DAMD [26], MinTL [28], UBAR [29], BORT [14], and UBARv2 [15].
# A Security Framework for the Internet of Things in the Future Internet Architecture
## 6 Delegation-Based Key Provisioning Protocol
### Use Cases  
The delegation-based key provisioning protocol is not only a lightweight membership enrolment protocol specified for the MobilityFirst-based IoT architecture, but can also serve as the cryptographic core of general-purpose key provisioning, where established keys may represent various trust relationships with different scope, semantics and criticality/impact.  
Delegation of key provisioning in accordance with our protocol assumptions can be applied to a wide variety of use cases. As an example, the delegation techniques can also be used for a special function delegation. For instance, a symmetric attestation scheme may involve the Parent as an established Verifier to delegate the capability for the Guardian (a new Verifier) to challenge and verify the Prover (Child) using a symmetric attestation key. Embodiments may further support provisioning an additional Verifier (Guardian) for the same Prover. In such cases, a fresh and unique key may be established between the Prover and the new Verifier, with the original Verifier's authorization and delegation. A device provisioning function that establishes a new root of trust key for a device could also use the above protocol.  
Another example can be applied to an ownership transfer protocol, which allows the original owner (Parent) to transfer all its privileges on the target device (Child) to the new owner (Guardian). In this case, a new ownership key is established on the Child, and replaces the old ownership key as the root of trust.  
To summarize, with respect to key management, different keys have different usage purposes and thus key semantics are essential to clarifying any ambiguity. Our delegation-based key provisioning protocol can work with various key semantics and provide a lightweight approach to establish key material for IoT systems.
## 1 Introduction
The RT-ETDE framework consists of a set of lightweight schemes for ETDE that rely on intelligent point cloud partition, feature extraction, and simple geometric solutions based on the characteristics of the point cloud in elbows and T-junctions. We present a linear-time sampling scheme for the camera pose and straight pipe diameter estimation. The elbow and T-junction are detected by evaluating the coverage of the point cloud in the inner and outer area from the straight pipe. The elbow direction is estimated by the deviation direction of the cloud, while the elbow radius and distance are computed via the estimation of its tangent. The elbow length is estimated by detecting the deviation of the point cloud from the elbow model. On the other hand, the branch direction of the T-junction is computed by identifying the highest intensity area. The branch radius is estimated based on the circumcircle through three points on the branch, while the T-junction distance and length are directly inferred from these points. To ensure real-time operation, several techniques are applied, including point skipping, language-dependent optimizations, and a multi-processor offloading scheme to distribute computational tasks across processors in the real PIG processing unit. The results collected with the pull-rig robot and the prototype pipeline validate that the RT-ETDE framework consistently detects and estimates the elbow and T-junction, as well as satisfying real-time constraints with a 10 Hz frame rate on the real PIG processing unit. The contribution of this paper is summarized as follows:  
* We propose an RT-ETDE framework which is the first solution that simultaneously achieves real-time operation, early detection, and full parameter estimation of elbows and T-junctions using 3D point clouds. * We propose a set of schemes for detecting and estimating each parameter of elbows and T-junctions in the RT-ETDE framework. Each scheme is designed to be computationally efficient and robust by relying on intelligent point cloud partition, feature extraction, and simple geometric solutions. * We present several techniques, including a multi-processor offloading scheme, to ensure the real-time constraint. * We validate the performance of the framework with real experiments. The results show that the detection and estimation are consistent. * The processing time result on the real PIG processing unit indicates that the RT-ETDE framework satisfies the 10-Hz real-time constraint. The remainder of this paper is organized as follows. Section 2 introduces related works. Section 3 describes the system model and defines the problem. In Section 4, we present our RT-ETDE framework. The real-time adaptation techniques are presented in Section 5. The numerical results are discussed in Section 6, and finally, the conclusion of this paper is given in Section 7.
## 3 Results and Discussion
### Cytotoxic Effects of Au-Ag Alloy NPs  
Metal NPs, particularly Ag NPs, frequently exhibit cytotoxic properties, which can be advantageous in specific purposes, such as the suppression of cancer cell proliferation [12; 26; 36]. However, their cytotoxicity raises safety concerns regarding their use in health care, cosmeceuticals, and environmental contexts. The cytotoxic effects of metal NPs are influenced by their physicochemical properties, such as size, shape, surface charge, bio-capping, concentration, and exposure duration [31]. The cytotoxic effects of Au-Ag alloy NPs on various cell types after 24 h exposure are presented in Figure 3. The concentration of Au-Ag alloy NPs was negatively correlated with cell viability. Raw264.7 cells exhibited the least sensitivity to these NPs, whereas CCD966 SK cells exhibited the highest sensitivity. Considering a cell viability threshold of 80% as the safety limit, we determined the safe concentration for HaCaT and CCD966 SK cells to be 80 mg/L. On the basis of a curve depicting the correlation between cell viability and NP concentration, the IC\({}_{50}\) values for the cytotoxic effects of Au-Ag alloy NPs on HaCaT and CCD966 SK cells were determined to be 120.87 and 112.61 mg/L, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **C** & **O** & **K** & **Cl** & **Ag** & **Au** \\ \hline Au & 9.18 & 2.15 & 1.05 & 1.01 & – & 86.61 \\ Ag & 8.34 & 5.18 & 0.68 & 0.57 & 85.23 & – \\ Au-Ag alloy & 10.28 & 3.35 & 0.74 & 1.86 & 40.21 & 43.56 \\ \hline \hline \end{tabular}
\end{table}
Table 1: EDS analysis of various NPs (element % by mass).  
[PERSON] et al. (2016) found that Ag NPs synthesized using Siberian ginseng stem extract exhibited considerable toxicity toward HaCaT cells, reducing cell viability to 45% at a concentration of 100 mg/L [26]. By contrast, Au NPs derived from the same plant extract exhibited relatively low toxicity toward HaCaT cells. [PERSON] et al. (2019) reported that Au-Ag alloy NPs synthesized using isonicotinylhydrazide exerted strong cytotoxic effects on mouse skin fibroblast cells [40]. Furthermore, [PERSON] et al. (2023) noted that Au-Ag alloy NPs synthesized using sericin protein reduced the viability of HaCaT cells to <80% when at a concentration of >25 mg/L [41]. The cytotoxicity of these NPs has been attributed to the elevated proportion of Ag within the composite NPs [42]. Together, the findings indicate that careful selection of plant extracts, such as MQLE, as reducing and capping agents can mitigate cytotoxicity and expand the potential applications of Au-Ag alloy NPs.
# Review of Image-Based 3D Reconstruction of Building for Automated Construction Progress Monitoring
## 3 Technology Path of Image-Based 3D Reconstruction
### Image-to-BIM Alignment
Although the early process of image registration and point cloud generation has been simplified, the process of picking the equivalent points manually was not subtracted, which requires a lot of manual work. It is time-consuming, sometimes it must be repeated, the result is not precise and leads to limited usefulness. 4. Perspective-based: This method uses the relationship among points, lines, and surfaces in images to directly register the image with BIM. For example, [PERSON] et al. [4] and [PERSON] et al. [44] proposed a new method to register images with BIM using perspective alignment for indoor monitoring of construction, as shown in Figure 5. First, video frames were captured with a monocular camera system to create as-built data of the current construction status. Second, the first frame was registered initially with the BIM model by superimposing the wire frame model on it in an AR manner. Then the fine correspondence between the model and the as-built scene was calculated from line candidates extracted by scanning as-built images RoI. Although only the first frame needs manual alignment, each video needs manual processing, which obviously requires a lot of manual labor. Because each room needs a separate video, and they need to be shot daily. Similarly, [PERSON] et al. [45] propose a novel procedure for 3D layout recovery of indoor scenes from single 360\({}^{\circ}\) panoramic images. The proposed method combined geometric reasoning and deep learning to generate a pruned set of lines belonging to the main structure of the room, from which they extracted candidate corners and generated layout hypotheses. These alignment methods register the as-built image directly with the as-planned model without the assistance of point clouds. However, these technologies only have been applied in the indoor decoration stage with little occlusion, which may not be applicable to the outdoor scenes.
# Bilateral Teleoperation System with Integrated Position/Force Impedance Control for Assembly Tasks
## 5 Experimental Validation
### Precision and Effectiveness of Teleoperation System
#### 5.2.2 Position Tracking Accuracy  
The operator wore the tracker and moved randomly; the remote robot followed the trajectory of the operator. The force and position data were recorded.  
Figure 7 describes the tracking performance in the X direction. The red line represents the positions on the local side while the blue line shows the positions on the remote site. At the beginning, the tracker was kept fixed. The results showed that the static tracking error of the robot arm was 0.336 mm, which matched the discussed positioning accuracy of the tracker. When the operator moved randomly, the results demonstrated that the relative position errors were 6.2 mm in motion. The large tracking error in the motion state was caused by the delay in motion response, since the remote robot received a new motion command before it had moved to the target position when some points had not yet arrived. Experiments have shown that the position trailing was precise enough for teleoperation, and the teleoperation system was effective.  
Figure 6: The positioning data of the tracker in the X direction.
# Decentralized Storage with Access Control and Data Persistence for e-Book Stores
## 4 Proposed System  
As described in the previous sections, the basic characteristics required of the e-book store envisioned in the proposed methodology are the following two points:  
* Persistent distributed storage is used so that data are never lost once uploaded.
* Only the user who has the access right to the e-book can retrieve the e-book data. (Without access rights, the node providing storage cannot retrieve the entire e-book data.)  
The proposed system realizes these functions using a decentralized distributed storage called d-book-repository and a smart contract called Access Control Contract (ACC) to manage access rights. In the following, we show a concrete procedure for uploading/downloading content in the proposed system, followed by an explanation of the supporting mechanisms in the order of access control and data persistence.
Sensors_**2013**, _13_, 5686-5699; doi:10.3390/s130505686  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Selective Detection and Automated Counting of Fluorescently-Labeled Chrysotile Asbestos Using a Dual-Mode High-Throughput Microscopy (DM-HTM) Method**  
**[PERSON] 1, [PERSON] 2, [PERSON] 1,\({}^{\dagger}\), [PERSON] 2, [PERSON] 3 and [PERSON] 34,*g**  
1 Department of Mechanical Engineering, Graduate School, Kookmin University, Jeongneung-ro 77, Seongbuk-gu, Seoul 136-702, Korea; E-Mails: [EMAIL_ADDRESS] (M.-O.C.); [EMAIL_ADDRESS] (D.L.)  
2 Department of Chemistry, Graduate School, Kookmin University, Jeongneung-ro 77, Seongbuk-gu, Seoul 136-702, Korea; E-Mails: [EMAIL_ADDRESS] (H.M.C.); [EMAIL_ADDRESS] (Y.G.Y.)  
3 School of Mechanical Systems Engineering, Kookmin University, Jeongneung-ro 77, Seongbuk-gu, Seoul 136-702, Korea; E-Mail: [EMAIL_ADDRESS]  
4 Department of Integrative Biomedical Science and Engineering, Graduate School, Kookmin University, Jeongneung-ro 77, Seongbuk-gu, Seoul 136-702, Korea  
\({}^{\dagger}\) Current address: Department of Mechanical and Materials Engineering, University of Nebraska-Lincoln, 1400 R street, Lincoln, NE 68588, USA.  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +82-2-910-4767; Fax: +82-2-910-4839.  
_Received: 16 February 2013; in revised form: 22 April 2013 / Accepted: 24 April 2013 / Published: 2 May 2013_  
**Abstract:** Phase contrast microscopy (PCM) is a widely used analytical method for airborne asbestos, but it is unable to distinguish asbestos from non-asbestos fibers and requires time-consuming and laborious manual counting of fibers. Previously, we developed a high-throughput microscopy (HTM) method that could greatly reduce human intervention and analysis time through automated image acquisition and counting of fibers. In this study, we designed a dual-mode HTM (DM-HTM) device for the combined reflection and fluorescence imaging of asbestos, and automated a series of built-in image processing commands of ImageJ software to test its capabilities. We used DksA, a chrysotile-adhesive protein, for selective detection of chrysotile fibers in the mixed dust-free suspension of crysotile and amosite prepared in the laboratory. We demonstrate that fluorescently-stained chrysotile and total fibers can be identified and enumerated automatically in a high-throughput manner by the DM-HTM system. Combined with more advanced software that can correctly identify overlapping and branching fibers and distinguish between fibers and elongated dust particles, the DM-HTM method should enable fully automated counting of airborne asbestos.  
Keywords:asbestos; chrysotile; DksA; high-throughput microscopy; dual-mode imaging; reflection; fluorescence; image processing and analysis; automated counting +
Footnote †: journal: Journal of Physics B
# An Efficient Library for Reliability Block Diagram Evaluation
## 1 Introduction
### Context and Motivation
* **Edraw Block Diagram**: this commercial tool allows the definition of RBD models [38]. * **Reliability Workbench**: this commercial tool allows the definition and analysis of scalable RBD models through the usage of submodels. Furthermore, it supports the _minimal cut-set_ analysis of the RBD model [39]. * **Relyence RBD**: this commercial tool has features comparable with Reliability Workbench [40]. * **SHARPE**: the Symbolic Hierarchical Automated Reliability and Performance Evaluator (SHARPE) tool is a general hierarchical modeling tool that analyzes stochastic models of reliability, availability, performance and performability [41; 42]. This tool al  
Figure 2: Application of layered reliability evaluation. lows the definition of hierarchical reliability models with several formalisms, including RBDs, and it supports the time-dependent reliability analysis. Of all considered tools, SHARPE is the closest to all our requirements. Since no one fits completely all of them, we have implemented a custom library that provides the RBD evaluation, from now on referred as _librbd_. More specifically, the _librbd_ library supports the numerical computation of the reliability curve for all RBD basic blocks, it exploits several optimizations and multithreading paradigm, and it is multiplatform. Finally, we have publicly released this open source library under the AGPL v3.0 license [43].
## 3 The Improved Path Planning Method
The success rate of path iteration and the results of path reduction rate are presented in Table 2, where the reduction rate represents the percentage reduction to the RRT* path length. The success rates of CDTD3 performing point-to-point tasks in the three environments were 91.3%, 87.3%, and 95.6%, respectively, which were better than those of TD3 and DDPG, owing to the random exploration mechanism of CDTD3 algorithm. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Index** & **Complex Obstacle** & **Maze Space** & **Narrow Space** \\ \hline \multirow{2}{*}{CDTD3} & Mean (cm) & 136.60 & 244.32 & 179.55 \\  & Variance & 2.59 & 20.51 & 12.39 \\ \hline \multirow{2}{*}{TD3} & Mean (cm) & 142.81 & 252.56 & 191.88 \\  & Variance & 23.79 & 127.29 & 75.65 \\ \hline \multirow{2}{*}{DDPG} & Mean (cm) & 145.62 & 258.12 & 201.08 \\  & Variance & 38.44 & 278.97 & 260.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mean and variance statistics of path length in the 2D simulation environment. Figure 8: Probability density results of path length. (**a**) The results of the maze space. (**b**) The results of the narrow space. Figure 9: Diagram of the relationship between path length change and the algorithm. (**a**) The results of the maze space. (**b**) The results of the narrow space. CDTD3 is combined with RRT* and RRT, respectively; and conducted 100 simulations in complex obstacle and maze environments. The settings of RRT remained consistent with those of RRT*, except for the inherent characteristics of their respective. The simulation results are shown in Table 3. Using CDTD3-RRT* could achieve a superior path due to the advantageous features offered by RRT*. This characteristic led to an improved initial path, resulting in a shorter final path. In the maze environment, the reduction rate of CDTD3-RRT was better than that of CDTD3-RRT*, but the final path length was not as good as that of CDTD3-RRT* since the path of the original RRT was longer than that of RRT*. Nevertheless, favorable outcomes can still be achieved using the proposed method. In the complex obstacle space and maze space, CDTD3-RRT* was compared with two more advanced path planning methods, including the artificial potential field with informed RRT* (APF-IRRT*) [36], the adjustable probability and sampling area RRT algorithm (APS-RRT) [37]. Table 4 presents the performance comparison of CDTD3-RRT* under 100 experiments. It can be seen that CDTD3-RRT* has significant advantages under the path length. Owing to the powerful sampling and search ability of RRT* and the optimization ability of CDTD3, APS-RRT and APF-IRRT* use limited exploration range operations in the two complex environments, which is not conducive to obtaining better paths.
# RANDnet: Vehicle Re-Identification with Relation Attention and Nuance-Disparity Masks
###### Abstract
This methodology adeptly minimizes intra-class distances across varying viewpoints of the same ID, requiring no additional data beyond  
Figure 1: The challenges faced by vehicle re-identification. The first row of images depicts interference from foreground occlusion and background clutter on vehicle images. The images in the second row highlight the inadequacy of approaches solely focused on identifying the most discriminative features as they fail to ensure effective vehicle re-identification across multiple viewpoints. vehicle IDs for network training. Our approach achieved a SOTA performance on VERI-776 and VehicleID datasets. The primary contributions of this paper are summarized as follows:  
1. We propose RANDnet, a network designed to mitigate background and foreground occlusions, discern multiple discriminative regions, and adaptively learn structural information related to vehicle viewpoints without the need for additional labeling, thereby significantly enhancing the accuracy of vehicle re-identification. 2. The introduction of the disparity mask for the automatic suppression of foreground noise or the background through simple negative class response intersections, and the nuance mask, which leverages hard negative class response intersections to reveal sub-discriminative vehicle image regions, facilitating the acquisition of robust global features. 3. We present a relation attention module incorporating an advanced transformer architecture, which enables the network to assimilate a relationship vector among various vehicle components, significantly narrowing the intra-class distances across divergent viewpoints. 4. Extensive experiments and visualization results demonstrate that our approach achieves the SOTA in the task of vehicle re-identification.