esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 KIDE4I: A Generic Semantic-Based Task-Oriented Dialogue System  
The main goal of KIDE4I is to obtain commands that are executable by the target system, given voice instructions uttered by the user. When the instructions are not clear, or when key information required to fulfill the goal is missing, KIDE4I engages in a conversation with the user and asks for missing information. The following lines will describe in more detail the process to obtain a target-system-readable command from user information and the components at play in it.  
First, the user will perform a voice command, which will be transcribed and sent to KIDE4I. At this point, KIDE4I's dialogue manager will interpret the command by first extracting its relevant key elements according to a set of rules implemented in the key extraction component. Once these key elements have been obtained, the information on the semantic repository is exploited so as to obtain, from these key elements, the action to be sent to the target system and the necessary elements needed for that action to execute successfully (arguments), in a target-system-readable format. After processing all the information in the command, the dialogue manager checks again with the semantic repository whether all the necessary information has been obtained. In some cases, there will be information that is missing or the system will need the user to confirm certain pieces of information, and the system will require a response from the user. In those cases, the semantic repository will provide the dialogue manager the request to present to the user and the type of information expected from that response (i.e., a piece of information or a confirmation--in the form of \(yes/no\) and equivalents). After receiving the user response, and depending on the type of information to process, the dialogue manager makes use of the key element extraction component, mentioned above, or the polarity interpreter, so as to interpret pieces of information or confirmations, respectively. Once the dialogue manager checks with the semantic repository that all the necessary information has been obtained, the command to be sent to the target system is generated and sent for its execution.  
The architecture of the semantic dialogue system presented in this work and mentioned above can be seen in Figure 1. In it, the 4 main components of the dialogue system can be distinguished: the **key element extraction** component, the **polarity interpreter**, the **semantic repository** and, finally, the **dialogue manager**. These components have been developed so as to be language independent and, thus, no additional effort to adapt KIDE4I to different languages is necessary. Furthermore, each of these modules are designed to be dockerized for an easy and fast deployment in server machines. Ontology instantiations are to be made available through the RDF store Virtuoso [29], which allows to access and infer information from the instantiated data through the standard querying language for RDF SPARQL, used by KIDE4I.  
The following sections will describe in more detail each of the components and their function.
## 2 Theoretical Background
### The Theory of Modal Reduction  
The purpose of establishing a linear state space model of the mirror structure dynamics is to perform rapid matrix calculation and data interaction. The focus is on accurately characterizing the dynamic response characteristics of the mirror structure. The research problem that must be solved is how to reduce the number of extracted modes and the dynamic model to achieve a limited number of modes that can be accurately and reasonably manipulated. To analyze the frequency response, the system model must be concise enough to accurately describe its dynamic performance. To increase the calculation speed, the model should be reduced as much as possible under the premise of ensuring accuracy [15]. The finite element model of the system has many nodes, and the frequency response analysis only needs to clarify the response relationship between the input and output points of the excitation. Therefore, only the mode shapes of the input and measurement nodes are extracted, and the set of degrees of freedom of the finite element model is reduced to a set containing only the state space excitation input and the degree of freedom of the measurement response.  
After modal analysis is performed on the finite element model, the number of modes obtained is the same as the number of degrees of freedom of the system. To further simplify the calculation, some important modes need to be retained for mode reduction. The damped system transfer function is as shown in Equation (17), where m is the total number of modes of the system. Under normal circumstances, each transfer function is a superposition of single-degree-of-freedom systems and the residual of each system is determined by the appropriate input and output eigenvectors. The characteristic frequency is determined by the characteristic value. The complex frequency domain variable s needs to be set to 0 to obtain the response of the \(i\)th mode in the low-frequency DC domain, that is, the DC gain. The modes are reduced according to the magnitude of the DC gain (DC gain method), that is, the mode with a large DC gain is retained, while the mode with a small DC gain is discarded to further improve the calculation efficiency. The calculation of DC gain is shown in Equation (24), where \(s=jw_{i}=0\):  
\[\frac{z_{ji}}{F_{ki}}=\frac{z_{nij}z_{nki}}{s^{2}+2\xi_{i}w_{i}s+w_{i}^{2}}=\frac {z_{nij}z_{nki}}{w_{i}^{2}} \tag{24}\]
# Smart Tags: IoT Sensors for Monitoring the Micro-Climate of Cultural Heritage Monuments
## 2 System Architecture and Design
### Visualization Platform  
The main purpose of the Smart Tag is to inform the end-users about the micro- climate in the vicinity of the installation points and provide them the ability to monitor live weather conditions and changes. In order to achieve that, a user- friendly visualization platform has been implemented that provides all the data measured by the Smart Tag Communication Protocol. As already discussed, the acquired measurements are stored in the Smart Tag's flash memory and then published to the server on a daily basis. The communication protocol used for the data transmission is the MQTT messaging protocol. MQTT is a publish/subscribe network protocol that provides bi-directional connections between devices. The MQTT protocol has been chosen over other protocols, e.g., HTTP, because it is a lightweight solution that requires minimal network bandwidth and achieve faster throughput. Furthermore, it offers lower complexity with basic functionalities such as connect, publish, subscribe, disconnect and transfers data as a byte array, which is perfect for resource-constrained devices such as a Smart Tag. A Rabbit MQQ MQTT broker is installed on the gateway, where the data are gathered and processed. The Rabbit MQQ broker was chosen because it is open source, lightweight and easily deployed.  
The user interface is designed to present all measured data in a precise, user-friendly and simply developed way. For that purpose, the Node-RED framework is installed and used on the gateway. Node-RED is an open source programming tool for developing application interfaces and online services [21]. It provides a browser-based visual programming interface that allows the developer to use and connect nodes, instead of writing code, and as a result, it reduces the complexity of the development. Each node execute a process and all the nodes wired together form a flow, which is the whole program. The flow of the Smart Tag's UI is composed of an MQTT input node that receives the measurements from the Smart Tag, a JSON parser, a node that process the data and the output nodes that visualize each measurement. The designed flow is outlined in Figure 6.  
The Node-RED dashboard is a module that provide specific nodes for the live visualization of the data. The designed dashboard is composed of temperature, humidity and dew point gauges with the live measurements, temperature and humidity charts that visualize all measured data during a day and the Dry/Wet state of the walls. The UI is presented in Figure 7.  
Figure 6: Node-RED flow for the user interface.
## 4 Results and Discussion
### Effect of Different Extension Time Differences on the Height of the Burst Pile  
The height of the blasting pile is related to the degree of disintegration of the building after the collapse and the crushing condition of the concrete, which affects the collection, loading, and transportation of construction waste after blasting. To more intuitively study the impact of different blasting time differences on the height of the burst pile of the structure, the output of the burst pile height of each option is shown in Table 4.  
The highest point of the burst pile in each program was selected as the measurement point of the height of the burst pile. The time differences of 0.1\(-\)0.3 s and 0.9 s were selected, because the formation of the directional axis of the same location and the formation of the burst pile form is very similar. When the extension time difference is 0.1\(-\)0.3 s, the plastic hinge action time is short, and the energy accumulated in the incision closure phase is less. However, with the increase in the time difference, the energy accumulated by the plastic hinge increases. Thus, in the 0.1\(-\)0.3 s extension time difference, the height of the burst pile gradually decreases. In the extension time difference of 0.4\(-\)0.8 s, with the increase in the column extension time difference, the plastic hinge gradually formed at the beam-column node. When the time difference reaches 0.6 s, the plastic hinge sufficiently rotates, the accumulated kinetic energy is sufficient to allow the structure to touch the ground disintegration, and the pile height reaches a minimum of 6.89 m. The delay-time difference is 0.9 s, and the burst stack height is 8.72 m. This is because, when the plastic hinge action time is too long, the energy changes from kinetic energy into internal energy, and the back row of columns in the deflection of the touchdown process and the beam is still part of the steel connection. This results in a collapse along the D\(-\)E axis at the beam to support the back row of columns, which is not conducive to structural disintegration.  
\[\mathrm{h}=21.87t^{2}-31.46t+18.73 \tag{4}\]  
The fit to the height of the burst pile is shown in Figure 14, and the fit indicator is 0.9289, which is a good fit. The height of the burst pile at any time differential trend is \(\mathrm{h}=21.87t^{2}-31.46t+18.73\). When the time difference is 0.6 s, the minimum height of the burst pile is 6.89 m. By comparing the nine scenarios of the extension time differences in small height-to-width ratio reinforced concrete structures, it was found that the extension time difference is between 0.5 s and 0.8 s when the burst pile height is relatively low, and the degree of disintegration is high.
## 2 Manufacturing System Context
### Single Machine
#### 2.1.1 Milling  
[PERSON] et al. [7] found the optimal cutting condition in terms of cutting speed and feed rate in order to minimize energy consumption of a machine tool. In their work, an analytical model for the energy characterization of a computer numerical control milling center was discussed.  
In the same context, [PERSON] et al. [8] provided an energy consumption model of a machine tool required during face milling operation. In the evaluation of energy consumption, several components needed for the milling, the cutting energy and the passive phase energy were considered. The objective function is the minimization of energy consumption and the cutting speed, the feed rate and the radial depth of cut, which were considered as parameters of the optimization. Other works concerning energy consumption, defined as one of the multiple objectives of the optimization in milling achievable through the proper selection of the cutting parameters, can be found in [9; 10]. Approaching energy consumption, [PERSON] and [PERSON] [11] considered material removal rate, surface roughness and cutting energy as the optimization objectives and addressed the investigation of best parameters (spindle feed, feed rate, depth of cut and width of cut) using the weighted grey relational analysis and response surface methodology. Moreover, a Cuckoo search algorithm was applied to a multi-objective optimization model, considering the minimization of the energy footprint and of production time [12]. Another metaheuristic algorithm was used for energy optimization in the milling context [13]. In their work, [PERSON] et al. [13] developed a dual-objective optimization model, i.e., the minimization of power consumption and of processing time and used an artificial bee colony algorithm to solve the mathematical problem to find the best parameters. [PERSON] et al. [14] developed a mechanical milling power model with the aim of analyzing the main milling parameter effects on the specific milling energy, specific removing energy and energy efficiency. The main result of the study is that the lower the rotational speed, the feed rate per tooth, the axial cutting depth and the cutting width, the higher the specific removing energy. This outcome is due to the increment of idle energy. The authors argued that an increment of the milling parameters leads to a higher value of energy efficiency.
# Conceptual Framework of an Intelligent Decision Support System for Smart City Disaster Management
## 3 Methodology
### Issues and Situations Of Forest Fire Disaster  
When considering information on the direction and speed of a spreading fire, a possibility of widespread fire is found to exist. Accordingly, a rapid plan to protect nearby houses and evacuate people is required. However, a gap remains in relaying the wildfire information from the fire helicopter to the ground firefighter commander. Even if a firefighter commander decides that an emergency defense is needed, it is still necessary to go through the integrated fire protection procedure to adopt the necessary measures. In the process, damages are frequently increased because of the inappropriate propagation of information or lack of content transformation. Therefore, it is necessary to improve the information sharing system for handling forest fires.
# Viability of ABO Blood Typing with ATR-FTIR Spectroscopy
## 3 Results and Discussion
### Probabilities of Random Classification  
In order to check whether the success rate of the proposed classification tools is better than a mere random classification, it is important to compare the true positive (T+), true negative (T-), false positive (F+), and false negative (F-) ratios of the proposed methodology with those obtained in a random classification. In principle, a random classification would assign a random class to a sample, following the frequency distribution of the whole population. That is, the probability of assigning the class i to a sample (p(i)) is the same as the frequency of class i in the population (f(i)). In such a case where p(i) = f(i), it is clear that the T+ ratio is \(\sum_{i}\frac{\text{p(i)}^{2}}{\text{N}}\) where p(i) is the probability of assigning i class to a sample and N is the total number of classes. Similarly, the rate of false positives and false negatives is identical and equal to \(\frac{\sum_{i}\text{p(i)}^{\text{p(i)}}\cdot\text{p(i)}_{\text{rel}}}{\text{N}}\). These formulae can be extended for those cases where p(i) \(\
eq\) f(i), for example, if assignation into classes is performed equiprobably. Table 3 shows the T+, F+, T-, and F-ratios expected for a random classification using the probabilities shown in Table 1 (p(A) = 0.23, p(B) = 0.39, p(AB) = 0.09, p(O) = 0.29).  
\begin{table}
\begin{tabular}{c c c} \hline
**Region/cm\({}^{-1}\)** & **Region/cm\({}^{-1}\)** & **Region/cm\({}^{-1}\)** \\ \hline
910–945 & 1213–1257 & 2829–2887 \\
980–999 & 1257–1348 & 2887–2941 \\
1059–1092 & 1348–1425 & 2941–2995 \\
1092–1113 & 1425–1477 & 2995–3107 \\
1113–1140 & 1477–1583 & 3107–3224 \\
1140–1186 & 1583–1716 & 3224–3383 \\ \hline \end{tabular}
\end{table}
Table 2: FTIR selected peaks.  
Figure 1: FTIR spectra of averaged ABO type samples (**left**) and Rh samples (**right**).  
It is obvious, then, that keeping the assignation probabilities as the true distribution frequencies provides the best T+ and T- ratios. All the classification algorithms will be checked against this random distribution.
# Review of Recent Advances in QEPAS-Based Trace Gas Sensing
## 5 Few-QEPAS Sensor for Long-Distance and Distributed Gas Detection  
Usually the QEPAS sensor is applied to gas concentration measurement for one point. It is hard for it to realize distributed gas sensing. Fiber evanescent wave (FEW) technology has the advantages of anti-adverse environment, anti-electromagnetic interference, low transmission loss, flexible in operation, and on-line measurement. In 2017, [PERSON] combined the QEPAS and FEW techniques and proposed a new FEW-QEPAS method for long-distance and distributed gas sensing [76]. Due to the fact that the power of the FEW increases with decreasing diameter of the micro-nano fiber, the diameter of the tapered fiber should be small enough (<2 \(\upmu\)m) to get a strong FEW. Using the flame-brushing method, the tapered fibers were fabricated from a standard single-mode fiber (SMF). To evaluate the FEW sensor performance, a single mode fiber with a length of 3 km was used to deliver the laser, and three tapers were fabricated as sensing units. The measured diameters of the three tapered fibers  
Figure 8: Schematic of the QTF, the laser beam, and the generated acoustic wave.  
Figure 9: Calculated displacement for two different QTTs with various lengths of micro-resonators (mRs): (**a–c**) for the QTF with a 200 \(\upmu\)m gap; (**d–f**) for the standard QTF with a 300 \(\upmu\)m gap.  
were \(\sim\)1.67 \(\upmu\)m, 1.77 \(\upmu\)m, and 1.12 \(\upmu\)m, respectively. The power percentage of FEW for the three tapers was calculated by a finite element method (FEM) of COMSOL Multiphysics (COMSOL Inc., Stockholm, Sweden) and is depicted in Figure 10.  
The schematic of the experimental setup for FEW-QEPAS sensor is depicted in Figure 11. An experimental evaluation was carried out by employing a distributed feedback diode laser with emitting wavelength of 1.53 \(\upmu\)m and choosing acetylene (C\({}_{2}\)H\({}_{2}\)) as the target analyte. The detection limit of 30 ppm, 51 ppm and 13 ppm at 1 s integration time was obtained for the three tapers, respectively. The corresponding NNEA were 3.55 \(\times\) 10\({}^{-6}\), 3.52 \(\times\) 10\({}^{-6}\), and 3.66 \(\times\) 10\({}^{-6}\) cm\({}^{-1}\) W/Hz\({}^{-1/2}\), respectively. Further improvement of the performance of FEW-sensor can be achieved by using a fiber taper with a smaller diameter and lower transmission loss.
## 4 Numerical Calculation of Slopes Based on Weibull Parameter Random Distribution Model
### Slope Stability Analysis Based on Cusp Mutation Theory
#### 4.3.2 Dissipated Energy Mutation Analysis  
As shown in Figure 11, the schematic diagram illustrates the variation in the mutation characteristic values of energy dissipation, plastic zone, and displacement. During the slope excavation process, the trends in energy dissipation, plastic zone volume, and displacement collectively reflect the evolution of slope stability. The study indicates that in the early stages of excavation, energy dissipation increases gradually, the growth of the plastic zone volume is limited, and displacement changes are minimal, suggesting that the slope remains in a stable state. However, as excavation progresses, particularly at the 13 th excavation stage, energy dissipation experiences a sharp surge, the plastic zone expansion reaches a critical state, and significant displacement occurs. This suggests that the redistribution of internal stress within the slope has triggered nonlinear deformation and stress concentration. According to cusp catastrophe theory, at this stage, the mutation characteristic value \(\Delta\) shifts from positive to negative, further confirming the transition of the slope from stability to instability. Through a comprehensive analysis of these factors, it can be concluded that the rapid accumulation of energy dissipation serves as a key indicator of energy instability, the expansion of the plastic zone reflects the degradation of the slope's mechanical properties, and the abrupt increase in displacement directly signals the slope's transition into an unstable state. Together, these factors elucidate the dynamic mechanism underlying the slope's shift from stability to instability.  
Figure 10: The plastic zone, maximum shear strain contour map, and statistical curve of the plastic zone after slope excavation completion. **(a)** Plastic zone contour map of the slope excavation to the bottom of the pit; **(b)** maximum shear strain contour map of the slope excavation to the bottom of the pit; **(c)** volume monitoring curve of the plastic zone from the slope excavation to the bottom of the pit.
# Performance Evaluation of a Novel Propulsion System for the Spherical Underwater Robot (SURIII)
## 2 Mechanical Design and Analysis
### Mechanical Design and Analysis of Propulsion System
The propulsive force of the SURIII comes from vectored water-jet thrusters and the orientation of propulsive force can be controlled by the rotation angle of two servo motors. Two servo motors are rotated in two planes which are vertically intersecting, so they provide two rotational DoFs for the thruster. Figure 8 shows the rotation range of these two servo motors. From the side view of vertical motion in Figure 8a, the nozzle turns \(45^{\circ}\) counter clockwise, the robot will move down underwater. When the nozzle turns \(90^{\circ}\) clockwise, the robot will move up to the surface of water. And from the top view of forward motion in Figure 8b, the robot will move forward when the nozzle turns \(90^{\circ}\) clockwise. If servo motor 1 rotates \(180^{\circ}\) in the forward motion and then the nozzle turns \(90^{\circ}\) counter clockwise as shown in Figure 8c, the robot will change from forward motion to backward motion quickly without any change in heading. In other words, through the simultaneous action of these two servo motors, the water-jet thruster can rotate from \(-90^{\circ}\) to \(90^{\circ}\) in horizontal plane. This angle will affect the orientation of horizontal motions and the orientation can be changed directly by rotating two servo motors. Besides the down motion, all the other motions have the same orientations with the propulsive force and this can maximize the use of it.
## 4 Query Processing Algorithm  
In this paper, we focus on processing the \(K\)NN query for moving objects in the grid-based sensor network. Given a sensor \(s\) issuing the query and a value of \(K\), the \(K\)NN query retrieves the \(K\)-nearest neighbors of \(s\). We develop an efficient algorithm combined with the grid index to answer the \(K\)NN query. In the following, we first define the parameters used to prune the non-qualifying objects and then illustrate the detailed steps of the algorithm. For ease of explanation, we assume that the 1 NN query is processed (_i.e._, \(K=1\)).