esa-sceva/satcom-chunk-collection huggingface dataset:
# Travel Time Prediction on Long-Distance Road Segments in Thailand
## 4. Results and Discussions
### Discussion
The advantage of our study is that it attempted to use only time-series of GPS data without other features to predict the travel time of a subsequent hour. This results in one prediction model per road segment. In addition, some other studies [16; 18; 20; 22] considered other features, such as weather, lane level, and the day of the week. In this case, our method framework can be enhanced by the inclusion of other aspects such as weather, road repair schedules, and local festivals that need much more study. Thus, further study should consider other factors so as to improve the travel time prediction model. The potential application of our work is to provide a service for the travel time prediction of commercial trucks. In this case, the government department that owns the big GPS data should calculate the travel time of every route in every following hour, then provide the predicted data to consumers. The consumers might be logistic firms that need precisely estimated travel times for a logistics cycle to improve their logistics plans and monitor unusual situations during delivery processes. To the best of our knowledge, a function for predicting the travel time of commercial trucks is not presented in any online map application. In this case, it can add more value to both data owners and data consumers. To this end, as mentioned in the first section, the monitoring of some anomalies in road traffic can help city policymakers and officers achieve solutions to traffic problems more quickly and effectively. When road traffic must be predicted under usual traffic conditions,any irregular state that is highly different from the predicted one can be observed [1; 2; 3]. As our work shows, historical traffic data can be used to make travel time prediction models. In the future, if we can create values from data in any perspective of the traffic domain, such as speed and flow prediction, route prediction, and accident prediction, then smart mobility will become even more realistic. In any case, our work is another significant step in the development of data-driven smart cities in Thailand and also in other countries.
# Methodology for the Prediction of the Thermal Conductivity of Concrete by Using Neural Networks
## 3 Methodology
### Development of the Model
#### 3.2.4 Generative Adversarial Network Model  
A Generative Adversarial Network (GAN) model consists of two distinct neural networks, the generator and the discriminator, which are trained simultaneously. The GAN captures the distribution pattern of a given dataset and generates data that resemble this dataset. First, the generator initiates the process, which takes a random noise as an input, creating samples similar to the original dataset. The discriminator then tries to distinguish the data and detect whether a sample is from the original dataset or the generator model distribution. During the training, the generator loss and the discriminator loss are evaluated, allowing the generator to get better and better at producing new artificial data and the discriminator to find out if they are real or synthetic (Figure 5). This methodology framework used the Copula GAN synthesizer, a CTGAN variation. It uses the cumula  
Figure 3: Example of the MLP model.  
tive distribution function-based transformation that Gaussian copulas apply, making the learning process easier.
# A Novel Technique for Semantic Segmentation of Hyperspectral Images Using Multi-View Features
## 4 Proposed Technique  
The hyperspectral image is envisioned to be segmented by utilizing the hyperspectral data's block structure in a multi-view manner. The entire hypercube is automatically divided into disjoint sub-blocks along the spectral dimension. An overview of the proposed technique to perform semantic segmentation on HSI is shown in Figure 5.  
With each spectral band essentially representing a distinct \"view\" of the same scene. These views encapsulate valuable information pertaining to the unique spectral characteristics of the materials and objects present in the scene. By treating each spectral band as an individual view, CCA can be employed to uncover meaningful relationships and correlations among these views. The key insight behind this approach lies in the fact that different spectral bands may convey complementary information. While some bands may excel in capturing the spectral signatures of specific materials, others may be more adept at highlighting spatial or structural details. By applying CCA to these multi-views, it becomes possible to extract features that are not only representative of the spectral information but also incorporate spatial characteristics. However, considering each spectral band as a different view for CCA hikes the computational complexity.  
In practical terms, hyperspectral images are divided into subsets or views, with each subset corresponding to a specific wavelength or band. CCA is then applied to identify linear combinations of bands that exhibit the highest correlation. These linear combinations effectively serve as new features, encapsulating both spectral and spatial information from the original dataset.
## 3 Results and Discussion
### Convective and Infrared Drying
#### 3.1.3 Specific Energy Consumption (SEC) of Convective and Infrared Drying of Terebinth  
Table 3 lists the SEC of treebinth drying using the CV and IR dryers with various pretreatments. Using US, BL, and MW significantly reduced the SEC for both dryers (\(p<0.05\)). Comparing the dryers and three pretreatments, it can be found that the lowest SEC (26.25 MJ/kg) was obtained for MW pretreatment and IR drying at 750 W. Whereasthe highest SEC (142.58 MJ/kg) was also calculated in the control sample using a CV dryer at 50 \({}^{\circ}\)C.  
As the inlet air temperature of the dryer increased, the SEC decreased for all pretreatments. As expected, a rise in the temperature incremented thermal gradient and mass transfer, giving rise to faster moisture exit and hence shorter drying time which decremented SEC [22]. It can be seen that the highest SEC was related to the method in which hot air was used without pretreatment. In the convective method of drying, all the energy from the hot air is not completely consumed for the process and a significant amount of that is wasted by the output flow. In the IR method, all the energy of the lamps is radiated directly to the sample and heats it. Therefore, there is no energy loss through the outlet valves [21]. These results are consistent with the studies by [PERSON] et al. [25] and [PERSON] and [PERSON] [23]. In comparison between different pretreatments in both drying methods (CV and IR), the lowest energy consumption was for MW pretreatment. In the MW pretreatment, heat is generated in the entire product due to the absorption of waves by the moisture and the vibration of water molecules. This will resolve the problems related to thermal conductivity compared to US and BL and reduce the drying time [14]. [PERSON] et al. [38] dried raspberries using a CV dryer under different pretreatments and showed that the lowest SEC can be obtained in microwave pretreatment. In drying strawberries with IR-convective dryers under different pretreatments (MW, BL, and US) it was stated that the MW pretreatment had the lowest SEC [33]. In another study, [PERSON] et al. [31] dried blackberries using different dryers (CV, IR-CV, and IR) and pretreatments (BL, US, and MW). They concluded that microwave pretreatment can further reduce the energy consumption of all three dryers.
## 3 Experiments
### Evaluation Metrics  
We assessed the accuracy of segmentation results by comparing the spatial distribution of the masks obtained by the automated segmentation with that of the masks obtained through a manual segmentation of the T1c images performed by radiologists. Metrics adopted for the accuracy is described below, according to [PERSON] et al. [45]. The minimal problem of assessing the agreement between two binary maps B1 and B2, representing reference and segmented data respectively, is obtained in terms of number of voxels at which both B1 and B2 score \"1\" (True Positive \(Tp\)) or \"0\" (True Negative \(Tn\)), the number of voxels at which B1 scores \"0\" and B2 scores \"1\" (False Positive \(Fp\)) and vice-versa (False Negative \(Fn\)). Several similarity indexes could be defined. We use Dice (\(DSC\)) [46], Precision and (\(P\)) and Recall (\(R\)) indexes ([PERSON] and [PERSON], 2008) defined as follows:  
\[DSC=\frac{2 Tp}{(2 Tp+Fn+Fp)} \tag{8}\]  
\[P=\frac{Tp}{(Tp+Fp)} \tag{9}\]  
\[R=\frac{Tp}{(Tp+Fn)} \tag{10}\]  
The \(DSC\) index has been used broadly in the field of segmentation as a measure of spatial overlap, \(P\) and \(R\) indexes allow to measure under- and over-estimations [45].
# Frequency Response Analysis of Perforated Shells with Uncertain Materials and Damage
## 4 Frequency Response Analysis under Uncertainty
### Frequency Response Analysis  
In frequency response analysis, the idea is to study the excitation or applied force in the frequency domain. Therefore, the uncertainty in the eigenproblem is directly translated to frequency response. Given \(\xi\in\Gamma\) the procedure is as follows [19]: We start from the equation of motion for the system  
\[\mathbf{M}\hat{\mathbf{v}}(\xi)+\mathbf{C}\hat{\mathbf{v}}(\xi)+\mathbf{S}( \xi)\mathbf{v}(\xi)=\mathbf{f}, \tag{28}\]  
where \(\mathbf{M}\) is the mass matrix, \(\mathbf{C}\) the viscous damping matrix, \(\mathbf{S}(\xi)\) the stochastic stiffness matrix, \(\mathbf{f}\) the (deterministic) force vector and \(\mathbf{v}(\xi)\) the displacement vector. The matrices \(\mathbf{M}\) and \(\mathbf{S}(\xi)\) are defined as above, whereas the viscous damping matrix is taken to be a constant diagonal matrix \(\mathbf{C}=2\delta\mathbf{I}\), where \(\delta=1/2000\). The choice of the viscous damping matrix is simply for convenience. There are many problem-dependent choices such as Rayleigh damping where \(\mathbf{C}\) would be some linear combination of \(\mathbf{M}\) and \(\mathbf{S}(\xi)\).  
In the case of harmonic excitation, a steady-state solution is sought and the force and the corresponding response can be expressed as harmonic functions as  
\[\begin{split}\mathbf{f}=\hat{\mathbf{f}}(\omega)e^{\mathrm{i} \omega t},\\ \mathbf{v}(\xi)=\hat{\mathbf{v}}(\xi,\omega)e^{\mathrm{i}\omega t }.\end{split} \tag{29}\]  
Taking the first and second derivatives of Equation (28) and substituting using (29) leads to  
\[-\omega^{2}\mathbf{M}\hat{\mathbf{v}}(\xi,\omega)e^{\mathrm{i}\omega t}+ \mathrm{i}\,\omega\mathbf{C}\hat{\mathbf{v}}(\xi,\omega)e^{\mathrm{i}\omega t }+\mathbf{S}(\xi)\hat{\mathbf{v}}(\xi,\omega)e^{\mathrm{i}\omega t}=\hat{ \mathbf{f}}(\omega)e^{\mathrm{i}\omega t}, \tag{30}\]  
thus reducing to a linear system of equations:  
\[(-\omega^{2}\mathbf{M}+\mathrm{i}\,\omega\mathbf{C}+\mathbf{S}(\xi))\hat{ \mathbf{v}}(\xi,\omega)=\hat{\mathbf{f}}(\omega). \tag{31}\]  
Any quantity of interest can then be derived from the solution \(\hat{\mathbf{v}}(\xi,\omega)\). In the following, we consider the mechanical energy defined by the natural energy norm as the quantity of interest in matrix notation  
\[E(\hat{\mathbf{v}}(\xi,\omega))=\sqrt{\hat{\mathbf{v}}(\xi,\omega)^{T}\mathbf{ S}(\xi)\hat{\mathbf{v}}(\xi,\omega)}.\]  
This is a mathematically reasonable choice, yet in practice it may be difficult to measure. We have ruled out the use of maximal deflections since the effects of the boundary layers should then be taken into account.
# TwSense: Highly Robust Through-the-Wall Human Detection Method Based on COTS Wi-Fi Device
## 5 Results and Evaluation
### System Performance Evaluation
R-TTWD had relatively better detection accuracy for presence or absence (no one is one category; other categories are considered one category), with an accuracy of about 93%; the TWMD system performed human counting work by detecting human bodies, and the recognition accuracy was around 94%. The comparison results showed that the TwSense system improved detection accuracy based on previous work and effectively improved the accuracy of the system. In order to verify the processing efficiency of these several systems using the methods, we compared the running time of several systems, as shown in Figure 15, from which it can be seen that the average recognition accuracy using the TwSense method was 95.5% and took 2.8 s; the average recognition accuracy using the TWMD method was 92.4% and took 4.2 s; the average recognition accuracy using the R-TTWD method was 90% in 3.7 s; the average recognition accuracy using the TW-See method was 71.2% in 5.6 s. In terms of processing time and accuracy, TwSense had a significantly lower processing time than the other systems, a higher recognition accuracy than the other systems, and the highest processing efficiency.
# FSM-HSVM-Based Locomotion Mode Recognition for Exoskeleton Robot
## 3 Results
### Algorithm Efficiency and Analysis  
In order to demonstrate the complexity of the algorithm, three trained algorithm models of SVM, HSVM, and FSM-HSVM are used to identify offline the same test set that has been previously processed, and the cumulative recognition time is calculated and compared with each other; the results are depicted in Figure 7. Compared to SVM and HSVM, the algorithm proposed in this paper reduces the running time t of the algorithm by about 8 s and 3 s, respectively. In other words, the recognition efficiency of the algorithm in this paper is improved between 60% and 36%, indicating that the overall amount of calculation is small, thereby conserving a significant amount of exoskeleton hardware resources.  
The time required for the classification and recognition of HSVM and FSM-HSVM models is discussed in detail below. Assume that \(n\) is the total number of identified data samples, \(a\) is the proportion of flat walking data in the total number of samples (\(0\leq a\leq 1\)), and the number of motion mode categories is \(K\) (\(K\geq 3\)). The recognition time of HSVM model and FSM-HSVM are as follows.  
\[T_{HSVM}=(K-1)n \tag{7}\]  
\[T_{FSM-HSVM}=anK+(1-2a)n. \tag{8}\]  
If \(a=1\), \(T_{FSM-HSVM}=(K-1)n\). In other words, the HSVM-HSVM model degenerates into HSVM model when all data are flat walking data. If \(a=0\), there are no flat data in the dataset, \(T_{FSM-HSVM}=n\). At this time, the recognition time is only \(1/(K-1)\) of the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Transition** & **RD** \\ \hline FW\(\rightarrow\)US & \(10.800\%\pm 2.255\%\) \\ US\(\rightarrow\)FW & \(30.400\%\pm 3.297\%\) \\ FW\(\rightarrow\)DS & \(5.333\%\pm 1.687\%\) \\ DS\(\rightarrow\)FW & \(19.333\%\pm 10.369\%\) \\ FW\(\rightarrow\)UR & \(31.600\%\pm 6.991\%\) \\ UR\(\rightarrow\)FW & \(43.067\%\pm 4.107\%\) \\ FW\(\rightarrow\)DR & \(5.867\%\pm 10.527\%\) \\ DR\(\rightarrow\)FW & \(53.733\%\pm 9.363\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: DRs of locomotion mode transitions.  
Figure 6: Comparison of recognition rates of three algorithms.  
recognition time of HSVM. If \(0\leq a\leq 1\), the ratio between FSM-HSVM recognition time and HSVM recognition time is given by:  
\[T_{ratio}=\frac{T_{FSM-HSVM}}{T_{HSVM}}=\frac{aK+(1-2a)}{K-1}\;. \tag{9}\]  
Obviously, the range of time ratio is: \(1/(K-1)\leq T_{ratio}\leq 1\). In other words, the recognition time of the FSM-HSVM model is less than or equal to that of the conventional HSVM model. Especially when the recognition category is large or there is less flat data, the overall time will be greatly reduced compared to HSVM model, and the recognition efficiency will be significantly enhanced. In comparison to SVM and HSVM models, FSM-HSVM has the highest recognition efficiency.
## 3 Analysis of Experimental Results  
### Basic Mechanical Properties  
The compressive strength results are displayed in Table 4 and Figure 2. The compressive strength at 7 days and 28 days revealed a tendency of initially increasing and then declining, and the replacement rate of RCA ranged from 0% to 100%. The greatest compressive strengths of RCASCC at 7 and 28 days were 27.9 MPa and 35.9 MPa, respectively, when the recycled aggregate substitution rate was 50%. The 7/28-day strength ratio grew and subsequently declined as the replacement ratio for RCA increased, showing that the addition of RCA boosted the early strength but did not improve the latter strength. At 50% of the RCA replacement rate, the greatest contribution to early strength improvement  
Figure 1: WHY-3000 automatic pressure testing machine.  
was found. The primary cause is the presence of recycled coarse aggregate (RCA) in the cement paste, which has a negative impact due to the index of its crusading strength, and a positive effect due to the pre-wetting treatment of RCA in the curing process of the surrounding cement, which will play a role in the internal curing and at the same time increase the density of the surrounding part of the concrete. The recycled coarse aggregate's large crushing index and low hardness are caused by some cement paste, which has the unfavorable consequence of lowering the strength of the concrete as a whole [27]. The aggregate gradation is good when the replacement ratio is 50% since the benefits of recycled coarse aggregate outweigh the drawbacks.  
The most notable improvement in early and late strength was therefore shown when 50% RCA was added in place of NCA [28].
# Target Recognition and Navigation Path Optimization Based on NAO Robot
## 2 NAO Robot's Monocular Vision Spatial Target Localization
### Image Preprocessing  
As the basis of target recognition, image preprocessing is also the data module of subsequent image processing stages. First, the image needs to be separated and filtered through specific channels [24]. The color image formats supported by Nao robots mainly include YUV422, RGB, and HSV, among which RGB color space is the most common. It is composed of three colors: R (red), G (green), and B (blue). Other colors are composed of them in different proportions [25]. During the experimental test, it is found that in general, the red ball can be accurately separated using either RGB or HSV color spaces. However, in the case of poor light (too strong or too weak), HSV color space is more stable. Therefore, this paper adopts HSV color space as the color space of the Nao robot visual image processing.  
How to separate the target from the image according to the threshold is also one of the important tasks of robot target recognition. For the color image obtained with the camera, it is difficult to separate the target with binarization according to a threshold, so it is necessary to sample and calibrate the thresholds of the three components. Under the illumination condition of the actual experimental environment, select the color area of the target in the image, count the color characteristics of each point, and get the upper and lower limit values of each component in the HSV color space [26]. The image is binarized according to the threshold, and the results are observed and adjusted in time. When the effect is ideal, the corresponding threshold is recorded as the final threshold. After the threshold is determined, the image needs to be filtered. Because Gaussian filtering can effectively suppress noise and smooth the image, this paper uses Gaussian filtering [27] to blur the local information of the image, which is conducive to the next step of Hough circle detection [28].
# Deep Learning-Based Portable Device for Audio Distress Signal Recognition in Urban Areas
## 2 Materials and Methods
### Database Collection  
We collected audios in real urban scenarios using a low-cost microphone with the aim of developing an inexpensive device that works in a real-world scenario. First, we asked 100 people to propose expressions in Spanish language that they would scream in a situation where they required help in public spaces. The three most commonly suggested signals were the wordless shout \"Ahhhh\", \"auxilio\" (help), and \"ayuda\" (synonym of help). From now on, we will refer to those noises as SN (shout noise), HN (help noise), and AN (aid noise), respectively. Audio collection was conducted in multiple real urban environments with different signal-to-noise ratio values, where background levels were classified into 3 categories: low, medium, and high. The following procedure was conducted for the audio collection.  
1. Set up the base to its maximum height and point it to an angle of 45\({}^{\circ}\) with respect to the base.
2. Set the data acquisition points with masking tape in the ground, starting from \(-60^{\circ}\) and marking a point for 3 m, 6 m and 9 m with respect to the base location, and repeat the process for angles 0\({}^{\circ}\) and 60\({}^{\circ}\) for a total of 9 points as is shown in Figure 2.
3. Start to record the audios with the data acquisition program.  
Figure 1: Diagram of the proposed device: Two meter tall structure containing the Raspberry Pi and the microphone slightly tilted forward.  
Position the subject in the initial starting point, that is, \((-60^{\circ}\), 3 m), and ask them to scream the predefined expressions SN, HN and AN.
5. Repeat the process for the following points.
6. Stop recording the data.  
The experiment was conducted on university undergraduate students ranging in age from 18 to 25 years in Colombia, South America. It was approved by the \"Universidad de los Andes\" ethics committee \"Comite de Etica de la Investigacion\". The experiment included audio recordings from 18 male and 5 female subjects. We chose students in this experiment due to the fact that they are the main population that transits the area where the experiment was being held. Every audio recording for a single subject included 27 positive distress signals and several background signals. The final dataset contains 24 audio files from 23 different subjects of about 2 min each, recorded at 22,050 Hz, with a total duration of around 48 min. A total of 648 positive events per class were collected. Table 1 shows the class distribution in the dataset.  
As it is intended to create an affordable device, the chosen hardware needs to be low cost. Therefore, the selected elements were as follows.  
* Snowball iCE Condenser microphone, Pressure Gradient With USB digital output. This has a cardioid profile with an even frequency response for human voice range to reduce the misdetection of distress signals.
* 3.5 m long Type-A to Type-B USB cable.
* 3 m long microphone base.
# Dynamic Parameters Identification of Serial Robot Based on Dual Quaternion
###### Abstract  
This paper studies the dynamic parameters identification problem of load and linkages of a serial robot in the presence of model uncertainty. The dynamic parameters of load and linkages of a serial robot have been identified through a combination procedure, which is useful for different platforms of serial robot systems. The purpose of this paper is to propose a dynamic parameter identification method for a serial robot based on a dual quaternion. Using the information of the force and torque of the load obtained by the six-dimensional force sensor installed on the end-effector of the robot, the dynamics parameter identification matrix of the load is derived, which also uses the information of motion speed and acceleration of the end-effector. On the other hand, the analysis of the dynamic relationship between adjacent linkages and the joints is based on dual quaternion algebra, and the identification matrix for the dynamic parameters and the difference values of associated linkages are derived, as well. The combination procedure of the method is flexible in the application of dynamic parameters identification for a serial robot using a dual quaternion. Furthermore, the proposed DQ (dual quaternion)-based method in this paper has the advantage of lower cost compared with the RBFNN (radial basis function neural network)-based method. The effectiveness of the proposed dynamic parameter identification method for a serial robot has been verified by relevant experiments.  
serial robot; dynamics; parameter identification; dual quaternion Article