esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### Analysis of the Classification Results  
The results presented in Table 3 show different accuracy results depending on the classifier used and the subcorpus included to train the classifiers. Focusing on SVM, which is the most accurate classifier, the best results were obtained with the subcorpus C1, and C1+C2 subcorpora. However, the classifier trained with C3 subcorpus presented the worst results. The C3 subcorpus included more speakers than the other subcorpora, but much fewer samples of each speaker. This result indicates that the sample size is more important than the number of speakers to obtain a better classification accuracy.  
In the PRADIA video game, it is very important to avoid evaluating as wrong a correct utterance, otherwise, frustration may arise. This is even more important when individuals with Down syndrome are the players, since they can be particularly susceptible to this feeling [4]. Bearing in mind that the video game aims to engage and motivate the users, the percentage of false negatives must be as low as possible. Table 5 shows that only 10.1% of the samples evaluated as Right by the expert are classified as Wrong by the classifier.  
An additional strength of the classifier developed here arises when comparing it with prior work on automatic assessment of disordered speech, such as aphasic speech [44] or dysarthric speech [45], where prosodic features and pronunciation scores are combined. In our study, instead, prosody was assessed by leaving aside the well-known difficulties of pronunciation of individuals with Down syndrome. Regardless of their intelligibility problems, prosody alone makes the speech of these individuals sound atypical [39]. Therefore, the development of an automatic speech assessment only focused on prosody in Down syndrome represents a relevant contribution. In addition, in the context of the PRADIA video game used in this study, the development of an automatic prosody assessment system in which pronunciation problems are not considered is important. Thus, for this video game to become a valuable prosody self-learning tool, communicative skills that provide an appropriate handling of prosody (production and distinction of sentence modalities and accents, among others) need to be prioritized.
## 4 Field Test Results and Discussions
Dual-frequency GNSS receivers (such as the BCM47755 chipset) in the Xiaomi Mi8 and Samsung S21 Ultra help to mitigate noise and multipath errors. These receivers utilize signals from multiple frequencies (L1/E1 and L5/E5), which enhance the accuracy and reduce errors caused by atmospheric conditions and signal reflections. The IGG-III Weighting algorithm helps to detect and remove outliers by adjusting the weights based on the residuals, effectively mitigating the impact of noise and multipath errors in the data. Observations with larger residuals receive reduced weights or are discarded, which helps to maintain the accuracy. The fusion method reduces the error to \(\pm\)10 m for both devices, suggesting that the antennae are able to provide sufficiently clean data that, when combined with other sensor inputs, result in much improved positional accuracy. The relatively small errors observed with the RTK mode suggest that both smartphones are equipped with antennae designed to minimize noise and maximize the capture of the direct signal from satellites, which is crucial for the RTK's error correction algorithms to work effectively. Table 4 shows the results of the ambiguity resolution. The ambiguity resolution process was tested using the Mi8 and S21 Ultra smartphones. The LAMBDA method and ratio test were employed to resolve carrier-phase ambiguities. The success rate indicates the percentage of epochs where ambiguities were correctly resolved, while the RMS error shows the residual error in meters after resolution. The \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Measurement** & **Success Rate (\%)** & **RMS Error (m)** & **Note** \\ \hline Mi8 & 95.2 & 0.03 & High consistency achieved \\ \hline S21 Ultra & 96.0 & 0.02 & Consistent with Doppler and TDCP \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ambiguity resolution results of the smartphones. Figure 9: (**a**) Satellite sky plot from the Samsung S21 Ultra, and (**b**) satellite sky plot from the Xiaomi Mi8, including observations from the multi-GNSS in the first part of the operation. results demonstrate high reliability and consistency across smartphones, validating the effectiveness of the proposed method.
# Volatile Organic Compounds in Breads Prepared with Different Sourdoughs
## 2 Materials and Methods
### Determination of Organic Acids in Sourdough and Bread  
The extraction of organic acids (lactic, succinic, acetic, and malic acids) in sourdoughs was performed following the method of [PERSON] et al. [18], with modifications. Briefly, the sourdough was diluted (1:10) with water and centrifuged at 8000\(\times\)\(g\) for 15 min. The supernatant was filtered through a 0.22 \(\upmu\)m membrane filter and injected in high-pressure liquid chromatography (HPLC).  
The extraction of organic acids from bread was carried out according to the method of [PERSON] et al. [19] with modifications. Briefly, 10 g of sample was added to 100 mL of ultrapure water, and the mixture was homogenized and centrifuged at 8000\(\times\)\(g\) for 15 min. The supernatant was filtered through a 0.22 \(\upmu\)m membrane filter, diluted with 3 mL of water, and injected into HPLC.  
HPLC analysis was carried out with an Agilent 1100 series system equipped with a Spherisorb S5 ODS2 column (5 \(\upmu\)m, 4.6 \(\times\) 250 mm) and a photo diode array detector. The mobile phase was composed of ultrapure water acidified with phosphoric acid at pH 2.1 under isocratic conditions at 25 \({}^{\circ}\)C. The flow was set at 0.6 mL/min. The wavelength of the detector was set at 210 nm. The organic acid concentration was determined with a calibration curve constructed with organic acid standards (acetic acid, lactic acid, citric acid, and succinic acid).  
The range of linearity and the square of the correlation coefficient (R\({}^{2}\)) for lactic acid were 30-2000 ppm and 0.99, respectively, while the range of linearity of acetic, citric, and succinic acid was 10-100 ppm, and the correlation coefficient (R\({}^{2}\)) was 0.99, 1, and 0.99, respectively.  
The results were expressed as mg of organic acid/kg of sample.
# Parasitic Motions of 3-PRS Parallel Mechanisms with Two Different Branch Chain Arrangements
## 2 3-PRS PM I  
### 3-PRS PM I Modeling  
The 3-PRS PM includes a moving platform \(S_{1}S_{2}S_{3}\), a fixed platform \(B_{1}B_{2}B_{3}\), and three PRS branch chains. The prismatic joint is actuated by a linear actuator. The axis of the revolute joint \(R_{i}\) (\(i=1,2,3\)) is perpendicular to the axis of the prismatic joint \(P_{i}\). To facilitate the design and analysis of 3-PRS PMs, the fixed platform \(B_{1}B_{2}B_{3}\) is set as an equilateral triangle with circumradius \(R\) and a central point \(O_{\text{B}}\), and the moving platform \(S_{1}S_{2}S_{3}\) is also an equilateral triangle with circumradius \(r\) and a central point \(O_{\text{m}}\). The fixed platform and the moving platform are connected by three PRS branch chains. The spherical pair \(S_{i}\) is evenly distributed \(120^{\circ}\) on the moving platform \(S_{1}S_{2}S_{3}\). This 3-PRS PM is called a 3-PRS PM I in this paper. The model is shown in Figure 1.  
As shown in Figure 2, the fixed coordinate system \(O_{\text{B}}\)-\(XYZ\) (\([O_{\text{B}}]\) for short) is connected to the origin \(O_{\text{B}}\) of the fixed platform \(B_{1}B_{2}B_{3}\). The direction of the \(Y\) axis is from \(O_{\text{B}}\) to \(B_{3}\). The direction of the \(X\) axis is parallel to \(B_{1}B_{2}\). The \(Z\) axis is perpendicular to the plane \(B_{1}B_{2}B_{3}\) and the direction is upward.  
Figure 1: 3-PRS PM I.  
Figure 2: Schematic diagram of 3-PRS PM I.  
The moving coordinate system \(O_{\text{m}}\)-\(xyz\) (\(\{O_{\text{m}}\}\) for short) is connected to the origin \(O_{\text{m}}\) of the moving platform \(S_{1}S_{2}S_{3}\). The direction of the \(y\) axis is from \(O_{\text{m}}\) to \(S_{3}\). The direction of the \(x\) axis is parallel to \(S_{1}S_{2}\). The \(z\) axis is perpendicular to the plane \(S_{1}S_{2}S_{3}\) and the direction is upward.
# On the Efficacy of Turbulence Modelling for Sloshing
## 3 Methodology
A summary of the mesh parameters is presented in Table 1. The number of cells in each direction is selected to obtain an aspect ratio of 1, and the total number of cells used is around 76,800, 259,200 and 512,000 for Mesh-1, Mesh-2 and Mesh-3, respectively. In the current work, multiple cases with different excitation amplitudes and turbulence models are discussed. However, due to the computational cost of the simulations, especially with the fine mesh, a full mesh-dependency study for all the cases permutations is not feasible within this study. A representative case with the highest excitation, \(Fr=1.85\), is therefore considered here, with two models \(k\)-\(\omega\) SST and no-model. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & \(n_{\pi}\times n_{y}\times n_{z}\) & **Number of Cells** & \(\Delta x\times\Delta y\times\Delta z\) \\ \hline Mesh-1 & \(80\times 40\times 24\) & 76,800 & \(1.25^{3}\) mm\({}^{3}\) \\ Mesh-2 & \(120\times 60\times 36\) & 259,200 & \(0.83^{3}\) mm\({}^{3}\) \\ Mesh-3 & \(160\times 80\times 48\) & 512,000 & \(0.56^{3}\) mm\({}^{3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the mesh parameters used. Figure 2 shows the damping force as a function of the displacement, obtained by the three mesh resolutions both with and without a turbulence model. It should be noted that the fine mesh run is prohibitively expensive compared to the others and, therefore, due to limited resources within the context of this study, the run is ended at T = 5 instead of T = 20. This is reflected in the smoothness of damping force curve as only 11 cycles are used in the phase averaging. The figure shows that mesh resolution bears a significant effect on the no-model simulations when compared with \(k\)-\(\omega\) SST model. The no-model results of Mesh-2 and Mesh-3 are very close (there are some differences near the peak as the fine mesh run has not fully converged), and they are significantly different from Mesh-1 which underestimates the peak of the damping force. Conversely, with the \(k\)-\(\omega\) SST model, all the meshes produce similar results. The medium mesh (Mesh-2), is found to have good balance between the accuracy and computational cost, and is hence used throughout the rest of the paper. This study also highlights another potentially important reason for using a turbulence model in the context of resource-limited industrial simulations in that, without one, the mesh-dependence of the solution is heightened.
# Agnostic Envelope Linearization of Dynamically Supplied Power Amplifiers for Mobile Terminals
## 2 Envelope Tracking PA Linearization
### EGMP Coefficients Extraction Process
```
1:Initialize the EGMP and SED-GMP model
2:Initialize several shaping functions \(\Gamma_{1}\cdots\Gamma_{k}\)
3:Initialize weights \(\mathbf{\alpha}\leftarrow\{\mathbf{0}\}\), \(\mathbf{\beta}\leftarrow\{\mathbf{0}\}\)
4:Generate sample signal \(\mathbf{u}\)
5:Generate envelope \(\mathbf{Env}_{gen,1}\cdots\mathbf{Env}_{gen,k}\)
6:Generate EGMP matrix \(\mathbf{E}\)
7:Generate SED-GMP matrix \(\mathbf{U}=\mathbf{U}_{1}\cdots\mathbf{U}_{k}\)\(\triangleright\) Row stack
8:\(\mathbf{y}=\mathbf{y}_{1}\cdots\mathbf{y}_{k}\gets trx(\mathbf{u},\mathbf{Env}_{gen,1} \cdots\mathbf{Env}_{gen,k})\)
9:\(\mathbf{\beta}^{0}\leftarrow\left(\mathbf{U}^{H}\mathbf{U}\right)^{-1}\mathbf{U}^{H}\mathbf{y}\)
10:\(\mathbf{a}^{0}\leftarrow\left(\mathbf{J}^{H}\mathbf{I}+\lambda\mathbf{I}\right)^{-1}\mathbf{J}^{ H}(\mathbf{U}_{1}\mathbf{\beta}^{0}-\mathbf{u}_{\text{BB}})\)
11:\(i\gets 0\)
12:repeat
13:\(\mathbf{Env}_{opt}\leftarrow\mathbf{E}\mathbf{\alpha}_{i}\)
14:\(\mathbf{Env}_{in,1\cdots k}\leftarrow\mathbf{Env}_{gen,1\cdots k}-\mathbf{Env}_{opt}\)
15:Regenerate SED-GMP matrix \(\mathbf{U}=\mathbf{U}_{1}\cdots\mathbf{U}_{k}\)
16:\(\mathbf{y}=\mathbf{y}_{1}\cdots\mathbf{y}_{k}\gets trx(\mathbf{u},\mathbf{Env}_{in,1}\cdots \mathbf{Env}_{in,k})\)
17:\(\mathbf{\beta}^{\prime}\leftarrow\left(\mathbf{U}^{H}\mathbf{U}\right)^{-1}\mathbf{U}^{H}\mathbf{y}\)
18:\(\mathbf{a}^{\prime}\leftarrow\left(\mathbf{J}^{H}\mathbf{J}+\lambda\mathbf{I}\right)^{-1}\mathbf{J} ^{H}(\mathbf{U}_{1}\mathbf{\beta}^{i}-\mathbf{u}_{\text{BB}})\)
19:\(\mathbf{\beta}^{i+1}=\gamma\mathbf{\beta}^{\prime}+(1-\gamma)\mathbf{\beta}^{i}\)
20:\(\mathbf{a}^{i+1}=\gamma\mathbf{\alpha}^{\prime}+(1-\gamma)\mathbf{a}^{i}\)
21:\(i\gets i+1\)
22:until\(i=i_{max}\) or meet linearity requirement
23:return\(\mathbf{\alpha}^{i-1}\)
```  
**Algorithm 1** Adaptation procedure for EGMP
# Effect of Ultrasonic Treatment on Taste and Flavor Quality of Japonic Rice
## 2 Materials and Methods
### Experimental Methodology
#### 2.4.5 Electronic Nose Analysis  
Volatile compounds in different varieties of cooked rice were analyzed using a portable electronic nose following the slight modifications of the method of [PERSON] et al. (2016). A 5 g cooked rice sample was placed in a specialized headspace container and sealed with a double layer of cling film. The sensor parameters were set with an analysis time of 60 s and an injection flow rate of 300 mL/min. Each sample was separately analyzed five times.
## 3 Results
### Comparison between Time-Series Phenological Estimation Portfolios for Crop Growth Stages
#### 3.2.2 Effect of Data-Measured Time Period on Estimation Accuracy  
The impact of data-measured time periods ('Morning', 'Afternoon' or 'Whole-Day') on estimation accuracy was assessed in this section. The definition of these time periods is detailed in Section 2.3. Figure 6 illustrates the accuracy of estimation results for various crop growth stages using ground-based datasets.  
For summer maize, the results shown in Figure 6a indicate a complex pattern when considering different data-measured time periods. In terms of in situ SIF data, 'Morning' measurements generally perform better than data from other time periods. However, there are exceptions, notably for T&S, where only the 'Morning' dataset provides an accuracy  
Figure 6: Accuracy assessment for crop growth stage estimations using different datasets. Subfigure (**a**) refers to maize, while subfigure (**b**) is focused on winter wheat. The circular arcs in both radar charts represent \(R^{2}\) for each estimation, ranging from 0 to 1. The labels ‘morning’, ‘afternoon’ and ‘day’ in this figure correspond to the data-measured time periods of ‘Morning’, ‘Afternoon’ and ‘Whole-Day’, respectively.  
of approximately 0.37, which is still considered unacceptable. The estimation accuracy of SIF measured during 'Afternoon' is slightly higher than that of SIF during 'Morning' for jointing, while the opposite is true for T&S and milk. Regarding in situ EVI data, the results in Figure 5(a) suggest that 'Afternoon' measurements are generally the most suitable for maize growth stage estimation. EVI data from 'Afternoon' significantly outperform data from other time periods for estimating T&S, while offering similar accuracy for the estimation of other growth stages.  
As for winter wheat, Figure 5(b) demonstrates that in situ SIF data measured during 'Afternoon' consistently provide outstanding performance in the estimation of almost all winter wheat growth stages, except for maturity. For Maturity, SIF data from 'Whole-Day' offer slightly better accuracy, while SIF data from the 'Afternoon' still yield a decent accuracy of approximately 0.53.  
In summary, for maize, the choice of data-measured time period has a mixed impact on estimation accuracy, with 'Morning' being generally better for in situ SIF data, and 'Afternoon' being more suitable for in situ EVI data. For winter wheat, 'Afternoon' measurements of in situ SIF data consistently offer the most accurate estimation results for most growth stages. To be noticed, Figure 5(b) does not include regrowth stage because in situ SIF could not be utilized for estimation if it (Figure 5(b)). The absence of in situ EVI data was due to its extremely low accuracy elaborated in the previous section.
## 1 Introduction
This paper focuses on experimental research investigating the impact of filtration and tribological parameters on the reliability, lifespan, and functional performance of transmission systems used in robotics. Based on these findings, progressive approaches to the maintenance of manufacturing equipment, including robots and other handling devices, are developed, enabling the rational application of analysis results for flawless operation and reliability of the equipment.
###### Abstract
The utilization of these algorithms is based on Python, while the development of INSUS is based on Unity. We integrate these two components with an _application programming interface (API)_, which allows two different programming languages to work together using a standardized format (e.g., JSON). We employ a _representational state transfer (REST) API_ to communicate between Unity and Python, as exemplified by several studies [13; 14; 15]. Python is responsible for the machine learning operations of _YOLOv8_, _PaddleOCR_, and the _Levenshtein distance_ process. It receives and processes the input from Unity and returns the output back to Unity. We utilize the FastAPI library in Python to handle these interactions [16]. On the other hand, Unity is responsible for providing the input in the form of images and processing the output to update the user's current coordinates. Unity handles this interaction using the _Unity Web Request_ module [17; 18]. For evaluations of the proposal, first, the accuracy of sign detection by _YOLOv8_ is evaluated. Images containing signs in the building are collected as a dataset to generate the model. Then, the _precision_, _recall_, and _mean average precision (mAP)_ values over different _intersections over union (IoU)_ thresholds ranging from 0.5 to 0.95 are calculated [19]. Second, the accuracy of the text recognition by _PaddleOCR_ is measured using the _character error rate (CER)_ metric [20]. Finally, the performance is compared between the proposal and the previous method in #2 and #3 Engineering Buildings at Okayama University. The results confirm the effectiveness of the proposal in reducing the execution time required for the _user location reset_ method. Our contributions are as follows: we trained a _YOLOv8_ model with our custom dataset to detect room numbers from _sign images_. We confirmed the validity of _PaddleOCR_ to extract text with an average _CER_ lower than 10% from small images. We also incorporated the _Levenshtein distance_ to mitigate slight character recognition errors and achieve accurate _database matching_ to acquire the correct room coordinates. The rest of this paper is organized as follows. Section 2 discusses related works. Section 3 presents previous work. Section 4 presents the user location reset method usingobject recognition. Section 5 evaluates the proposed system through experiments. Finally, Section 6 concludes this paper with future works.
# Model Based Robust Predictive Control of Ship Roll/Yaw Motions with Input Constraints
## 2 Problem Formulation
### Predictive Model of Rudder-Fin Joint Rolllyaw Control System  
In this paper, we consider a fifth-order state and dual-input rudder-fin joint roll/yaw control discrete-time system corresponding to Equation (2), which is in essence an affine nonlinear MIMO model. With the help of first-order Euler method, the predictive model of rudder-fin joint roll/yaw control system can be achieved by the discretization of Equation (2), as expressed by  
\[\mathbf{x}(k+i+1\big{|}k)=\mathbf{A}\mathbf{x}(k+i\big{|}k)+\mathbf{B}\mathbf{u}(k+i\big{|}k)+\mathbf{ d}(k+i\big{|}k), \tag{8}\]  
where \(i\geq 0\), the index \(k+i\) and \(k+i+1\) denote the variables at \(k+i\) and \(k+i+1\) time step respectively. For notional simplicity, we employ \(\mathbf{x}(k)\), \(\mathbf{u}(k)\) and \(\mathbf{d}(k)\) instead of \(\mathbf{x}(k)\), \(\mathbf{u}(k)\) and \(\mathbf{d}(k)\).  
Our control objective is to maintain the course keeping and stabilize the ship form roll motion, even in severe situations where the wave disturbance is large. It is noticed that \(H_{\infty}\)-type MPC approach can attenuate the effect caused by disturbances [30; 31] Thus we employ it in ship control as presented in this paper. The following \(H_{\infty}\)-type cost function is taken into account  
\[J_{\infty}(k)=\sum_{i=0}^{\infty}\Delta(\mathbf{x}(k+i\big{|}k),\mathbf{u}(k+i\big{|}k ),\mathbf{d}(k+i\big{|}k))\,, \tag{9}\]  
where the stage cost is defined as \(\Delta:=\|\mathbf{x}\|_{Q}^{2}+\|\mathbf{u}\|_{R}^{2}-\lambda\|\mathbf{d}\|^{2}\), \(Q\) and \(R\) are weighting matrices that are positive definite and \(\|\mathbf{x}\|_{Q}^{2}\), \(\|\mathbf{u}\|_{R}^{2}\) denote \(\sqrt{\mathbf{x}^{T}Qx}\) and \(\sqrt{\mathbf{u}^{T}Ru}\) respectively. Additionally, \(\mathbf{d}\) denotes generalized disturbance.
## 5 Implementation and Results
### Rescuer Rescue Guide  
* System operation step: (1) click on rescue button \(\rightarrow\) (2) click on 1 st floor button \(\rightarrow\) (3) drawing recognition of floor \(\rightarrow\) (4) recognition of ignition point and dangerous area \(\rightarrow\) (5) click on occupant's position and suggests a safe path guideline  
Figure 18 shows system operation for rescuer. Assuming a fire situation, the rescuer quickly launches the AR-based Smart Building Disaster Management Service application and clicks the Rescue button. The rescuer will then click on the drawing of the floor where the occupant is to be rescued and recognizes the drawing of the building (structure recognition of the building in real situations) through the camera. The abnormal area and the fire occurrence area are recognized by the smart sensor and displayed on the application screen based on the AR. The position of the occupants to be rescued is identified by the room sensor and the position of the structure is selected. The recommended rescue path for occupants is suggested and displayed on the smart pad screen.  
Figure 15: Implementation Model: (**a**) Smart Building Model; (**b**) System operation UI (3 rd floor); (**b**) system operation UI (2 nd floor); (**c**) system operation UI (2 nd floor); (**d**) system operation UI (1 st floor).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Device name** & Gas Leak Detector & **Device name** & Electric Leak Detector \\ \hline
**Type** & Sensor & **Type** & Actuator \\ \hline \multirow{2}{*}{**Object gas**} & Liquified Natural Gas (LNG), & **Size** & \(7.5\times 7.5\times 3.5\) \\  & Liquified Petroleum Gas (LPG), & **Size** & \(7.5\times 7.5\times 3.5\) \\ \hline
**Method** & Catalytic combustion, diffusion type & **communication** & Zigbee \\ \hline
**Device name** & Flame Detection Sensor & **Device name** & Damper Breaker \\ \hline
**Type** & Sensor & **Type** & Actuator \\ \hline
**Size** & \(32\times 7\times 4\) & **Maximum Power** & \(12\)W \\ \hline
**Weight** & \(0.3\) & **Size** & \(3.2\times 7\times 4\) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Device specifications.