esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Result and Discussion
### Performance of the Hybrid ANN-BBO in Estimating Fruit Properties Based on Spectral Data  
Figure 3 represents the regression plot between the mean estimated and actual amount of starch of Fuji apples based on spectral data of 800 to 900 nm. As mentioned, 100 iterations were executed to evaluate the hybrid ANN-BBO method's reliability in predicting starch. The regression coefficient was 0.97 for predicting starch, indicating the algorithm's high accuracy in predicting starch at the given spectral range. Figure 4 represents the criteria evaluating the performance of the ANN-BBO for predicting the starch content of Fuji apple at 100 iterations. These graphs indicate that in all iterations, the regression coefficient value for starch was higher than 0.87, and in the best state of training, it was close to 0.97.  
According to Figure 5, the regression coefficient of 0.94 for predicting TSS implies that TSS estimation at the range of 920-980 is highly accurate. Figure 6 illustrates that in all iterations, the prediction algorithm's regression coefficient was higher than 0.83. In the best state of training, it was close to 0.96. The performance of hybrid ANN-BBO was evaluated using boxed diagrams of mean estimated and actual starch and actual TSS of apple at the range of 800 to 900 nm. Most samples' box diagrams were almost overlapped, indicating close results between estimated and actual starch values and TSS values.  
Figure 3: Regression analysis of the scatter plot between the mean estimated and the measured starch content in Fuji apple based on spectral data of 800 to 900 nm.  
Figure 4: Box diagram of five criteria for evaluating ANN-BBO performance in estimating the starch content of Fuji apple on spectral data of 800 to 900 nm in 100 iterations. (**a**) coefficients of regression and determination, (**b**) error criteria.  
Figure 5: Regression analysis of the scatter plot between the mean estimated and the measured content of TSS in Fuji apple based on spectral data of 920 to 980 nm.
## 4 Consecutive Collision Problem Analysis of the JCS Sidelink Resource Allocation  
### Principle of the SB-SPS Resource Allocation Scheme  
NR-V2X supports Mode 2 sidelink communication and employs the sensing-based semi-persistent scheduling (SB-SPS) scheme [45]. Initially, the SB-SPS scheme is designed to support periodic safety messages, utilizing sensing windows and a resource reservation mechanism to reduce end-to-end latency. The fundamental working principle of SB-SPS is illustrated in Figure 2, with specific steps as follows:  
In the sensing window, vehicles measure the reference signal received power (RSRP) of a physical resource block (PRB), continuously generating a list of available resources \(L_{a}\). This list includes time-frequency resources with RSRP values below the threshold \(P_{th}\). Once the number of resources in \(L_{a}\) is less than \(X\%\) of the total resources, \(P_{th}\) will increase by 3 dB to increase the number of \(L_{a}\). The \(X\%\) threshold can be set to 20%, 35%, or 50% depending on the configuration and service priorities. Subsequently, during the resource selection window, a reselection counter (\(RC\)) is employed to manage the use of reserved resources, with resource reselection occurring only when the \(RC\) reaches 0. This process involves selecting a new resource with probability \((1-P_{k})\) or continuing to use the previously reserved resource with probability \(P_{k}\), where \(P_{k}\) ranges from 0 to 0.8. Once a resource is selected, continuous transmission occurs in the same resource block, with the number of transmissions determined by the value of \(RC\). \(RC\) is randomly chosen within a range between 5 and 15 and decremented by 1 after each transmission until \(RC\) reaches 0, at which point the next selection process is triggered. Equation (9) demonstrates the \(\Delta RC\), which is the \(RC\) decreasing step size of the SB-SPS:  
\[\Delta RC=RC^{\prime}-RC=1 \tag{9}\]  
Due to the distributed resource allocation characteristics of the sidelink, it is impossible to obtain complete channel state information, which means that it cannot be guaranteed that all vehicles select idle resources. In addition, there is partial overlap in the candidate resource pools between neighboring vehicles, resulting in packet collisions. This issue is exacerbated in dynamic scenarios with high-density and large packet-size services. Furthermore, the sidelink echo signals are also subject to interference from consecutive packet collisions, which degrade the sensing performance.  
Figure 2: Process flow of sensing-based semi-persistent scheduling (SB-SPS).
## 3 Results and Discussion
### Validation  
The probability that an image is considered to have good quality and indeed has good quality is called sensitivity. Equation (9) can be used to calculate sensitivity.  
\[\textbf{Sensitivity}=\frac{\textbf{True Positive (TP)}}{\textbf{True Positive (TP)+ False Negative (FN)}} \tag{9}\]  
Specificity is the probability of an image being considered to have low quality and indeed has low quality. Equation (10) can be used to calculate specificity.  
\[\text{Specificity}=\frac{\text{True Negative (TN)}}{\text{True Negative (TN)}+\text{False Positive (FP)}} \tag{10}\]By dividing the number of classifications by the number of correct classifications, as shown in Equation (11), the accuracy of classification success can be described.  
\[\text{Accuracy}=\frac{\text{True Positive }(\text{TP})+\text{True Negative }(\text{TN})}{\text{True Positive }(\text{TP})+\text{True Negative }(\text{TN})+\text{False Positive }(\text{FP})+\text{False Negative }(\text{FN})} \tag{11}\]  
In Table 3,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**File** & **Class** & \multicolumn{2}{c}{**Results of Sampling Types of Meat**} \\ \hline  & & **Image** & **Histogram** \\ \  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**File** & **Class** & **Results of Sampling Types of Meat** \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**File** & **Class** & **Results of Sampling Types of Meat** \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._This k-NN research was created using the Google Colab platform. A GPU, or graphics processing unit, was used in each experiment. Two approaches are usually adopted. In the first stage, two techniques--GLCM and the Haar wavelet algorithm--were used to extract features to identify meat images. In the second approach, the meat image was determined directly from the tracked meat features (feature extraction) using k-NN that has been trained on various image domains. This set of features was put into the k-NN model to see whether the classification performance is sufficient to separate the texture image (a) from images (b) and (c).
# Scanning Inside Volcanoes with Synthetic Aperture Radar Echography Tomographic Doppler Imaging
## 4 Validation of the Sonic Tomographic SAR Results
### Topographic Validation
**(a)** DEM result in the slant-range coordinates. **(b)** Tomographic result (in magnitude), and topographic height (yellow line taken from the white line 1 of Figure **(a)**).
## 4 Discussion
In relation to the mass centre acceleration approximated by the MP device, it should be noted that a strict protocol must be followed to minimise potential sources of errors. It must be a priority that all movement be as much as feasible isolated in the vertical direction. [PERSON] et al. [8] indicated that the device's location could be a source of error since rotational effects of the pelvis are included in the data pertaining to be the motion of the mass-centre. However, it was found that one of thepredominant sources of errors was from using a low sampling frequency. This can affect the accurate identification of the data to be used in the integration approach [7; 10]. Only the vertical component of the ground reaction forces was selected for the FP, removing some of the challenges discussed above for the tri-axial accelerometer (MP). Constant gravitational acceleration can be assumed during the aerial phase, and vertical acceleration can be established independent of body orientation during the jump. Considering the results presented in Table 1, the ABJ represents the best accuracy and precision (0.8 \(\pm\) 14.7 mm) compared to marker tracking with a 3D motion capture system. Height estimation by the double integration method using the vertical ground reaction force is the second most accurate and precise (1.5 \(\pm\) 13.9 mm), and the third most accurate result was the QMS fight-time method (6.1 \(\pm\) 17.1 mm). A lower precision was obtained from flight-time for EJ, MP, and FP devices since the physical principle used to determine flight-time underestimated its true value. Interestingly, the double integration method with the MP device led to the fourth most accurate measurement of jump height but was accompanied by a poor demonstration of precision (\(-\)31.4 \(\pm\) 122.7 mm). This suggests a lack of frequency resolution, and the sensitivity of the method to sensor orientation may heavily affect the results. A low precision of the jump height is observed in all the devices and methods. This limitation can be overcome by taking the average value of several jumps, which requires the athlete's performance to be the same in all the jumps. The flight time is underestimated when using EJ, MP, and FP devices. Therefore, the athlete's performance can only be evaluated by comparing measurements from the same device since the jump height presents low accuracy. The low-frequency resolution of vertical acceleration, measured with the MP device, limits the evaluation of jump height to the flight-time method, which is determined from the maximum and minimum velocity. The jump height can be evaluated from the highest point of the vertical trajectory, which is determined by double-time integration of acceleration obtained from the ground reaction force, measured with the FP device. However, the accuracy of the result is highly dependent on the post-processing of the reaction force, which involves the determination of vertical acceleration, the correct identification of the jump-starting point, and the double-time integration of acceleration.
## 4 Results and Discussion  
### Energy Balance Before Integration of the Electrolyzer  
Currently, the total energy consumption of Kaunas WWTP is 26,029 MWh. The electricity consumption is 9350 MWh (or 35.9% of the total energy), of which 80.6% was obtained from the electricity grid, 17.4% was produced in cogeneration from biogas, and 2.0%--in a solar power plant. The heat consumption is 16,129 MWh (or 62.0% of the total energy). Almost all the heat (98.1%) was produced from biogas in the boiler house and cogeneration. Only 395 MWh of natural gas was consumed for heat production (or 1.5% of the total energy). 455 MWh of diesel (or 1.7% of the total energy) was consumed in the WWTP's own transport and for treated sludge transportation.  
The energy production in Kaunas WWTP is 17,645 MWh. Of this, 15,829 MWh (or 89.7%) is heat production from biogas, 1627 MWh (or 9.2%) is electricity production from biogas, and 190 MWh (or 1.1%) is electricity production in a solar power plant. Although the energy value of the produced biogas is higher (27,161 MWh), a significant portion of the biogas energy is lost with exhaust gases when burning biogas in the boiler room and cogenerators.  
Thus, the total energy balance (produced energy minus consumed energy) is negative and amounts to (\(-\)8383) MWh, or (\(-\)32.2%) of the amount of energy consumed.  
The energy balance data are presented in Table 5.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Energy Form** & **Existing Situation** & **Case V1 \({}^{*}\)** & **Case V2 \({}^{*}\)** \\
**Energy consumption** & **MWh/a** & **MWh/a** & **MWh/a** \\ \hline Electricity consumption & 9350 & 64,067 & 64,067 \\ \hline Heat consumption (produced from biogas) & 15,829 & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Energy balance data (structured by the authors).
## 1 Introduction
Unfortunately, daily sound environments contain noise and reverberation, and it is well known that noise and reverberation have significant effects on the perception of speech. For CI listeners, a previous study found that the comprehension of simulated speech from CIs was significantly lower than that of the normal speech (original speech) due to the addition of noise and reverberation [16]. Previous research related to the perception of nonlinguistic information of NVS suggested that, except for extremely poor sound conditions, in daily environments (signal-to-noise ratio (SNR) is greater than 10 dB and reverberation time (\(T_{\text{R}}\)) is less than 1.0 s), there were no significant effects of noise and reverberation in nonlinguistic information recognition [17]. However, how the noise and reverberation affect the contribution of MSFs to the perception of vocal-emotion is still unclear. We used qualitative and quantitative analyses, focusing on the relationship between the results of vocal-emotion-recognition experiments and MSFs to investigate the following two research questions:  
1. Can MSFs be used to explain the vocal-emotion-recognition results in noisy reverberant environments? 2. Are there common MSFs that contribute to vocal emotion recognition in all daily environments? This paper is organized as follows: Section 2 introduces the results of vocal-emotion-recognition experiments obtained from previous research. Section 3 presents the analysis of these vocal emotion recognition experiments. Section 4 presents the analysis of MSFs. Section 5 describes the qualitative and quantitative analyses of the relationship between MSFs and the vocal-emotion-recognition results. Section 6 discusses the contribution of common MSFs to vocal emotion recognition. Section 7 summarizes the results.
# A Review on Head-Related Transfer Function Generation for Spatial Audio
## 3 Measurement and Estimation
These individual-specific data may range from simple inputs, such as facial or ear images and anthropometric measurements, to more complex and structured data, like 3D meshes. Indeed, a frequent preliminary problem concerns data acquisition for constructing a detailed model of an individual's head and torso. Such high-quality data are necessary when physical modelling HRTFs and for customisation and interpolation procedures. These 3D measurements are often provided in the form of 3D meshes, which consist of a set of vertices and polygons representing the head and upper torso of the subject. These measurements are typically obtained using 3D vision systems. By their nature, such systems have a limited field of view (FoV), meaning that only a restricted portion of the subject can be captured at any given time. To overcome this limitation, two primary approaches are commonly employed. The first involves the use of multiple 3D vision devices positioned around the subject to capture different perspectives, followed by the fusion of the data from each device. Though offering the highest accuracy, this approach is also the most expensive due to the need for multiple devices. For example, the NoW dataset [88], which contains 3D head scans and high-resolution images of the subjects, belongs to this category. The second approach involves moving a single device around the subject to capture the necessary data. This method is more cost effective, as it eliminates the need for multiple devices, but it is also less accurate. One limitation is the potential for subject movement during the scanning process. Additionally, to achieve a complete 3D reconstruction, the movement of the sensor in space must be accurately estimated, which is necessary to align and merge the individual measurements. This is typically achieved through simultaneous localisation and mapping (SLAM) techniques followed by several refinement steps; these steps, however, tend to be less accurate compared to the multidevice approach. Datasets like HUTUBS [89], SONICOM [90], and 3D3A [91], which include 3D models and HRTF measurements, belong to this category. A detailed list of available HRTF datasets can be found in Appendix A.
## 3 Stability Analysis of an Integral Load-Bearing Structure of GER
### Interaction Mechanism of Discontinuous Arrangement Roadside Support
#### 3.2.1 Stress Distribution between Columns
The tensile stress zone of the roof between the columns was distributed in an arch shape; the arch feet were located at the edge of the concrete-filled steel tubes, and the arch crown was located at the middle line between the two columns. The roof of the roadway in the top caving working panel was coal rock full of cracks, which is prone to tensile damage and leads to instability in the whole load bearing structure. Thus, reasonable spacing must be controlled to ensure the stability of the rock mass between columns. It can be seen from Figure 6 that when the spacing between columns was 1.8 m, the tensile stress zone between columns was within the range of 0.3 m of the roof's depth. However, when the spacing reached 2.0 m, the tensile stress zone between columns extended to the range of 1.2 m of depth; when the spacing reached 2.2 m, the tensile zone between columns extended to the range of 2.2 m of depth, and when the spacing reached 2.4 m, the range of tensile zone between columns was greater than 2.6 m of depth, which exceeds the control depth of the basic anchor bolt support system of the 91-101 ventilation roadway. Thus, to ensure the stability of the roof between columns and the whole roadside support system, the spacing between the concrete-filled steel tubes should not be greater than 2.2 m.  
Figure 6: Vertical stress distribution in the roof between columns with different spacing.
# Analysis of a Tracked In-Pipe Robot's Obstacle-Crossing Performance
## 3 Results
### Prototype Testing and Analysis of the Robot  
In accordance with the overall design scheme of the pipeline robot discussed in the preceding sections, the development of the pipeline robot prototype was successfully completed. The completed prototype is depicted in Figure 14. The pipe diameter adaptation mechanism primarily comprises the active rod, driven rod, lead-screw nut mechanism, lead-screw motor, and left and right support seats, among other components. The lead-screw nut mechanism serves as a crucial transmission element within the system. The lead-screw motor is connected to one end of the lead screw, and through the motor's drive, the nut moves horizontally along the lead screw. Three connecting seats are evenly distributed at 120\({}^{\circ}\) intervals on the nut, and these connecting seats play a pivotal role in the mechanism's function. As the nut moves, it drives the active rod to adjust its angle correspondingly, enabling the traveling mechanism to elevate or descend. This design not only enhances the flexibility of the mechanism, but also allows it to accommodate various pipe diameters. Two sets of driven rods form a parallelogram mechanism that connects the traveling mechanism to the support seat. The coordinated action of the active and driven rods enables the mechanism to adapt to different pipe diameters, thereby improving operational efficiency. The left and right support seats provide stable support for the mechanism, ensuring its stability during operation and preventing failures due to vibration or displacement.
## 5 Application to a Specific Case Study: ITER Divertor Remote Handling System
### ITER Requirements  
The essential requirements of ITER organization [32] for the RH-IHMI software in a critical environment are outlined in Appendix A.  
In this table, it is remarkable that providing a user-friendly interface supporting real-time communications with RH systems is necessary. Additionally, the interface must be efficient in monitoring and visualizing large datasets and incorporate robust security measures to prevent unauthorized access. It should also be adaptable to various screen sizes and resolutions and comply with industry standards for reliability and safety. Furthermore, comprehensive logging and auditing features, along with multi-language localization support, are essential. Customizable dashboards and reporting tools, modular architecture for easy maintenance, built-in error handling, and support for integration with other systems complete the list of requirements for this critical software system.  
Ultimately, the architecture design process will be guided by hazard and risk assessments, with a recommendation to adopt the ISO-10218 [33] standard for industrial robots to comply with the European Machinery Directive. Furthermore, the ITER nuclear safety control tier is utilized for I&C related to nuclear safety.  
ISO-10218-1:2011 SS5.4.2 [33]: Safety-related parts of control systems shall be designed so that they comply with PL=d with structure category 3, as described in ISO-13849-1:2006 [34], or so that they comply with SIL 2 with a hardware fault tolerance of 1 with a proof test interval of not less than 20 years, as described in IEC 62061:2005 [35].
## 4 HertDroid Model
### Android APK Transformer
#### 4.3.2 Heterogeneous Multi-Component Attention  
To handle the heterogeneity of APK HG, we build a heterogeneous multi-component attention (HMA) module to generate the embedding of APKs. The architecture of HMA is illustrated in Figure 4c.  
To get the embedding \(H^{\prime}(i)\) of target node \(i\), formally, HMA can be expressed as below:  
\[\mathrm{H^{\prime}(i)}=\alpha_{\tau(i)}\mathrm{W^{A}_{\tau(i)}}\Big{(}\mathrm{ mish}\Big{(}\sum\
olimits_{j\in N(i)}\mathrm{ATT(i,j)}\cdot\mathrm{MES(i,j)} \Big{)}\Big{)}+\Big{(}1-\alpha_{\tau(i)}\Big{)}\mathrm{H(i)} \tag{5}\]  
where \(N(i)\) denotes the source nodes of \(i\), and \(H(i)\) is the feature of \(i\). The activation function _mish_[32] is applied to the expression involving attention mechanism \(ATT\) and neighborhood aggregation \(MES\). Moreover, \(\tau(i)\) denotes the node type of \(i\), and both the trainable parameter \(\alpha\) and learnable matrix \(W^{A}\) are specific to \(\tau(i)\).  
The \(ATT\) calculates the weight of importance between pairs of nodes based on the associated node and edge types:  
\[K_{j}=W^{K}_{\tau(j)}H(j) \tag{6}\]  
\[Q_{i}=W^{Q}_{\tau(i)}H(i) \tag{7}\]  
\[AttHead^{p}(i,j)=\Big{(}K^{p}_{j}W^{ATT}_{\Phi(j\to i)}\Big{(}Q^{p}_{i} \Big{)}^{\mathsf{T}}\Big{)}\cdot\frac{1}{\sqrt{\mathrm{C}}} \tag{8}\]  
\[ATT(i,j)=softmax\Bigg{(}\underset{\mathrm{p}\in[1,\mathrm{h}]}{\mathrm{AttHead ^{p}(i,j)}}\Bigg{)} \tag{9}\]  
where \(C\) is the dimension of nodes and \(\parallel\) denotes stacking. HMA employs a multi-head attention mechanism containing \(h\) heads to calculate the weight \(ATT(i,j)\) of source node \(j\). Each attention head captures hidden relationships between target node \(i\) and source node \(j\). It is worth noting that two different trainable transforms (i.e., \(W^{K}\) and \(W^{Q}\)) are specific to node type, and a projection matrix \(W^{ATT}\) is specific to edge type \(\Phi(j\to i)\).  
Similar to \(ATT\), the message passing operator \(MES\) also involves a multi-head mechanism, which can be expressed formally as below.  
\[MES(i,j)=\underset{p\in[1,h]}{\parallel}MesHead^{p}(i,j) \tag{10}\]  
\[MesHead^{p}(i,j)=W^{M,p}_{\tau(j)}H(j)W^{MSG}_{\Phi(j\to i)} \tag{11}\]  
where \(W^{M}\) and \(W^{MSG}\) are projection matrices.