esa-sceva/satcom-chunk-collection huggingface dataset:
# Intent Classification and Slot Filling Model for In-Vehicle Services in Korean
## 5 Discussion
### Error Analysis  
To deeply understand the errors in our models, we analyze the inference examples from the value prediction procedure. We categorize them into two types of errors. One is the situation where the model misses out on the address name units as in Table 7. The Korean address system is divided into three hierarchies: -daero, -ro, and -gil [41]. -daero is designated for a road with a width of 40 m or more than eight lanes. -ro is roads or streets smaller than daero. These roads are between eight and two lanes. -gill is typically the smallest road having only one lane. That is, the road name and the corresponding hierarchy appear together in the sentence but should be interpreted separately. Due to the characteristic of our mobility domain, examples related to these addresses occupy a larger proportion compared to other domain datasets. For this reason, we find that the model becomes more confused and shows errors in omitting the address hierarchy.  
The other occurs when the model predicts the slot value without omitting the postposition from the utterance as in Table 8. Korean has a linguistic characteristic that is different from English. In Korean, when a root word and a postposition are combined, the word has a specific meaning in the sentence [42]. Postposition includes (eun), (i), (j), (ga), (leul), and (ui). That is, the root word has different meanings in the sentence depending on the postposition [43]. For example, if (eun) is used as a postposition, the root word has the meaning of the subject in the sentence, and when used with (leul), it has the meaning of the object. Therefore, it is important to distinguish the postposition from the root word [42] as the meaning of the slot value also depends on the postposition. Therefore, we find that this error occurs since it learns the meaning of the slot value that depends on the postposition in the sentence.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**PP Type** & **KoBERT** & **KLUE-RoBERTa** & **mBERT** \\ \hline DM + VM & **86.55** & **86.42** & **90.74** \\ DM & 85.91 & 84.82 & 90.54 \\ VM & 83.51 & 83.35 & 87.66 \\ - & 76.95 & 80.55 & 86.38 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation Study on value-refiner. DM indicates the Database-Matching module, and VM is Value-Matching module. The number in the table is the result of joint goal accuracy metric.
## 5 Conclusions  
1. In response to the actual problem of water inrush accidents from the through-type fault floor, a survey of water inrush accidents from faults was conducted in the Fengfeng Mining Area, and three types of water inrush and four water inrush modes were systematically divided. Based on the three types of water inrush, combined with the relative position relationship between coal seams and aquifers, and the spatial combination relationship between water inrush sources and water inrush channels, these were further divided into 6 subcategories and 17 fine categories.
2. A micro individual evaluation model for the risk of water inrush from through-type faults was constructed using the grey correlation degree and the Analytic Hierarchy Process (AHP). The evaluation showed that the hydrogeological conditions and mining conditions belong to level IV, while the fault conditions are level II. The comprehensive evaluation of the water inrush risk level of the through-type fault  
Figure 13: Cloud Chart of Pore Water Pressure Distribution in Coal Seam Floor. (**a**) Before mining the working face, (**b**) working face advancing 100 m, (**c**) working face advancing 250 m, (**d**) working face advancing 400 m, (**e**) working face advancing 500 m, (**f**) working face advancing 625 m.  
zone is level I, and the evaluation results are reasonable, verifying the actual water inrush accident.
3. Numerical simulation was used to study the spatial failure mechanism of intact rock layers on the floor and progressive intrusion zones of faults. The process of water inrush can be summarized as follows: the rupture and instability of the floor water barrier fracture produces the ore pressure failure zone; a progressive intrusion zone develops--the mining pressure failure zone and the progressive intrusion zone are connected with the water inrush; the occurrence of a floor lag flood disaster. When mining 625 m, the stress within a 20 m range of the front end of the working face reaches the peak value of 38.67 MPa, and the maximum seepage velocity peak value appears on the left side of the F\({}_{55-1}\) fault. Under the combined action of mine pressure and confined water pressure, the height of confined water conductivity significantly increases.
4. Finally, a three-level identification of the risk of water inrush from the through-type floor was formed, which includes \"macroscopic system type classification, microscopic individual risk assessment, and simulation analysis of mining damage utility\", revealing the characteristics and mechanisms of the water inrush mode of the through-type fault.
# Design of Remote Upgrade System for Data Processing Unit in Marine Engine Room Simulator
## 3 Description of Simulator  
The system is designed for the DPU in the marine engine room simulator. It has been widely applied in maritime education [16]. Figure 1 is the typical layout of the marine engine room simulator. It includes a number of semi-physical simulation equipment as shown in Figure 2 and an M-side running on a computer as shown in Figure 3. In the simulator simulation system, the higher computer is mainly the M-side. It is a virtual software developed in the laboratory that is capable of highly modeling the various devices in the cabin of a ship's vessel. Its simulation is based on the mother ship's marine system, including the main engine system, main generator system, boiler system, oil and water separation system, and other subsystems. The lower computer is mainly composed of semi-physical simulation control boxes, consoles, and other simulated cabin equipment. Moreover, they have a high degree of likeness and operability to marine engine room equipment and are effective for teaching and evaluating seafarers' competencies [17].  
Figure 1: Typical layout of marine engine room simulator.  
Figure 2: Semi-physical simulation equipment.  
The DPU is the key for synchronizing information between the upper and lower computer [18]. The data of semi-physical simulation equipment is collected through the DPU and transmitted to the upper computer via Ethernet. Action commands from the upper computer are also transmitted to the DPU via Ethernet. Through its processing, the action commands are synchronized and reflected in the semi-physical simulation equipment based on the electrical reaction.  
The marine engine room simulator has to be linked to the LAN through the network interface in order to exchange data with the top computer. Once connectivity has been established, the simulator may be utilized for maritime instruction and competency evaluation. The DPU can access the Web server when the LAN is linked to the Internet.
# Generation of Computational 3D Models of Human Bones Based on STL Data and CAD Software Packages
## 2 Working Condition  
In order to perform a finite element analysis, the used CAD model must be of solid type; thus, an STL model obtained through 3D or computer tomography (CT) scanning must be converted into a solid 3D model. To do this, in this article, we used two software packages: CATIA and Creo. CATIA is one of the most widely used with integrated CAD/CAM/CAE software, with applications in various fields of the industry. The integrated solutions CAD/CAM/CAE/PLM of the Creo software package offer complete functionality for all stages of product development, from conception to production.  
Today, 3D modeling is the key to creating innovative products, allowing engineers to develop the idea of a concept that can be turned into a physical product [13; 14; 15]. These studies used CATIA Version 5 and Creo Parametric 2.0 Version. For the FEA, a Creo module and Simulate Version 2.0 were used with three-dimensional tetrahedral type elements employing high-order interpolation functions. Even though there are new releases of the above-mentioned software, the use of standard functions does not influence the capabilities of the described methodology. The workstation used for both the conversion/modeling and for FEA is an HP WorkStation xw8600, 2 xCPU Intel Xeon E5420, 16 GB RAM, Nvidia Quadro FX4600 video card, running the operating system Windows7 Professional.  
The analyzed STL files contain data obtained by computer tomography (CT), including the 3D models of the human orthopedic system, namely: the upper part of the right femur (Figure 0(a)) and left zygomatic bone (Figure 0(b)). As can be seen in Figure 1, the two models have specific anatomical shapes, with very high complexity.  
For converting the 3D models (STL type), specific functions of CAD software (offset--CATIA and Shrinkwrap--Creo Parametric) will be used. These functions generate complex 3D solid models that are similar to the STL model. The advantages of using these functions comprise the speed at which the solid model can be obtained, and their ease of use (a high-level of modeling experience is not necessary). The major disadvantage is their sensitivity to the existence of voids (non-covered areas) in the STL model that make the conversion impossible or at a poor quality, resulting in an unusable solid. To avoid this disadvantage, this article proposes a third method that involves obtaining the solid models based on STL through geometric modeling using sketches in specific sections of the analyzed models.
# A Hybrid IVFF-AHP and Deep Reinforcement Learning Framework for an ATM Location and Routing Problem
## 3 The Proposed Methodology
### Fermatean Fuzzy Analytic Hierarchy Process  
Fuzzy logic is an effective strategy for process control when the information is complex, nonlinear, and challenging to model and when the nature of the knowledge is ambiguous or uncertain. To deal with fuzziness and uncertainty in information, various fuzzy sets can be used to create linguistic expressions [27]. As a result, [PERSON] generalized the fuzzy set theory and established the intuitionistic fuzzy (IF) set theory [28]. An element in IF sets is defined using the non-membership function \(v\left(x\right)\in\left[0,1\right]\) and the membership function \(u\left(x\right)\in\left[0,1\right]\), subject to the constraint \(u\left(x\right)+v\left(x\right)\ \leq 1\). [PERSON] defined Pythagorean fuzzy (PF) sets developed from IF sets. In PF sets, the total of the membership and non-membership degrees can be more than 1, but their square sums are not allowed to be greater than 1 [29; 30]:  
\[0\leq\mu_{\widetilde{F}}(x)^{2}+v_{\widetilde{F}}(x)^{2}\leq 1 \tag{1}\]  
[PERSON] and [PERSON] defined the Fermatean fuzzy (FF) set as an extension of IF and PF sets [31; 32]. This set can be regarded as a new technique to express unreliable, unclear, and uncertain information in a fuzzy environment [33]. In Equation (2), an FF number in fixed set X is represented as \(\widetilde{F}\)[32]. \[\widetilde{F}\cong\left\{x,\ \mu_{\widetilde{F}}(x),\ v_{\widetilde{F}}(x);xeX\right\} \tag{2}\]  
where \(\mu_{\widetilde{F}}(x)\) and \(v_{\widetilde{F}}(x)\) denote the Pythagorean membership and non-membership degrees of an element \(x\), respectively. Compared to IF sets, this condition allows for more detailed uncertainty modeling, especially when the hesitation component is present. The degrees of membership and non-membership of the element \(x\in X\) to \(\widetilde{F}\) are defined by \(\mu_{\widetilde{F}}(x):X\rightarrow\left[0,1\right]\) and \(v_{\widetilde{F}}(x):X\rightarrow\left[0,1\right].\) Equation (3) defines the main constraint of Fermatean Fuzzy sets and ensures that the sum of the cubes of membership and non-membership degrees remains between 0 and 1. \[0\leq\mu_{\widetilde{F}}(x)^{3}+v_{\widetilde{F}}(x)^{3}\leq 1 \tag{3}\]  
The degree of uncertainty is calculated using Equation (4) and represents the portion of uncertainty that cannot be determined as either membership or non-membership. A higher uncertainty value indicates greater indecision or uncertainty in expert assessments. The degree of hesitancy is determined:  
\[\pi_{\widetilde{F}}(x)=\sqrt[3]{1-\left(\mu_{\widetilde{F}}(x)\right)^{3}- \left(v_{\widetilde{F}}(x)\right)^{3}} \tag{4}\]  
Furthermore, for each x \(\epsilon\) X, \(\mu_{\widetilde{F}}(x)\) and \(v_{\widetilde{F}}(x)\) are closed intervals, and their lower and upper limits are represented by \(\mu_{\widetilde{F}}^{\cdot L}(x)\), \(\mu_{\widetilde{F}}^{\cdot U}(x)\), \(v_{\widetilde{F}}^{\cdot L}(x)\), and \(v_{\widetilde{F}}^{\cdot U}(x)\), respectively.
# A Coupled Darcy-Forchheimer Flow Model in Fractured Porous Media
## 4 Validation  
The two selected examples were reported by [24]. The two models were rectangular regions with a size of 2 \(\times\) 1 m. Model 1 contained four fractures with an aperture of 0.01 m, which intersected at one point \(T_{1}\). Model 2 contains five fractures with an aperture of 0.01 m, which intersect at points \(T_{2}\) and \(T_{3}\). The permeability coefficient \(k_{m}\) of porous area is \(10^{-9}\) m\({}^{2}\), and the permeability coefficient of fracture is \(10^{-7}\) m\({}^{2}\). The boundary conditions are as follows: the water pressure is set as \(10^{6}\) and \(0\) Pa at the left and right boundaries of the porous region, \(10^{6}\) and \(0\) Pa at the upper and lower boundaries of the fracture, and the other boundaries are flow-free boundaries, as shown in Figure 3a,b. The fluid has a density of 1000 kg/m\({}^{3}\) and a viscosity coefficient of 10 \(\times\) 10\({}^{-3}\) Pa\(\cdot\)s. To illustrate the accuracy of the proposed method, error index \(e\) is used to represent the difference between the new method and the solutions of [24],  
\[e= \frac{\sum(p_{\text{New}}-p_{\text{F}})^{2}}{\sum(p_{\text{F}})^{2}} \times 100\% \tag{19}\]  
where \(p\) is the pressure distribution calculated by the new method, and \(p_{\text{F}}\) is the pressure distribution calculated by [24]. Firstly, the pressure distribution of the case with a nonlinear  
Figure 2: Flowchart of coupling Darcy–Forchheimer flow in fractured porous media.  
parameter \(\beta\) = 10 and a mesh size of 0.02 m is simulated. Figure 4 shows the pressure distribution and fracture velocity distribution calculated by the new method. The calculated pressure distribution and fracture velocity distribution are consistent with the calculated results of [24].  
Figure 4: Pressure distribution of two cases.  
Figure 3: Geometry model of intersecting fracture cases.  
In addition, the influence of different mesh sizes on the accuracy of the pressure solution was investigated. Figure 5 shows the relationship between mesh size \(h\) and the calculation error index \(e\) of the new method. It can be seen that for the mesh size range investigated, the calculation error index \(e\) decreases from 12% to 0.6% with decreasing mesh size. When the mesh size is less than 0.01 m, the error index is less than 1%, which indicates that the new method can describe the nonlinear seepage behavior in the dual media.
# Research on Kinematic Parameters of Multiple Gait Pattern Transitions
###### Abstract  
Gait recognition technology is the key technology in the field of exoskeletons. In the current research of gait recognition technology, there is less focus on the recognition of the transition between gait patterns. This study aims to determine which kinematic parameters have significant differences in the transitions (between level and stair walking and between level and ramp walking) of different gait patterns, to determine whether these parameters change differently in different gait pattern transitions, and the order the significant differences occur through a comparative analysis of various kinematic parameters between the transition stride and the before stride in the former pattern. We analyzed 18 parameters concerning both lower limbs and trunk. We compared each time point of the transition strides to the corresponding time points of the before stride using a series of two-sample \(t\)-tests, and we then evaluated the difference between the transition stride and the before stride based upon the number of time points within the gait cycle that were statistically different. We found that the sagittal plane angular velocity and the angular acceleration of all joints and the resultant velocity of the thigh and shank of the leading limb had significant differences in the process of transition; the sagittal plane angular velocity of all joints of the trailing limb and the velocity of the trunk in the coronary axis direction also showed a significant difference. The angular acceleration of all joints, the sagittal plane angular velocity of the ankle joint of the leading limb, and the acceleration of the trunk in the coronal axis direction showed a difference in the early stage of the transition. In general, the leading limb had a significant difference earlier than the trailing limb, and the acceleration parameters changed earlier than the velocity parameters. These parameters showed different combinations of changes in the transition of different gait patterns, and the changes in these parameters reflected different gait pattern transitions. Therefore, we believe that the results of this study can provide a reference for the gait pattern transition recognition of wearable exoskeletons.  
exoskeleton; kinematic parameters; gait pattern transition; recognition +
Footnote †: journal: Applied Sciences
## 5 Practical Issues
### LoRa
**Satellite:** This communication is another possible alternative to LoRa, with the added advantage of having a limitless range, as network traffic is handled by satellites. It means that if two communicating UAVs have satellite connections, they can communicate with each other, providing reasonable data rates and making it a suitable solution for IoD. However, there are also some concerns with this technology, such as its high cost and dependency on service providers, vulnerability to signal interference, high power consumption, and high latency.
## 2 Unit Cell Design  
Figure 1 depicts the unit cell design. The ultimate design of this cell contains four layers. The top layer is a SiO\({}_{2}\) anti-reflection (AR) coating, and the bottom metal layer is tungsten (Tu), which acts as a reflector. There are two layers of SiO\({}_{2}\) in between the AR and the reflective layer. One is the intermediate layer between the resonator and the ground, and the other is wrapped around the tungsten resonator. The final layer thicknesses are h1 = 105 nm, h2 = 12.5, h3 = 68, h4 = 21, respectively, resulting in a total thickness of 206.5 nm. The width of the proposed unit cell is W1 = 200 nm. Table 1 contains detailed structural information of MA. Simulation of the proposed MA unit cell was performed with CST Studio Suite [42]. Periodic boundary conditions have been applied for sidewall, where two floquet ports were applied at the top and bottom of the structure. Tungsten is substantially more dependable for a broad frequency band in the optical range and infrared range application than other commonly used metals in MA. At the same time, the use of tungsten can greatly reduce the production cost compared to the previous studies. For a typical sample of tungsten, the refractive index and excitation coefficient at 632.8 nm or mid optical window band are 3.63/39 and 2.916877, respectively. On the other side of SiO\({}_{2}\), the refractive index and excitation coefficient at 632.8 nm are 1.45704 and 0, respectively. Figure 2 shows the refractive index and excitation coefficient of tungsten and SiO\({}_{2}\) in the optical and infrared range.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbole** & **Parameter Value (nm)** & **Symbole** & **Parameter Value (nm)** \\ \hline W1 & 200 & h2 & 12.5 \\ W2 & 157.20 & h3 & 68 \\ W3 & 31.5 & h4 & 21 \\ h1 & 105 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structural parameters of the MA unit cell.  
Figure 1: The design of the unit cell (**a**) perspective view (**b**) cross-section (**c**) internal structure and layer constituents.  
Figure 2: The refractive index and excitation coefficient of (**a**) tungsten and (**b**) SiO\({}_{2}\) in the optical and infrared range.  
Later in this paper, different metals and media are compared. As the bottom layer comprises tungsten, the transmission of light is prevented. Therefore, the formula for absorption is represented by Equation (1) [29].  
\[A=1-R \tag{1}\]  
where \(A\) is absorption and \(R\) is reflectance. In Figure 2, the optical parameters of Tungsten (Tu), and SiO\({}_{2}\) are illustrated, obtained from [43; 44; 45]. For the vertical incident of electromagnetic wave, TM mode was used as the proceeding EM wave across the negative \(z\)-axis, and polarization of the proceeding light was aimed across the \(x\)-axis.
# Optical Cable Lifespan Prediction Method Based on Autoformer
## 3 Related Work
### Calculation Method for Optical Cable Load Ratio  
Cable load indicates the weight borne by the cable per unit length, holding immense significance in cable design and maintenance. Before considering external loads, it is crucial to account for the cable's self-weight as it contributes to the overall stress on the cable [16]. The formula to calculate the self-weight load ratio \(g_{1}\) is  
\[g_{1}=(G_{1}/S)\times 10^{-3}, \tag{10}\]  
where \(g_{1}\) represents the cable's self-weight load, \(G_{1}\) is the cable's self-weight, and \(S\) denotes the cable's cross-sectional area. The factor \(10^{-3}\) is used to convert the units of self-weight (typically in kg/m) into a consistent unit (e.g., N/mm\({}^{2}\)) for load ratio calculation.  
This calculation necessitates a meticulous consideration of factors such as lightning strikes, wind force, and temperature [16]. The detailed steps for the cable load calculation are as follows:  
1. Lightning Strike Assessment: During a lightning strike, determining whether the current surpasses the cable's capacity is crucial. Exceeding this limit leads to immediate cable interruption and retirement. If the current remains below the maximum carrying capacity, the optical cable will not experience direct interruption, yet it will still be subject to the aforementioned self-weight load and other external loads.
2. Wind Force Influence Calculation: When wind affects the optical cable, the calculation of the combined impact of the self-weight ratio and wind pressure ratio becomes essential. Specifically, the formula for wind pressure ratio is  
\[g_{2}=0.6125 aCv^{2}\times 10^{-3}/S, \tag{11}\]  
where \(g_{2}\) denotes the cable's wind pressure load, \(a\) represents the turbulence intensity of wind speed, \(C\) is the aerodynamic coefficient, \(d\) signifies the cable diameter, \(v\) stands for wind speed, and \(S\) signifies the cable's cross-sectional area. The factor \(10^{-3}\) is used for unit conversion, similar to Equation (10).  
The total cable load (\(g_{all}\)) can be determined by inserting the self-weight load (\(g_{1}\)) and wind pressure load (\(g_{2}\)) into the formula:  
\[g_{all}=\sqrt{g_{1}^{2}+g_{2}^{2}}, \tag{12}\]