esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Due to their high expectations for the quality of their housing, the residents of multi-residential buildings demand regulations regarding the levels of indoor noise such as floor impact noise, airborne noise through walls, and water supply and drainage noises. Especially in Korea, the level of this requirement is higher than those of other countries because more than 50% of people reside in multi-residential buildings with high property prices. The Korean government has consequently enacted several noise regulations regarding the allowable indoor noise level and classifications for floor impact noise, the sound insulation of walls, and transport noise.  
In contrast to the above noise regulations, water supply and drainage noises from bathrooms are evaluated by a Korean certification tool named G-SEED (Green Standard for Energy and Environmental Design) [1], which assigns different points for each tier in the evaluation of the sustainability of a building. In G-SEED, the sustainability of the water supply and drainage noises is mainly determined by whether pipes and construction methods that result in the generation of low noise levels have been applied or not, rather than the noise level.  
The noise generated from sanitary wares such as a water closet is unpleasant for residents living on lower floors. In Korea, water supply and drainage noises result in one of the main noise complaints because the drain-pipe system is installed through the penetration of a slab between the upper and lower floors in most multi-residential buildings. The penetrated vertical pipe is then connected to horizontal pipes that are installed above the ceiling. The pipe system draining the fluctuating fluid consequently comprises a vulnerable structure that transmits sound energy with structure-borne and airborne paths [2; 3; 4; 5].  
In Korea, multi-residential buildings have been built with various floor plans. Generally, the number of bathrooms is one for floor plans with two bedrooms or fewer, while more than two are built for floor plans with more than three bedrooms. In housing units with more than two bathrooms, one bathroom is generally attached to a master bedroom. The water supply and drainage noises can therefore be transmitted easily through an adjacent bedroom, which causes noise annoyances during rest periods, as well as sleep disturbances at night.  
In this study, a series of field measurements was therefore carried out to investigate the current situation regarding water supply and drainage noises in terms of the bathrooms and adjacent rooms of 14 types of floor plans for multi-residential buildings in Korea. This measurement campaign was conducted as a preliminary study for the proposal of an acoustic-design guideline so that a certain noise level can be satisfied for the water supply and drainage in the bathrooms and adjacent rooms of multi-residential buildings.
## 2 Windage Loss Model  
The previous theoretical research shows that the windage loss in the rotor-stator gap is related to flow state, medium density \(\rho\), outer radius \(R_{0}\) of disks, inner radius \(R_{i}\) and angular velocity \(\omega\). When the fluid in the gap is laminar, according to the law of [PERSON]'s inner friction, the force on the surface of disk is proportional to the relative velocity and contact area of the fluid and inversely proportional to the size of the gap:  
\[\frac{F}{A}=\mu\frac{\omega R}{S} \tag{1}\]where \(F\) is the force on rotor, N; \(A\) is the contact area between rotor and fluid, m\({}^{2}\); \(\omega\) is the angular velocity of rotor, rad\(\cdot\)s\({}^{-1}\); \(S\) is the gap size between rotor and stator, m.  
The torque of the rotor is:  
\[M_{r}=\int_{R_{\rm i}}^{R_{0}}4\pi\frac{\mu\omega R^{3}}{S}dR \tag{2}\]  
\[M_{r}=\frac{1}{2}C_{m}\rho\omega^{2}(R_{0}^{5}-R_{\rm i}^{5}) \tag{3}\]  
The corresponding equation of windage loss is as follows:  
\[W=\frac{1}{2}C_{m}\rho\omega^{3}(R_{0}^{5}-R_{\rm i}^{5}) \tag{4}\]  
where \(C_{m}\) is the skin-friction coefficient.  
When the disk rotates in an enclosed chamber, [PERSON] and [PERSON] conducted an experimental study and proposed four flow regions with a relative axial gap (\(S/R_{0}\)) and Reynolds number (\(Re\)) as characteristic parameters, as shown in Figure 1. The division principle is the flow state (laminar, turbulent) and the merging (Region III) or separation (Region IV) of boundary layers. The equation of the skin-friction coefficient \(C_{m}\) corresponding to the four regions is shown in Table 1.  
It should be noted that the constant coefficient of the skin-coefficient model of Region IV in Ref. [3] is 0.0102. Through the analysis of the cited literature and the comparison of the previous numerical calculation results of the research group, it is found that the original intention of [PERSON] and [PERSON] should be 0.102. In 1995, [PERSON] [21] studied the surface roughness of a disk. In 2000, [PERSON] [22] studied the design of centrifugal compressors. The constant coefficient was found and corrected to 0.102. The numerical results of the  
Figure 1: Flow state region division [3].  
modified model of Region IV are in good agreement with the experimental results and the relative deviations are less than 5%.
# LabConcept--A New Mobile Healthcare Platform for Standardizing Patient Results in Telemedicine
## 2 The LabConcept's Architecture
### Loading the Results  
The LabConcept application has an empty database when it is first launched. There are three options to add data to the internal database.  
The first option consists of the novelty of this telemedicine platform, which is scanning the printed QR code from the assay bulletin. As shown in Figure 2, the assay bulletin has specific blood categories with their corresponding QR codes. The application has enough built-in blood categories for home or professional use and also covers all the ranges of the existing parameters available on most blood analyzers from the market (hematology, immunoassay, coagulation, blood gas, biochemistry and urine summary).  
The second option consists of importing the stored data from the device's local drive, if a back-up was performed, or from a selected folder, if the results are sent back via e-mail by the medical doctor after the clinical interpretation was performed and the comments were added. In the second case, the database is downloaded and imported by the user in order to update it in his application. This option can be also used if the integrity of the LabConcept application is compromised and needs reinstallation and database restoration.  
The third option is another novelty in the telemedicine applications because it integrates the Laboratory Information System (LIS) protocol in the LabConcept software platform to directly receive and upload in the database the results received from the connected home use blood assays equipment, such as coagulation or glucose meters.  
The LIS protocol is a software-based solution that appeared in the 1970s because the blood results reporting was manually done, a process often riddled with transcription errors [14]. Most available blood analyzers have a LIS option which means that they can be connected to the hospital's network in order for the results to be centralized on a host computer for later assay validation and printing [15]. The LabConcept application has the ability to integrate the blood analyzers with their specific LIS protocol and if an assay is performed with the home blood analyzers, the results obtained can be transferred directly in the LabConcept's database for automatic pre-diagnostic in order to judge if there are any abnormalities regarding the user's health and to be immediately sent to the doctor via telemetry for further professional interpretation. This means that the LabConcept is a flexible application that can embed many functional options that help both patients and doctors, having a universal designation. As it is a software platform, it can be adapted to the user's needs.
## 6 Limitations of the Study and Future Work  
The proposed method was performed through a set of steps that began with extracting the features from medical images. This extraction process was achieved by using MobileNetV3. MobileNetV3 was fine-tuned on the medical image datasets to learn more complex and meaningful representations, and it helped to extract relevant feature representations. Then, the extracted features were fed to the feature selection phase, which depended on the improvement of the behavior of the new metaheuristic algorithm called the Artificial Hummingbird Algorithm. This improvement was performed by using Aquila Optimization to enhance the diversity of agents during the search process. This led to an enhancement in the convergence rate towards the optimal subset of relevant features. Due to the rapid calculation of the threshold parameters and the high reliability of the output, the AHA-AO achieved a high convergence rate, indicating that it prevented trapping in optimal solutions and was well balanced between the exploitation and exploration phases.  
Our study proposes an advanced feature selection optimization technique that improves the performance of deep learning and machine learning models. However, there are still some limitations for the proposed algorithm. The most important problems are the time and the memory. In future work, we plan on lowering the complexity and enhancing the performance of the suggested model. In addition, we will introduce an AHA-AO based on a multi-objective FS technique for higher-dimensional space with a low number of instances to enhance the classification results by reducing the number of features, allowing us to use more accurate classifiers. Furthermore, the implementation of hyper-heuristic techniques in FS could be a significant research area.
## 4 Results and Discussion
### Data Collection and ANN Results
#### 4.2.2 ANN Model Performance  
Figure 9 depicted the ANN model's performance. The performance of single and double hidden layers is depicted in the image. It was evident that the coefficient of determination (R\({}^{2}\)) for the training stage, testing stage, validation stage, and overall process was recorded as (0.99947, 0.999907, 0.99924, and 0.99936) accordingly. The R\({}^{2}\) value indicated that the database had been properly educated. Thus, the best performance for the validation stage was reported to be excellent at R\({}^{2}\) at 0.99924. This indicates that the ANN structure with a single hidden layer has a strong capacity to predict the friction coefficient under the aforementioned conditions, including the number of nodules, load, nodule diameter and sliding time. On the other hand, the determination coefficient for the ANN structure's double hidden layer was found to be 0.99841, 0.99878, 0.99902, and 0.99846 for the training, testing, and validation processes, respectively. When comparing the two structures' performances, it is obvious that the single layer structure performs better than the double hidden layer structure.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Load** & **Time** & **Number of** & **Diameter of** & **Friction** \\  & **(N)** & **(min)** & **Grains/mm\({}^{2}\)** & **Grain (\(\mu\)m)** & **Coefficient** \\ \hline
1 & 17.33 & 5 & 256 & 30.6 & 0.293 \\
2 & 17.33 & 10 & 256 & 30.6 & 0.356 \\
3 & 17.33 & 15 & 256 & 30.6 & 0.356 \\
4 & 17.33 & 20 & 256 & 30.6 & 0.356 \\
5
## 3 Results
### Testing the Prosthesis's Movement
The objective of the experiments was to analyze the behavior of the self-adjusting prosthesis to reflect its ability to compensate for a simulated leg length discrepancy (LLD). The simulation was performed on the intact limb of the mannequin by introducing standardized plates with thicknesses ranging from 1 mm to 10 mm under the plantar surface. As shown in Figure 10b, points 1 and 2 (P1 and P2) were placed on the intact limb, recording the displacement caused by the introduction of the test plate. Point 3 (P3) was positioned on the actuator designed to compensate for the displacement in order to correct the LLD, while point 4 (P4) was placed on the foot, which rests on the mannequin's support plate. The DIC system tracked and monitored the prosthesis's response in real time, highlighting its speed and efficiency in compensating for the artificially induced LLD. It provided precise insights into the actuator's ability to respond to varying degrees of displacement. The graphs were obtained using PONTOS software version 6.3.1-1. The results for the displacement generated by a simulated LLD of 1 mm and 10 mm are presented below. Figure 11 illustrates the variation in deviation along the \(Y\)-axis over time for two distinct points (P1 and P3) during a simulation of a 1 mm LLD. Stage 0 represents the starting point of the DIC measurement process. A sudden increase in the displacement curve of P1 is observed around the value of 1 mm, consistent with the introduction of the standardized 1 mm plate used to simulate the limb length discrepancy (LLD). After a short period, the curve stabilizes. For P2, positioned on the movable component of the MOPE, the curve shows a continuous increase in the initial seconds, followed by stabilization, with the maximum displacement value approaching 1 mm. The relative error with which the prosthesis compensated for the simulated LLD is 8.8%. In Figure 12, which is analogous to Figure 11, it can be observed that the prosthesis compensates for an LLD exceeding 9.2 mm when a simulated LLD is induced using a 10 mm plate. Figure 11: Displacement performed by the prosthesis for a 1 mm LLD. In this instance, the relative error value with which the mannequin's leg length discrepancy was compensated is 7.2%. As illustrated in Figure 13, the simulated LLD values (blue curve) are shown, ranging from 1 mm to 10 mm with 1 mm increments. This enables an assessment of the impact of leg length discrepancy on the measurement and compensation system. Another curve (green) illustrates the evolution of LLD compensation performed by the prosthesis. This curve demonstrates how the prosthesis automatically compensates for length discrepancies based on the simulated LLD values. This visual representation facilitates a direct comparison between the simulated LLD and the prosthesis's capacity to automatically adjust its length, with an average relative error of 9.8%.
## 3 Compliance Model of _n_-4R Compliant Parallel Pointing Mechanism
### Compliance of the \(n\)-4R Compliant Parallel Pointing Mechanism  
For the compliant parallel mechanism, the stiffness of the mechanism is the sum of the stiffness of each parallel branch [26], and the compliance matrix and stiffness matrix are mutually inverse matrices. Hence, the compliance of the \(n\)-4R compliant parallel pointing mechanism is expressed as:  
\[\mathbf{C}_{n-4\text{R}}=\left(\sum_{i=1}^{n}\left(\mathbf{C}_{Bi}^{O}\right)^{-1} \right)^{-1} \tag{6}\]  
where \(\mathbf{C}_{n-4\text{R}}\) is the overall compliance matrix of the \(n\)-4R compliant parallel pointing mechanism, and \(\mathbf{C}_{Bi}^{O}\) represents the compliance matrix of branch \(i\) (\(i=1,\,\ldots,\,n\)) in the global coordinate frame.  
Since the branches of the \(n\)-4R compliant parallel pointing mechanism are similar and evenly distributed, the compliance matrix \(\mathbf{C}_{B2}^{O}\) of branch 2 can be obtained by rotating the compliance matrix \(\mathbf{C}_{B1}^{O}\) of branch 1 by \(2\pi/n\) angle around the \(y\)-axis of the global coordinate frame _Oxyz_.  
\[\mathbf{C}_{Bi}^{O}=\mathbf{A}\mathbf{d}_{R}\mathbf{C}_{B(i-1)}^{O}\mathbf{A}\mathbf{d}_{R}^{\text{T}},\text{ where }\mathbf{A}\mathbf{d}_{R}\ =\ \begin{bmatrix}\mathbf{R}_{y,2\pi/n}&0\\ 0&\mathbf{R}_{y,2\pi/n}\end{bmatrix} \tag{7}\]  
where \(\mathbf{A}\mathbf{d}_{R}\) is the adjoint transformation matrix, \(\mathbf{R}_{y,2\pi/n}\) is the rotation matrix, which represent the rotation of \(2\pi/n\) angle around the \(y\)-axis of the global coordinate frame _Oxyz_, \(n\) (\(n\geq 3\)) is the number of branches, and \(i\) represents the \(i\)-th branch, \(i=2,\,\ldots,n\).
## 5 Simulation Results
Figure 7: The delivery time of the first message versus the distance between the vehicle and the EV at the time when the first message was received in (**a**) the Zilina scenario and in (**b**) the Bratislava scenario. In simulation, after receiving the first message informing about the EV approaching, vehicles change lane and continue driving with unchanged desired velocity. From the simulation, the available reaction time was evaluated as the difference between the time when the vehicle received the first message and the time when the EV overtook the vehicle. If in the simulation the EV did not manage to overtake the vehicle, then the value of the reaction time is not reported. All three values, the lower and upper estimates and the value of the available reaction time measured from the simulation are presented in Figure 8.  
The linear dependency between the available reaction time and the distance from the EV to the vehicle, for the values of estimated reaction time (lower and upper bounds) indicates that the communication time takes only a small part of the reaction time. Hence, the PEEV-WS successfully manages to communicate the information about the approaching EV to the drivers. If the distance between an EV and the vehicle exceeds 300 m, the reaction time exceeds 25 s, which provides vehicle drivers with sufficient time to take an appropriate action. In the Bratislava scenario, the free flow of vehicles is interrupted by several traffic-light-controlled intersections. The vehicles which received PEEV-WS request message changed to the rightmost driving lane. However, they could not continue their route as the rightmost lane is a turning lane. Hence vehicles stop at the intersection and wait until the EV safely overtakes them. This behavior can be observed in Figure 8b as groups of points formed at various distances and have very similar reaction times.
# Dynamic High-Type Interval Type-2 Fuzzy Logic Control for Photoelectric Tracking System
## 1 Introduction
The feature of T2 FLS is to fuzzify the membership value in the fuzzy set, which enhances the ambiguity of the set, thereby improving its ability to deal with uncertainties [23]. The experimental results show that in high uncertainties situations, T2 FLS has significantly better performance than the corresponding T1 FLS, and the higher the degree of uncertainties, the more obvious this advantage is [24]. The defuzzification process of T2 FLC requires the use of type-reduction (TR) algorithms. Commonly used algorithms are EKM and EIASC based on iterative calculations [25; 26; 27], and NT algorithms that do not require iteration [28]. The TR algorithm based on iterative calculation has high calculation accuracy, but the calculation speed is slow; while algorithms such as NT that do not require iteration have fast calculation speed and sufficient accuracy to meet system requirements, so they are more suitable for real-time systems [29]. To further improve the speed and accuracy of the NT algorithm, this paper proposes a WTNT algorithm based on the principle of numerical integration. To reduce the dependence of the fuzzy controller on human experience, the multi-group genetic algorithm (MPGA) is used to optimize the FLC parameters. The main contributions of this paper can be summarized as follows:A DHT-IT2 FLC system is constructed to realize dynamic high-type control. As a judging mechanism, the FLC changes the system type in real time according to the system status, which not only achieves the purpose of improving the steady-state accuracy and speeding up the response speed, but also avoids the hidden dangers of system oscillation and integral saturation caused by the increase of type. 2. Compared with the traditional controller and the T1 FLC, the IT2 FLC can significantly improve the ability of the system to handle uncertainty, make the system run more smoothly and stably, and enhance the robustness of the system. 3. Aiming at the TR problem of T2 FLC in real-time system applications, the improved WTNT TR algorithm is proposed to reduce computational complexity. By comparison, WTNT has faster response speed and calculation accuracy than EKM and EIASC in this project. The structure of this paper is as follows. Section 2 systematically introduces the background knowledge of DHTC, FLC, and MPGA; Section 3 introduces the design of the control system; Section 4 presents the experimental results, and make conclusions in Section 5.
# Transformation-Based Fuzzy Rule Interpolation Using Interval Type-2 Fuzzy Sets
## 4 Experimentation and Discussion
### Case 2  
This example case concerns an interpolation involving singleton-valued conditions using rules with multiple antecedent variables. In particular, two rules each involving two antecedents \(\bar{A}_{11}\wedge\bar{A}_{12}\Rightarrow\bar{B}_{1}\), \(\bar{A}_{21}\wedge\bar{A}_{22}\Rightarrow\bar{B}_{2}\) and the observation \(\{\bar{A}^{*}_{1}\), \(\bar{A}^{*}_{2}\}\) are given in order to illustrate the interpolative process in which the result \(\bar{B}^{*}_{c}\) is determined. All of the conditions are listed in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline Rule 1 & \(\bar{A}_{1}=<(1,3.5,4;1),(0,3.5,5;1)>\) \\  & \(\bar{B}_{1}=<(15,2,3;1),(0,2,5;1)>\) \\ \hline Rule 2 & \(\bar{A}_{2}=<(12,13,13.5;1),(11,13,14;1)>\) \\  & \(\bar{B}_{2}=<(11,11.5,12;1),(10,11.5,13;1)>\) \\ \hline Observation & \(\bar{A}^{*}=<(6.5,8,9.5;1),(6,8,10;1)>\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Involved interval type-2 fuzzy sets for Case 1.  
Figure 4: Two single-antecedent rules interpolation with identical normal points.  
The interpolated conclusion \(\bar{B}_{c}^{*}=<(4.35,5.68,6.71;0.54),(3.87,5.68,7.15;1)>\) is shown in Figure 5. It follows that if certain components involved in the given rules are singleton-valued, the interpolated conclusion remains an interval type-2 fuzzy sets.