esa-sceva/satcom-chunk-collection huggingface dataset:
# PRANAS: A Process Analytics System Based on Process Warehouse and Cube for Supply Chain Management
## 4 Applications to Process Analytics
### Performance Prediction  
Since process warehouse and cubes in PRANAS include the functions of OLAP, data mining or machine learning techniques can be performed on the data schema. Therefore, the system can be utilized to analyze operation performance by choosing the suitable analytics algorithms such as classification, regression, and clustering. Suppose a manager is willing to analyze the last two years of data, as well as the collaboration processes related with all company sizes except small-sized companies. To filter the target data, two dicing operations are required for time dimension and class type dimension. After this, data analytics techniques such as decision tree and linear regression methods are applied. Two example scenarios of the data-oriented analytics are presented below.  
* Classification for on-time delivery: Suppose a manager analyzes the process to find which product or project is expected to be frequently delayed. In this example, the classification models predict categorical classes which are 'on-time delivery' or 'delayed delivery' with the dimensions selected from the process cube. Figure 5(a) shows the result of using the decision tree classifier by adopting the classification techniques for the on-time delivery. The result of the first branch in decision tree can be interpreted as follows: If 'Schedule Hit Rate' (the compliance rate schedule) is more than 95%, products are expected to be on-time delivery with 98.06%.
* Regression for design change rate: Assume a manager needs to predict how many times the design changes will be done for a particular situation in the process cube. The results derived from the regression method for the design change rate based on linear regression is shown in Figure 5(b). For the design change rate, in case of 'Priority' is not 2 (which means'medium'), the linear regression equation is: \[\begin{array}{l}\text{(DesignChangeRate)}=8.451+0.001\times\text{(ReceivingCost $-$ 10,686,667)}+0.434\times\\ \text{(Satisfaction $-$ 84.933)}+0.088\times\text{(EmailReplyTime$-$ 22.333)}\end{array}\] (1)  
From the regression model, it can be said that the factors such as receiving cost, satisfaction, and email reply time mainly affect the design change rate.  
Figure 6: Two examples of data-oriented analytics using PRANAS with Microsoft SQL Server BIDS. (**a**) Classification model for on-time delivery based on decision tree; (**b**) Regression model for design change rate based on linear regression.
## 4 Case Study of Interworking of QKD Networks and the Role of SDN Orchestrator
### Implementation Results of SDN Controller and SDN Orchestrator for KOREN
#### 4.2.4 General Procedure  
Figure 8 shows the exchange of messages between the user application and the network operator with QKDNC as well as QKDNO. General users refer to those who register on the user portal and apply to test and use the QKD service link on the KOREN test network. Network administrators manage the KOREN test network and are responsible for generating and managing service links requested by general users.  
When an interconnection request is received, the QKDNO of KOREN leverages its pre-existing knowledge of the two networks, each with its own vendor, to determine the processing route for the request, which is then transmitted to the QKDNC of each network domain for further processing of the specific route within their respective segments. It is feasible to generate a service and alter its path by transmitting a control remote procedure call (RPC) from the QKDNO to the QKDNC.  
This paper primarily focuses on the roles of the QKDNO and QKDNC in the process of transmitting keys between different QKD networks, as well as the development of related service flows and YANG models. Therefore, it only provides an explanation of this process with an RPC for the connectivity services between the QKDNO and QKDNC as follows:  
* Retrieve topology from each QKDNC: QKDNO retrieves the network topology information only relevant for the interoperable KMS from each QKDNC.
* Application registration from QKDNO and QKDNC: When the QKDNO receives an application request from a network administrator, it uses a path computation engine to design a path that can accommodate the request, taking into account the need for interconnection between different QKD networks if required.  
Figure 8: Sequence diagram of application registrations for interworking with RPC for the connectivity services.  
* Create service link: When an application requests a service, the QKDNO or QKDNC assigns a service link ID for the application and creates a combination of the application and service link. Since the node is specified, information can be sent to the KMS, but there is currently no path information.
* Create candidate paths per service link: Creates candidate paths per service link for an application. If necessary, continuous update requests can be made through a separate update RPC before selecting the final candidate path.
* Deploy candidate paths per service link: QKDNO-selected paths are deployed per service link for the application.
## 1 Introduction
### Deterioration of Bridge Structure Components  
Superstructure, which consists of beams and slabs, etc., is the span that directly supports live loads. On the other hand, substructure, which consists of piers and abutments, etc., supports the entire bridge structure and loads on the underground footings. Their long-term deterioration due to external loadings in the service environment is vastly reported as regards fatigue damage with crack propagation in concrete slabs and pier scours due to heavy rains and floods [2; 3]. Damage in slabs is mostly found in highway (motorway) superstructures, while pier scouring is naturally recognized as the main cause of damage and failure of bridges over waterways.  
In steel-concrete composite decks, the often-occurring damage type is fatigue damage due to numerously repeating traffic overloads [4; 5]. The final form of this damage is well known as large horizontal cracks in steel-concrete composite slabs [5]. As it is difficult to visually (by visual inspections) detect internal damage, which potentially affects structural performance [6], it becomes a very serious problem for highway reinforced concrete (RC) structures [7].  
Scouring is one of various problems that occur in the structural performance deterioration of bridges. It is a phenomenon due to insufficient rooting at the foundation of the pier due to disappearance of riverbed sediment with running water, which results in a loss of bridge stability [8]. It was pointed out that relatively few vibration-based damage detection studies focused on substructures (especially piers and foundations), as opposed to the studies on superstructures or entire bridges [9]. Since it has been particularly confirmed that vibration mode shapes of bridge piers are relatively simple, it is possible to detect pier scouring by the change in modal amplitude ratio in impact vibration tests [10; 11]. However, it is still too early to conclude that it is the optimal test method because it is risky to impact tall piers from the cap and it is almost impossible to constantly perform the test [12].  
Appropriate inspection, monitoring, and maintenance of aging structures are required for safety purposes. However, in the above-mentioned conventional measurement methods, the cable connections to the power grid may cause a mechanical limitation of inspection and inefficient in situ monitoring operation. A real-time monitoring system that can detect the damage and deterioration of target structures is of great help in globally challenging disaster mitigation and social safety issues. In order to achieve a safe civil infrastructure network, a compact, easy-to-use, small-sized, and self-sustaining device with a stand-alone power system is ideal to be applied for entire bridge structural health monitoring.
## 2 Materials and Methods
### Headspace Solid-Phase Microextraction (SPME)  
The volatile compounds were isolated according to the method applied in previous studies [21; 29] with slight modifications. An aliquot (2 g) of olive oil sample was accurately weighed (\(\pm\)0.0001 g) into a 20 mL glass vial and hermetically closed with a polytetrafluoroethylene (PTFE)/silicone septum. The vial was left for 5 min at 40 \({}^{\circ}\)C under agitation (250 rpm) to allow for equilibration of the volatile compounds in the headspace. Then, the SPME fiber (Divinylbenzene [DVB]/Carboxen [CAR]/Polydimethylsiloxane [PDMS], 50/30 \(\upmu\)m, length 2 cm; Sigma Aldrich, Darmstadt, Germany) was exposed to the headspace for 40 min to isolate the volatile compounds, under the same conditions. Before use, the fiber was thermally cleaned according to the instructions of the manufacturer and after each sample analysis at 260 \({}^{\circ}\)C for 5 min.
## 2 Spectral Analysis
### Numerical Evidence: Preconditioning, Visualizations, GMRES Iterations
Figure 6: Eigenvalues of the preconditioned matrix of size \(n^{2}=2^{12}\) for \(\gamma=0.8\) and \(\alpha=\{1.2,1.4,1.6,1.8\}\), respectively. Figure 8: Eigenvalues of the preconditioned matrix of size \(n^{2}=2^{12}\) for \(\gamma=1.5\) and \(\alpha=\{1.2,1.4,1.6,1.8\}\), respectively. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{\(\mu(x,y)=1/(x+\mathbf{i}y)^{\intercal}\)} \\ \hline  & \multicolumn{6}{c}{\(\gamma=0.5\)} \\  & \multicolumn{2}{c}{\(\alpha=1.2\)} & \multicolumn{2}{c}{\(\alpha=1.4\)} & \multicolumn{2}{c}{\(\alpha=1.6\)} & \multicolumn{2}{c}{\(\alpha=1.8\)} \\ \(n\) & - & \(P_{\tau}\) & - & \(P_{\tau}\) & - & \(P_{\tau}\) \\ \hline \(2^{4}\) & 35 & 9 & 41 & 8 & 47 & 7 & 54 & 7 \\ \(2^{5}\) & 54 & 9 & 67 & 9 & 83 & 8 & 101 & 7 \\ \(2^{6}\) & 82 & 10 & 109 & 9 & 144 & 8 & 189 & 7 \\ \(2^{7}\) & 124 & 11 & 177 & 10 & 251 & 9 & 351 & 7 \\ \(2^{8}\) & 189 & 11 & 288 & 10 & 437 & 9 & \(>\)500 &
###### Abstract  
For this paper, full-scale/model test comparisons to validate the traditional atmospheric boundary layer (ABL) wind-tunnel simulation technique performed until now by the wind engineering community are systematically reviewed. The engineering background includes some benchmark low-rise buildings specifically established for use in wind engineering research (the Aylesbury experimental buildings, the Texas Tech University experimental building, the Silose buildings, etc.), several high-rise buildings in North America and East Asia, long-span bridges, large-span structures, and cooling towers. These structures are of different geometries, are located in different wind environments, and are equipped with various transducers and anemometers. By summarizing the different articles in the literature, it is evident that notable discrepancies between the full-scale measurement and the model test results were observed in most full-scale/model test comparisons, which usually took certain forms: the mean and/or the peak negative pressures at the flow separation regions on buildings were underestimated in the wind tunnel; differences in the root-mean-square (rms) values of the acceleration samples between the full-scale measurements and the force balance model tests were non-negligible; the vertical vortex-induced vibration amplitudes of bridges measured using section models and aero-elastic models were much lower than those observed on the prototypes, etc. Most scholars subjectively inferred that inherent technical issues with the ABL wind tunnel simulation technique could be responsible for the observed full-scale/model test discrepancies, including the Reynolds number effects, the turbulent flow characteristics effects, and the non-stationarity effects. However, based on the authors' years of experience and after discussion with experienced researchers, it was found that some of the full-scale measurements performed in earlier research were inherently less accurate and deterministic than the wind tunnel experiments they were supposed to validate, which could also be a significant cause of the full-scale/model test discrepancies observed. It is suggested herein that future studies in this field should regard full-scale measurements only as benchmarks, and that future works should focus on synthesizing the results from different schools of physical experiments and formulating universal empirical models of high theoretical significance to properly validate future wind tunnel tests.  
Keywords:comparison study; full-scale measurement; wind tunnel test; Reynolds number effect; turbulent flow characteristics effect; non-stationarity effect; future perspectives +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 3 O-MAC Protocol Design Goals and Architecture  
For several applications, hybrid approaches are preferred over individual technology approaches in terms of energy efficiency, scalability and QoS provisioning. To improve the energy efficiency, scalability and QoS, O-MAC with the PT routing paradigm is designed and implemented. The O-MAC protocol leverages the features of both CSMA and TDMA. O-MAC uses a novel ODFF model for CSMA as a replacement for the IEEE 02.15.4 data frame format, whereas TDMA deploys the semi-synchronized scheduling methodology rather than traditional scheduling approach. The semi-synchronous approach helps to obtain faster access to the medium [20]. In our case, we deploy a semi-synchronized approach to handle the additional energy consumption that is consumed in the overhearing problem. The ODFF incorporates new features, such as a reduction in the size of the short preamble, introduction of the anycast methodology, automatic buffering, and insertion of a security portion. In addition, O-MAC involves three modules: interior communication, exterior communication, and dynamicity of the operational node. These three modules aim to improve the QoS provision and energy efficiency. As a result, the network lifetime is extended. The interior communication module uses the anycast methodology, semi-synchronized features, automatic buffering and randomization. The randomization feature is used to handle the congestion in the network when two nodes attempt to access the same slot in the channel. We achieve the following goals using O-MAC:  
* Reducing the time and energy consumption for channel access,
* Reducing the time and energy consumption for loss (retry) packets,
* Reducing the time and energy consumption for acknowledgement,
* Reducing the time and energy consumption for data forwarding,
* Handling the overhearing and avoiding congestion in the network,
* Improving the QoS provisioning (e.g., throughput, latency, bandwidth utilization), and
* Extending the network lifetime.
## 1 Introduction  
Information extraction (IE) aims to structure information contained in text. Entity and relation extraction (ERE) is a core task in information extraction. Entity pairs and the relations between them can be represented by relational triples (i.e., subject, relation, and object), where the subject is the head entity and the object is the tail entity. Thus, an unstructured text is structuralized by several triples, which are basic components of the knowledge graphs.  
The field of ERE has always faced the overlap problem. Sentences are divided into two overlapping types: single entity overlap (SEO) and entity pair overlap (EPO) [1]. The SEO means that one entity is overlapping and contained by multiple relational triples in a sentence, while the EPO means that an entity pair is overlapping. When a sentence has no overlap, it belongs to the normal category.  
In recent years, many studies have been devoted to this challenge; for example, CopyRE [1], CasRel [2], PRGC [3], etc. More studies and their detailed descriptions are given in Section 2.1.  
The aforementioned methods have made great contributions to joint ERE. However, few existing works consider the semantic fusion between subjects and relations when extracting objects. Incorporating subjects and relations as a priori information would detect more semantically relevant objects in the sentence and alleviate the problem of overlapping triples. It is worth noting that subjects and relations are two distinct types of information for objects, so it is necessary to distinguish the importance of different types and different information.  
The heterogeneous graph attention network (HGAT) has achieved great success in short text classification [4; 5], knowledge graph embedding [6], social event detection [7], etc. These achievements are consistent with the target of ERE, so a model based on an entity and relation heterogeneous graph attention network was proposed to fuse their semantic information to cope with overlapping relational triples.  
In this paper, a novel entity and relation heterogeneous graph attention network is imported to achieve better performance on SEO and EPO. A heterogeneous graph attention network is constructed including three types of nodes--word nodes, relation nodes, and subject nodes--to learn and enhance the embedded representation of parts for relation extraction, combined with a gating mechanism to control the updating of the nodes. The experimental results demonstrate that the ERHGA can effectively solve the relational triple extraction problem and outperform all baselines [1; 2; 3; 8; 9; 10; 11; 12; 13; 14; 15].  
The rest of this article is structured as follows: Section 2 summarizes prior research on joint ERE and HGAT. In Section 3, the problem formulation is proposed. In Section 4, we describe the methodology. The experimental details, evaluation results, and discussion are presented in Section 5. Finally, our concluding viewpoints are presented in Section 6.
## 2 Theoretical Analysis
### COP Prediction Model Based on Machine Learning
This study developed a coefficient of a performance prediction model that shows high accuracy at a low cost using the input values that are generally monitored and easy to measure during the operation of a heat pump system. The coefficient of a performance prediction model was developed using the operational data of geothermal systems and measurement data in various temperature ranges through experiments. The accuracy was improved through hyper-parameter optimization. \begin{table}
\begin{tabular}{c c c c} \hline
**Category** & **Methods** & **Input** & **Author** \\ \hline Mathematical method & Polynomial & 7 point (Heating load, etc.) & [PERSON] et al. \\ \hline \multirow{8}{*}{Machine learning} & Random Forest & 9 point (Power consumption, etc.) & [PERSON] \\  & Recurrent Neural Networks & 12 point (Environmental variable, etc.) & [PERSON] et al. \\  & ANN & 5 point (Power consumption, etc.) & [PERSON] \\  & SVM & 8 points (Heating capacity, etc.) & [PERSON] et al. \\  & Adaptive neuro fuzzy inference & 8 points (Heating capacity, etc.) & [PERSON] \\  & system & 7 point (Power consumption, etc.) & [PERSON] \\  & K-NN & 8 points (Water flow rate, etc.) & [PERSON] \\  & Random Forest & 12 point (Power consumption, etc.) & [PERSON] \\  & ANN & 29 point (Power consumption, etc.) & [PERSON] et al. \\  & ANN & 5 point (Ground temperature, etc) & [PERSON] et al. \\  & ANN & 3 point (Ground temperature, etc) & [PERSON] et al. \\  & Back-propagation Neural Network & 23 point (Water flow rate, etc) & [PERSON] et al. \\  & Random Forest and Back propagation & 16 point (Power consumption, etc.) & [PERSON] et al. \\  & Neural Network & & \\ \hline \end{tabular}
\end{table} 
Table 2: Previous studies of performance prediction.
## 4 Results and Discussion
### Theoretical and Practical Implications  
4.5.1. Theoretical Implications  
This study enhances our understanding of how the Unified Theory of Acceptance and Use of Technology (UTAUT) model can be adopted to investigate the acceptance of robots, particularly in office environments. By analyzing the links between perceived sociability and other UTAUT constructs, we improved our understanding of how perceived sociability plays a role in robot acceptance within the UTAUT framework. Furthermore, our study is among the pioneering studies empirically showing that effort expectancy, a key construct of technology acceptance within the UTAUT model, might not be significant in the domain of robot acceptance.  
4.5.2. Practical Implications  
For Robot Designers: These research results show a significant relationship between perceived sociability (PS) and behavioral intention (BI), emphasizing the importance of designing robots with advanced social capabilities. Robot developers should prioritize features that enable effective communication, emotional responsiveness, and user-friendly interactions. For example, incorporating natural language processing and adaptive learning algorithms can enhance robots' ability to engage with users in a meaningful way. Additionally, paying attention to the aesthetic design of robots to avoid the \"uncanny valley\" effect can further improve user acceptance. For example, designs should focus on incorporating human-like traits that enhance usability and approachability while maintaining distinct non-human features to set clear expectations of the robot's capabilities. Achieving this balance can improve user comfort and acceptance, particularly in office environments where frequent human-robot interactions are expected.  
For Organizations: Organizations aiming to integrate robots into office environments should create a supportive climate for adoption. This includes fostering positive social influence through management endorsement and peer support for robot use. Training programs can be designed to familiarize employees with robot functionalities and reduce apprehension. Furthermore, aligning robot capabilities with employees' performance ex pectations can enhance perceived usefulness, thereby increasing acceptance. Organizations should also consider cultural nuances when deploying robots across different regions to ensure alignment with local workplace norms and expectations.