esa-sceva/satcom-chunk-collection huggingface dataset:
# Traffic Conflict Prediction for Sharp Turns on Mountain Roads Based on Driver Behavior Patterns
## 3 Results
### Association Rule Mining Results  
#### 3.2.1 Association Rule Generation  
Focusing on the driving behavior patterns derived from the FP-Tree analysis, we developed association rules that estimate the relationship between certain driving behaviors and traffic conflicts. These rules are characterized as \"if behavior pattern X occurs, then traffic conflict Y follows with probability Z\". The reliability and strength of these relations were managed through two parameters:  
Minimum support threshold (min_sup = 0.02): This parameter specifies the minimum rate of occurrence of association between the behavior and conflict that qualifies as significant. We chose a very low baseline (0.02 or 2%) due to a preliminary data review that indicated that serious conflicts are infrequent occurrences. This value captures critical patterns that lead to immense conflicts while maintaining statistical significance. In comparison, prior research conducted in city settings ([PERSON] et al., 2022) [13] had an average range of 0.05-0.10, which, in turn, would have disregarded numerous important mountain-specific patterns in our model. Minimum confidence threshold (min_conf = 0.6): This parameter defines the minimum conditional probability required for a valid association rule. Our selection of 0.6 (60%) means that for a rule to be accepted, when the specified driving behavior pattern occurs, the associated traffic conflict must follow at least 60% of the time. This threshold was determined through cross-validation testing, which showed that lower values (0.4-0.5) produced too many spurious associations, while higher values (0.7-0.8) missed important moderate-strength relationships that were nonetheless actionable for safety interventions.
# A Case Study on Integrating an Eco-Design Tool into the Construction Decision-Making Process
## 4 Conclusions  
The aim of this work was to optimize, test, and validate an eco-design tool initially developed in the UAveiroGreenBuilding project. The tool is designed to incorporate eco-design concepts into decision-making for construction projects using information sourced from environmental product declarations (EPDs) and life cycle assessment (LCA) studies. The tool proved to be effective in processing data and providing easily interpretable outputs, enabling intuitive comparisons of the performance of competitive construction solutions.  
Different window frame solutions considered in the rehabilitation of the Central Technical Area of the University of Aveiro were used as the case-study. PVC window frames were found to have the best overall performed due to their well-balanced environmental and economic profiles. However, the unavailability of Portuguese Environmental Product Declarations may limit the regional applicability of the gathered results, stressing the need for further incentives to publish GDP.  
Communication with BIM was achieved via a Dynamo routine. The developed routine simplified and expedited the data transfer process, significantly improving the consistency and updating of BIM models while reducing manual labor and associated errors. Similar to BIM, the eco-design tool could be linked to digital product passports in the future, providing a comprehensive and detailed overview of the materials used in construction projects, as well as their environmental impacts and service performance throughout their life cycle. Continuing to develop the integration of digital tools in the building sector will further facilitate decision-making processes, increasing transparency and compliance with updated construction standards and ultimately contributing to a more efficient and sustainable construction sector.  
Methodology, G.A.; formal analysis, K.C. and G.A.; investigation, K.C.; data curation, K.C.; writing--original draft, K.C.; writing--review & editing, G.A. and V.M.F.; supervision, G.A. and V.M.F.; project administration, V.M.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the EEA grants (UAveiroGreenBuilding project), managed by the Environmental Program in Portugal. This research was also supported by FCT--Fundacao para a Ciencia e Tecnologia, I.P., by project reference UIDB/04450/2020 and DOI identifier 10.54499/UIDB/04450/2020. This publication only reflects the authors' views, thus exempting the funding agency from any liability.  
Figure 6: Parameters entered into BIM (**a**) and imported data after executing the developed Dynamo routine (**b**).
## 5 Conclusions  
In this paper, a multi-area nonlinear state estimation model is presented. The model considers the Innovation Index (\(II\)) concept, which estimates the masked error component pertaining to the Jacobian null space and then composes the measurement error. The multi-area measurement model is solved with the alternative direction method of multipliers, which enables minimal information sharing. Validation is made considering two \(IEEE\) test systems. The two systems are partitioned to emulate different control centers controlling and monitoring interconnected power system. Tests include comparison with the benchmark centralized quasi-static measurement model. Test results statistics regarding robustness to measurement noise and computational speed are analyzed. Presented results show that the model is capable of handling noise in shared states among areas while maintaining computational speed and precision. Further, multi-area model converges to centralized solution within acceptable range of errors. Considering that the majority of utilities companies software rely on a centralized nonlinear state estimation, the presented model can be easily integrated in real-life application without major changes.  
Conceptualization, N.A.; methodology, N.A.; software, N.A. and T.Z.; validation, N.A.; formal analysis, N.A.; investigation, N.A. and T.Z.; resources, N.A.; data curation, N.A.; writing--original draft preparation, N.A.; writing--review and editing, N.A. and T.Z.; visualization, N.A. and T.Z.; supervision, A.S.B. and N.G.B.; project administration, A.S.B.; funding acquisition, A.S.B. All authors have read and agreed to the published version of the manuscript.  
This work was supported by NSF grant ECCS-1809739.  
Not applicable.  
Data Availability Statement: Data is contained within the article or supplementary material.  
The authors declare no conflict of interest.  
Figure 11: Performance index for different noise level on the IEEE-118 bus system 3 regions.
# IRONEDGE: Stream Processing Architecture for Edge Applications
## 3 Proposed Architectural Framework
### Generic Pipeline for Stream Processing  
The Stream Processing component represents any processing that will be applied to the received Data Reports, so as to acquire the corresponding Events. For different use cases where the IRONEDGE framework is applied, the implementation details of this component will differ in two dimensions: (i) The Stream Processing system chosen for the implementation in a given use case (e.g., Apache Storm, Apache Flink, among others); (ii) The nature of the steps that needed to produce Events for a given Data Source. In order to simplify the development of customized solutions under the IRONEDGE framework, a  
Figure 2: High-level overview of the proposed framework, identifying the fundamental set of components: Data Collection, Stream Processing, Local Storage, Event Output.  
generic pipeline for any smart infrastructure use case is provided. This pipeline makes no assumptions about the implementation details of any component, but can be applied to any Stream Processing system. Additionally, the usage of a common model to define processing pipelines has, as a secondary advantage, minimizing the effect of different requirements in the use cases. This is when comparing the performance of different implementations of the IRONEDGE framework.  
Consider the proposed pipeline, as presented in Figure 3, in the context of the high-level overview of the architecture. The set of steps included in the pipeline can be described as follows:  
1. Input Stage--Read data from the Data Collection component, with no transformations applied. Messages should be tagged with the current timestamp;
2. Mapping Stage--Data Reports are parsed and mapped into a use-case-specific Data Model, a Data Point. Additional context may be fetched from the Local Storage;
3. Windowing Stage--Data Points are grouped by common characteristics in the use case (e.g., time intervals, geographic area);
4. Aggregation Stage--The created groupings are aggregated into a data aggregate;
5. Predictive Stage--Given a data aggregate, a use-case-specific predictive model is applied, which allows the detection and classification of Events. Additional parameters may be fetched or stored in the Local Storage;
6. Output Stage--The resulting Events are output into the Event Output component.
# Formation Control of Dual Auto Guided Vehicles Based on Compensation Method in 5G Networks
## 1 Introduction
For the control system, if data packet cannot arrive in one control cycle, the data will be dropped out. The packet loss will cause the system to have no new data available. 2. Delay. The network system is responsible for the data transmission and exchange of all nodes. Due to the limited bandwidth, many nodes can only share network bandwidth resources. Network congestion will inevitably bring communication delays, especially when multiple nodes send signals at the same time. There will be a delay which cannot be ignored compared to the control cycle. The real-time performance of the control signal and feedback signal will be reduced, and the control system is prone to problems such as losing accuracy and stability. How to deal with the problem of delay is the key issue in applying 5G networks to real-time control. 3. Control cycle. To meet the needs of different tasks in real-time control, there are often multiple control cycles on the controller. How to set the control cycle considering 5G R15 capability to access the requirements needs to be discussed. Due to the data sharing, each cycle will be coupled with others. The influence of other control cycles cannot be ignored when analyzing NCS control. To design NCS AGV control system, researchers propose many methods such as robust control [18] and compensation control [19]. NCS AGV control system on AGV has received researchers' attention. However, much research has focused on proving stability of the NCS and pays less attention to specific networks [18; 20]. Research on multi devices cooperation in specific networks usually chooses near field communication technologies such as Bluetooth [21] and ZigBee [22]. AGV control in 5G networks is concentrated on single AGV [12]. There is few research on multi-AGV collaborative transport considering 5G networks. The main contributions of this paper include the estimation of 5G communication delay and the application of 5G delay estimation in dual-AGV formation control. To realize the formation control of dual-AGV collaborative transport in 5G R15, this paper measures and analyzes the delay characteristic of 5G R15 and establishes delay estimator based on Kalman filter. Then this paper proposes a control system of dual-AGV collaborative transport in 5G R15 network. Finally, an experiment is carried out to prove the effectiveness of the control system. The following sections of this paper are organized as follows: Section 2 discusses the characteristics of 5G R15 and estimation of the transmission delay. Section 3 establishes a dual-AGV collaborative transport control system considering 5G R15 network characteristics. Section 4 carries out the experiment. Section 5 summarizes the full paper.
## 2 Action Mechanism and Calibration of the Optical-Fiber-Embedded Beam (OFEB)
### Calibration Test  
The accuracy of settlement measurements is very important. Thus, a calibration test was conducted to assess the measurement accuracy of the OFEB.  
Figure 4: Schematic showing how the beam deflection is calculated.  
As shown in Figure 5a, the ends of the OFEB were consolidated on movable supports with an adjustable height to facilitate on-site installation. A displacement transducer was used to measure the beam deflection and to calibrate the measurements of the optical fiber. A 0.9 mm diameter single-mode tight-jacketed optical fiber was used as the sensor, and the beam body was a uniform and flexible high-density polyethylene (HDPE) pipe with an external diameter \(D\) of 25 mm and a thickness of 2.5 mm.  
Figure 5b shows the photographic image of the calibration test. First, the OFEB was constructed: an optical fiber was laid symmetrically along the upper and lower surfaces of the HDPE pipe, coated with epoxy resin glue, and firmly bonded to the pipe. A 2.2 m length of the OFEB was used to perform a calibration test. An excessively long OFEB was not constructed to enable the calibration test to be carried out in the laboratory. Second, the OFEB was fixed to the support by being passed through an iron junction box filled with epoxy resin glue. The consolidation length at the end of the beam was removed to obtain an OFEB with an effective test length of 2 m for the calibration test.  
A customized displacement transducer with a measurement range of 400 mm and a measurement accuracy of 0.1 mm was used in this study. Displacement transducers were arranged at one third and two thirds of the length of the OFEB (corresponding to the measurement points A and B shown in Figure 2, respectively). The top ends of the displacement transducers were connected to the OFEB through a ring clamp to control the settling rod to drive the OFEB to realize differential settlement. During the test, no settlement occurred at A, while five random settlement groups occurred at B. The settlement values were categorized from smallest to largest and assigned the names Tests 1 to 5.
## 4 Conclusions  
In this study, the extracts of _A. carduchorum_, prepared using five solvents with different degrees of polarity, were investigated for their phytochemicalen profile and tested for antioxidant and enzyme inhibition properties. While the extracts showed varying antioxidant potential, the polar extracts and, in particular, the methanolic extract showed better antioxidant abilities, which could be related to the richness in bioactive contents as revealed by HPLC-MS/MS. Moreover, while the polar extracts were better antioxidants in terms of the radical scavenging and reducing activities, the less polar extracts were better enzyme inhibitors. The findings from this study, therefore, demonstrated that the solvent is an important factor to consider in the extraction of _A. carduchorum_, as it clearly influenced the total bioactive contents and the biological activities. This study could be used to guide optimum extraction of phytochemicals from _A. carduchorum_ and direct further studies (animal, toxicity, bioavailability, etc.) to maximize the extraction of bioactive compounds and investigate the effects of groups of compounds on the biological potencies of extracts.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12136561/s1](https://www.mdpi.com/article/10.3390/app12136561/s1)]([https://www.mdpi.com/article/10.3390/app12136561/s1](https://www.mdpi.com/article/10.3390/app12136561/s1)), Table S1. HPLC-MS/MS acquisition parameters, working as \"dynamic Multiple Reaction Monitoring\" mode, including retention time (RI) and delta retention time (\(\Delta\) Rt) for each transition.  
Conceptualization, A.I.U., G.Z. and D.M.; methodology, G.Z., U.C., S.S. and M.D.U.; software, A.I.U.; validation, G.Z., D.M. and G.C.; formal analysis, M.M.H.; investigation, G.Z., G.C., G.S. and S.A.; resources, G.Z.; data curation, A.I.U. and D.M.; writing--original draft preparation, S.J. and M.F.M.; writing--review and editing, D.M. and M.F.M.; visualization, A.I.U.; supervision, G.Z.; project administration, G.Z.; funding acquisition, D.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Compound** & **GI** & **BBB** & **CYP1A2** & **CYP2C19** & **CYP2C9** & **CYP2D6** & **CYP3A4** & **Lipinksi Rule** & **PAINS** \\ \hline Shikimic acid & High & No & No & No & No & No & No & Yes; 0 violation & 0 alert \\
5-Caffeoylquinic acid & Low & No & No &
# A Fast Acquisition Algorithm for Hybrid Signals of 5G and BeiDou B1
## 3 Results and Discussion
### Hybrid Signal Receiver Hardware Design
Considering the future differential applications and combined navigation applications, a wireless transmission module interface and IMU module interface are also designed, where the wireless transmission module interface is used to receive the reference station radio differential data or VRS public network differential data, and the IMU module interface is used for the development and application of combined navigation algorithms. Figure 10: (**a**) Hybrid signal receiver hardware. (**b**) Hybrid receiver hardware circuits design. (**c**) FPGA baseband architecture design.
# DeNERT-KG: Named Entity and Relation Extraction Model Using DQN, Knowledge Graph, and BERT
## 4 Experiments
### Results  
#### 4.3.1 Result for NER Task  
CoNLL 2003, EWNERTC, and W-NUT 2017 datasets are utilized to verify the performance of the DeNERT model that performs NER task. We use F1 score as the metric of the experiments.  
Table 4 is an experiment result of EWNERTC dataset. The proposed DeNERT model is applied to Deep Q-network by conforming the BERT model and POS embedding. We conducted an experiment on EWNERTC datasets to verify the performance improvement when applying POS tagging and different sizes of BERT models used in DeNERT model. All models are the results of Deep Q-Network application. First of all, we conducted an experiment on the size of the BERT model. We also experimented with models with POS embedding on each model. As a result, the DeNERT model we propose shows the highest F1-score.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Relations**} \\ \hline no\_relation & perchildren \\ org:alternate\_names & percities\_of\_residence \\ org:city\_of\_headquarters & percity\_of\_birth \\ org:country\_of\_headquarters & percity\_of\_death \\ org:dissolved & per:countries\_of\_residence \\ org:founded & per:country\_of\_birth \\ org:founded\_by & per:country\_of\_death \\ org:member\_of & per:date\_of\_birth \\ org:members & per:date\_of\_death \\ org:number\_of\_employees/members & per:employee\_of \\ org:parents & per:origin \\ org:political/religious\_affiliation & perother\_family \\ org:shareholders & per:parents \\ org:stateproprovince\_of\_headquarters & per:religion \\ org:subsidiaries & per:schools\_attended \\ org:top\_members/employees & per:siblings \\ org:website & per:spouse \\ per:age & per:stateprovince\_of\_birth \\ per:alternate\_names & per:stateprovince\_of\_death \\ per:cause\_of\_death & per:stateprovince\_of\_residence \\ per:charges & per:title \\ \hline \hline \end{tabular}
\end{table}
Table 2: Relations in TACRED dataset.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type** & **Detail Configuration** \\ \hline CPU & Intel Xeon E5-2620 [EMAIL_ADDRESS] GHz \\ OS & Ubuntu 16.04 \\ Memory & 32 GB \\ HDD & 3.6 TB \\ GPU & EVGA GeForce GTX 1080 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Configuration of the server.  
Table 5 is the result of a comparative experiment with existing models to verify the model we propose. CoNLL 2003 and W-NUT 2017 datasets are used to conduct experiments. Results of the CoNLL 2003 dataset show the highest F1-score 93.45 when the proposed model is used. In W-NUT 2017 dataset, our model shows the second score 45.78. The W-NUT 2007 data show relatively lower results than the CoNLL dataset because many noise data exist.
# Lossless Compression of Sensor Signals Using an Untrained Multi-Channel Recurrent Neural Predictor
## 3 Multi-Channel Recurrent Predictor  
In this section, we present the proposed multi-channel recurrent predictor by first introducing the background of RNNs.
## 3 Methods
### Proof of System Stability  
The Lyapunov function of the control system is defined as follows:  
\[L=\frac{1}{2}\mathbf{r}^{T}\mathbf{M_{e}}\mathbf{r}+\frac{1}{2}tr(\mathbf{\bar{ W}}^{T}\mathbf{F}^{-1}\mathbf{\bar{W}}), \tag{26}\]  
where \(\mathbf{F}\) is a positive definite matrix, \(\mathbf{\bar{W}}\) is the weight error matrix of the RBF neural network in approximation, \(tr(\mathbf{X})\) is to trace matrix \(\mathbf{X}\), whose result is a constant value.  
The first derivative of the Lyapunov function shown in Equation (26) is obtained as follows:  
\[\dot{L}=\mathbf{r}^{T}\mathbf{M_{e}}\dot{\mathbf{r}}+\frac{1}{2}\mathbf{r}^{T} \mathbf{M_{e}}\mathbf{r}, \tag{27}\]  
Combining Equation (20), Equation (22), and Equation (24):  
\[\dot{L}=-\mathbf{r}^{T}\mathbf{Kr}+\mathbf{r}^{T}(\varepsilon+\mathbf{J_{eh}}^ {T}\mathbf{F_{h}}+\mathbf{J_{ek}}^{T}\mathbf{F_{k}}+\mathbf{\tau_{d}}+\mathbf{ v}), \tag{28}\]  
Due to:  
\[\begin{split}&\mathbf{r}^{T}(\varepsilon+\mathbf{J_{eh}}^{T} \mathbf{F_{h}}+\mathbf{J_{ek}}^{T}\mathbf{F_{k}}+\mathbf{\tau_{d}}+\mathbf{ v})=\mathbf{r}^{T}(\varepsilon+\mathbf{J_{eh}}^{T}\mathbf{F_{h}}+\mathbf{J_{ek}}^{T} \mathbf{F_{k}}+\mathbf{\tau_{d}}\Big{)}+\mathbf{r}^{T}\mathbf{v}\\ &=\mathbf{r}^{T}(\varepsilon+\mathbf{J_{eh}}^{T}\mathbf{F_{h}}+ \mathbf{J_{ek}}^{T}\mathbf{F_{k}}+\mathbf{\tau_{d}}\Big{)}-\mathbf{r}^{T}( \varepsilon_{\mathbf{N}}+\mathbf{b_{d}})\leq 0\end{split} \tag{29}\]  
Then \(\dot{L}\leq 0\). According to [PERSON]'s stability discrimination theorem, when \(\mathbf{r}\
eq 0\), \(\dot{L}(\mathbf{r})<0\) and \(\mathbf{r}=0\), \(\dot{L}(\mathbf{r})=0\). Because \(\dot{L}(\mathbf{r})\) is a negative definite, it is asymptotically stable at the origin, and because \(\dot{L}\leq 0\), \(\mathbf{r}\) and \(\mathbf{e}\) is uniformly bounded by [PERSON] stability theory. In addition, because \(\mathbf{r}=\dot{\mathbf{e}}+\mathbf{\Lambda e}\), \(\dot{e}\) is uniformly bounded, therefore, according to [PERSON]'s lemma, when \(t\rightarrow\infty\), \(\dot{L}(\mathbf{r})=0\), it is \(\mathbf{r}\to 0\), thus \(\mathbf{e}\to 0\) and \(\dot{\mathbf{e}}\to 0\), which proves that the control system is stable.