esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study on the Development of Tourist Support System Using ICT and Psychological Effects
## 4 History of the Development of a Sightseeing Support Application for Nikko
### Phase 2: Classification of Sightsseeing Information According to the Priority and UI Design
\begin{table}
\begin{tabular}{c l l} \hline \hline
**Level** & **Needs** & **Element of Sightseeing Information** \\ \hline
1 & Biological and physiological needs (such as & \\  & hungry, thirsty) & Food and drink, Restroom \\ \hline
2 & Safety needs (such as protection, security) & Transportation, Access, Accommodation, Wi-Fi, \\  & & Price, Weather, Crowd, Disaster prevention, \\  & & Dangerous animal, Map, Multilingual \\ \hline
3 & Love and belongingness needs (such as & \\  & connecting friend) & SNS, Word of mouth, Letter, Postcard \\ \hline
4 & Esteem needs (Get evaluation and certification) & Response on SNS such as press the like button \\ \hline
5 & Cognitive needs (such as know, understand) & Famous sightseeing spots, Model courses, Events \\ \hline
6 & Aesthetic needs (such as harmony, order, & Explanation of history, Photos of scenery, animals \\  & beauty) & and plants \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mapping among elements of sightseeing information and [PERSON]’s hierarchy of needs. Figure 8: Two different designs of UI: the left one was designed based on layer 1 and 2, and the right one was designed based on layer 5 and 6. * What was the useful information (multiple selections are possible)
* What was the impressive information (multiple selections are possible)
* The following ten items are evaluated on a scale of 1 (not good) to 5 (very good). 1. Easy to operate  
2. The characters are easy to read  
3. The amount of information is appropriate  
4. Good design  
5. Easy to read map  
6. Ease of use of bus timetable  
7. The button layout is correct  
8. The pop-up is easy to see  
9. The pop-up information is appropriate
* Impression of the app (multiple selections allowed)
* Impressions using the app (free description)  
The impression of information is summarized in Figure 10.  
Figure 10: Two different designs of UI: the left one was designed based on layers 1 and 2, and the right one was designed based on layers 5 and 6. Figure 9: Two different designs of UI: the left one was designed based on layers 1 and 2, and the right one was designed based on layers 5 and 6. As a result, when Layout-1 was used, the information on surrounding areas and on the time required to reach bus stops and major sightseeing spots included in restroom information and pop-ups using beacons was highly evaluated. When Layout-2 was used, the guidebook of birds, flowers, and mountains was highly evaluated. We think that the UI design based on the mapping among sightseeing information elements and [PERSON]'s hierarchy of needs is valuable. The detailed discussion is described in [49; 50].
# Study on Strength and Microstructure of Red Clay Reinforced by F1 Ionic Soil Stabilizer
###### Abstract  
High-liquid limit red clay has poor engineering characteristics, namely poor water stability, low strength, and large expansion and contraction deformation. The clay may be reinforced with an F1 ionic soil stabilizer. The engineering characteristics of this reinforced clay were studied, specifically concerning its basic physical parameters, shear strength parameters, and micropore structure. The F1 ionic soil stabilizer significantly improved the water sensitivity, compaction characteristics, and shear strength of red clay. We determined that the optimal F1 ionic soil stabilizer mix was 0.5 L/m\({}^{3}\), resulting in a reinforced clay with plastic limit increased by 45.74%, optimal moisture content increased by 12.12%, maximum dry density increased by 5.8%, liquid limit reduced by 8.4%, plasticity index reduced by 43.8%, infiltration coefficient reduced by 41.8%, cohesion increased 1.64-fold, and internal friction angle increased 1.30-fold. Freeze-thaw cycles reduced the shear strength parameters of the reinforced red clay, although even after 15 cycles, it still had 18.4% higher cohesion and 57.1% higher internal friction angle than undisturbed red clay. The F1 ionic soil stabilizer significantly reduced the pore size and area of red clay, the complex connected pore structure is adjusted to a more regular structure. The reinforced clay had 56.64% lower pore area ratio, 32.27% lower average Feret diameter, and 2.43% lower fractal dimension.  
red clay; ionic soil stabilizer; physical and mechanical properties; microstructure
# Joint Timekeeping of Navigation Satellite Constellation with Inter-Satellite Links
## 2 Joint Timekeeping Framework  
The GNSS system time reference is established and kept by atomic clocks in ground operation control systems or are joined by the satellite clocks. The local time of each satellite is maintained by its spaceborne clock, and it is synchronized to the GNSS system time by two-way satellite time and frequency transfer (TWSTFT) or by multi-satellite precise orbit determination (MPOD) [3; 16; 17], which is called distributed timekeeping. The prediction parameters of clock deviations are broadcast in the ephemeris for positioning and timing. In this section, the establishment method of the inter-satellite link time is introduced and the improvement is evaluated in brief compared to the distributed timekeeping.
# Sequential-Fault Diagnosis Strategy for High-Speed Train Traction Systems Based on Unreliable Tests
## 4 Case Study
### Construction of Traction System Diagnosis Strategy under Reliability Test and Unreliable Test  
Assuming that the test is reliable, based on the fault-test dependency matrix, we use the method in Section 3.4 to construct a single-fault diagnosis strategy of the traction system, as shown in Figure S1.  
According to the diagnosis tree, we can obtain the diagnosis sequence of the traction system using a reliable tests as follows:  
\[\begin{array}{l}t_{7}\to t_{20}\to t_{8}\to t_{23}\to t_{21}\to t_{22}\to t_{9}\to t_{10}\to t_{11}\to\\ t_{12}\to t_{13}\to t_{14}\to t_{15}\to t_{16}\to t_{6}\to t_{13}\to t_{25} \to t_{1}\to\\ t_{3}\to t_{19}\to t_{24}\to t_{5}\to t_{9}\to t_{17}\to t_{3}\to t_{18} \to t_{1}\to t_{4}\end{array}\]  
In this diagnosis strategy, a total of 28 steps need to be performed to isolate all faults. The test and isolation steps required to isolate faults are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test** & **Reliability** & **Test** & **Reliability** & **Test** & **Reliability** \\ \hline \(t_{1}\) & 0.9475 & \(t_{10}\) & 0.9529 & \(t_{19}\) & 0.9350 \\ \(t_{2}\) & 0.9436 & \(t_{11}\) & 0.9398 & \(t_{20}\) & 0.9387 \\ \(t_{3}\) & 0.9550 & \(t_{12}\) & 0.9322 & \(t_{21}\) & 0.9502 \\ \(t_{4}\) & 0.9436 & \(t_{13}\) & 0.9528 & \(t_{22}\) & 0.9580 \\ \(t_{5}\) & 0.9523 & \(t_{14}\) & 0.9511 & \(t_{23}\) & 0.9455 \\ \(t_{6}\) & 0.9416 & \(t_{15}\) & 0.9446 & \(t_{24}\) & 0.9549 \\ \(t_{7}\) & 0.9521 & \(t_{16}\) & 0.9588 & \(t_{25}\) & 0.9590 \\ \(t_{8}\) & 0.9513 & \(t_{17}\) & 0.9536 & \(t_{26}\) & 0.9538 \\ \(t_{9}\) & 0.9476 & \(t_{18}\) & 0.9366 & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Traction system test reliability.  
When the test is unreliable, we can obtain the fault diagnosis tree of the system, as shown in Figure S2.  
According to the diagnosis tree, we can obtain the diagnosis order as follows:  
\[\begin{array}{l}t_{7}\to t_{18}\to t_{23}\to t_{8}\to t_{21}\to t_{22}\to t_{14}\to t_{13}\to t_{15}\to\\ t_{12}\to t_{16}\to t_{10}\to t_{19}\to t_{6}\to t_{11}\to t_{13}\to t_{20} \to t_{19}\to\\ t_{25}\to t_{1}\to t_{26}\to t_{18}\to t_{9}\to t_{5}\to t_{17}\to t_{1} \to t_{3}\to t_{4}\end{array}\]  
In this diagnosis strategy, a total of 28 steps of testing are also required to isolate all faults. The test and isolation steps required to isolate faults are shown in Table 5.
## 4 OFDM-FTN Applications Based on the UEP Algorithm
### Simulation Results  
The performance of the OFDM-FTN transmission method, which combines the efficient OFDM method in multipath channels with the FTN method to enhance the transmission ratio, was analyzed through simulations. In addition, the proposed UEP algorithm was applied to the OFDM-FTN transmission method, and the performance was comparatively analyzed. The simulation parameters are the same as those shown in Table 1 in Section 3.3. The number of the interference rate was 4, which is \(N_{p}=4\). M was the number of subcarriers. The subcarriers, M, of the OFDM-FTN method were set to 4, 8, 16, and 32. The multipath channel environment was simulated by setting the multipath number, \(L\), to 3 in Equation (13) and setting the channel response coefficients, h(0) to 0.8, h(1) to 0.2, and h(2) to 0.1.  
Figure 12 shows the performance of the conventional FTN method and OFDM-FTN method in a multipath channel environment. We compared the performance by setting the FTN interference rate to 30%. The simulation results for the conventional FTN method could not be decoded in the multipath channel environment. However, data for the OFDM-FTN method could be decoded in the multipath channel environment. In addition, we confirmed that the performance improved as the subcarrier number, \(\mathbf{M}\), of the OFDM-FTN transmission method increased. In order to overcome the degradation of performance due to the interference ratio in the OFDM-FTN transmission method in the multipath channels, the proposed UEP was applied to the OFDM-FTN transmission method. The simulation was performed by applying type 4 in Table 3, and the number of subcarriers, M = 4, 8, 16, and 32.  
Figure 13 shows that the number of M subcarriers of the OFDM-FTN method was set to 4, 8, 16, and 32, with UEP settings based on the FTN interference ratio of 30%. The UEP settings shown in  
Figure 12: The performance of FTN and OFDM-FTN in the multipath channel (\(\tau^{\prime}=30\%\)).  
type 4 of Table 3 were applied to the simulations. According to Figure 13, the proposed OFDM-FTN based on the UEP method improved the performance by approximately 0.1 to 0.3 dB, compared to the OFDM-FTN method, at the BER of \(\mathbf{10^{-4}}\). We confirmed that the OFDM-FTN method applying the UEP method also has a better performance than that of the OFDM-FTN method in multipath channels.
## 5 Numerical Results  
### Scenario and Parameter Configuration  
For the WSN scenario under study, the value of its different configurable parameters has been defined in Table 1.  
The reception and transmission power shown in Table 1 have been obtained from [29]. To obtain them, the sensor is considered to operate with a voltage source in the range \([2.7,3.3]\) in volts, the consumed current of the radio transceiver is 17.4 mA while transmitting at 0 dBm, and 19.7 mA in reception mode. If we assume that a sensor operates with a 3 V voltage source, the power consumed during transmission and reception modes is 52 mW and 59 mW, respectively. This approach is similar to the one used in [30], where a model of a communication system that deploys energy harvesting is proposed.
## 4 Experiments
### The Performance of Similar User Recommendation
#### 4.2.1 The Baseline Models  
In this paper, we took a different approach to recommender systems than conventional ones. Conventional recommender systems have the premise that the ratings of different users on various items are available. Then, standard recommendation methods such as user-based and item-based collaborative filtering approaches can be used to learn similar users or similar items on the basis of their past rating behaviors, so that ratings for items that were never given before can be deduced. However, in a real-world scenario in social networks where only post content and hashtags are available, it is not possible to obtain the \"items\" since posts are only posted by one user and replied or shared by a few. It  
Figure 8: The accuracy of the TextCNN Model.  
is not possible for them to be \"rated\" by many different users. In this case, we propose a more realistic approach using hashtags as user preferences for recommending similar users in social networks. When we only have post content and hashtags, the baseline for such a recommendation system is simply content-based recommendation from posts since hashtags have been used as the ground truth.  
In our experiments, we considered two baseline models: baseline_text and baseline_pic. The baseline_text model is based on the representation of user preferences only by the text contents of users' posts. Specifically, for each user, we simply use the distributed representation of the texts posted by that user as their characteristics. First, we converted the words into vectors using the same Word2 Vec embedding model with the same dictionaries in Table 1. Next, vectors for all words in a document were averaged as the representation of the document. We further averaged the vectors of all documents posted by the user as the user feature. Lastly, we calculated the cosine similarity of the current user to all other users to generate a list of recommendations. On the other hand, the baseline_pic model is based on the representation of user preferences only by the image contents of user posts using the ImageCNN model. The only difference is that we took the featuremaps and categories from all images posted by the user as the user feature.
# Analysis of the Refraction Effect in Ultrasound Breast Tomography
## 3 Results
### Analysis of the Effect of Water Temperature on the Refraction of the Beam Path
On the auxiliary \(X\)-axis, the values of the incidence angles of the beam rays at the water/skin boundary determined from the \(\alpha_{IN}\) angles are given. The optimal temperature value due to the minimization of the refraction phenomenon (\(\Delta_{opt}\approx 0\)) for the range of angles \(\alpha_{IN}=0\)-32\({}^{\circ}\) is from 32.6 \({}^{\circ}\)C to 32.1 \({}^{\circ}\)C, respectively. Starting from an angle \(\alpha_{IN}\approx 36^{\circ}\) (i.e., the refraction angle of \(\sim\)58.1\({}^{\circ}\)) the optimal temperature value rapidly decreases to a value of about 24-25 \({}^{\circ}\)C for an angle \(\alpha_{IN}\approx 37.5^{\circ}\) and then quickly rises to a temperature of about 42 \({}^{\circ}\)C near the critical angle, and \(\Delta_{opt}\approx 0\) achievement is impossible over this range. Optimization shows the existence of a characteristic region (refraction/distortion ring) that starts from the angle of incidence from water to the breast skin of \(\sim\)50\({}^{\circ}\), where the phenomenon of refraction of the ultrasound beam rays in the cross-section of the breast immersed in water starts to contribute more and more, and its intensity increases exponentially later.
## 4 Experiments
### Analysis of Experimental Results
#### 4.5.3 Comparison with State-of-the-Art Methods  
To validate the superiority of BGA-Net, we compared our method with state-of-the-art approaches, including RSACNN and FERFC. Table 5 presents the accuracy comparison among these different methods.  
From Table 5, it is evident that our proposed BGA-Net outperforms the method proposed by [PERSON] and [PERSON] [39], which combines handcrafted features with convolutional neural networks for deep feature extraction, by 1.89% on the Fer2013 dataset. In comparison to the approach introduced by [PERSON] et al. [40], which trains multiple classifiers based on the complexity of test samples to address the complexity of facial expression recognition, our BGA-Net achieves similar recognition performance in an end-to-end manner. Similarly, on the CK+ dataset, our method surpasses the approach proposed by [PERSON] et al. [41], which utilizes multi-layer convolutions to extract feature vectors from different ROI regions, by 9.19%. Furthermore, our method outperforms the fusion of local binary pattern-based facial expression images with the original image proposed by [PERSON] et al. [42] by 0.76%. On the JAFFE dataset, our method achieves a 2.18% improvement over the approach presented by [PERSON] et al. [43], which combines the ResNet18 network structure with a multi-head channel attention mechanism. Overall, our approach consistently delivers the best FER recognition results across all three datasets, thanks to the combination of BiGRU and the attention mechanism in BGA-Net, enabling the model to focus on crucial facial expression regions.  
Lastly, we would like to highlight the potential applications of the proposed facial expression recognition method. In the field of human-computer interaction, facial expression recognition can contribute to improving the user experience by adaptively adjusting interfaces and content based on the recognized emotional states of users. In the healthcare domain, it can be utilized to identify patients' emotional states and tailor treatment plans accordingly, thereby enhancing treatment effectiveness. Additionally, in gaming and entertainment, it can enable the recognition of players' emotional states and dynamically adjust factors such as sound effects and visuals to elevate the gaming experience for users.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Method** & **Accuracy/\%** & **Year** \\ \hline \multirow{8}{*}{Fer2013} & [39] [PERSON] et al. & 70.00 & 2023 \\  & [44] [PERSON] et al. & 70.02 & 2021 \\  & [45] RSACNN & 71.33 & 2022 \\  & [40] [PERSON] et al. & 71.67 & 2019 \\  & **BGA-Net(Ours)** & **71.89** & **-** \\ \hline \multirow{8}{*}{CK+} & [41] [PERSON] et al. & 87.20 & 2020 \\  & [46] [PERSON] et al. & 93.53 & 2023 \\ \cline{1-1}  & [47] [PERSON] et al. & 95.00 & 2023 \\ \cline{1-1}  & [42] FERFC & 95.60 & 2021 \\ \cline{1-1}  & **BGA-Net(Ours)** & **96.36** & **-** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison with the accuracy of existing methods.  
Figure 7: ROC curves for the Fer2013, CK+, and JAFFE datasets. (a) Fer2013, (b) CK+, (c) JAFFE.
# A Nonlinear CFD/Multibody Incremental-Dynamic Model for A Constrained Mechanism
###### Abstract  
Numerical analysis of a multibody mechanism moving in the air is a complicated problem in computational fluid dynamics (CFD). Analyzing the motion of a multibody mechanism in a commercial CFD software, i.e., ANSYS Fluent(r), is a challenging issue. This is because the components of a mechanism have to be constrained next to each other during the movement in the air to have a reliable numerical aerodynamics simulation. However, such constraints cannot be numerically modeled in a commercial CFD software, and needs to be separately incorporated into models through the programming environment, such as user-defined functions (UDF). This study proposes a nonlinear-incremental dynamic CFD/multibody method to simulate constrained multibody mechanisms in the air using UDF of ANSYS Fluent(r). To testify the accuracy of the proposed method, Newton-Euler dynamic equations for a two-link mechanism are solved using Matlab(r) ordinary differential equations (ODEs), and the numerical results for the constrained mechanisms are compared. The UDF results of ANSYS Fluent(r) shows good agreement with Matlab(r), and can be applied to constrained multibody mechanisms moving in the air. The proposed UDF of ANSYS Fluent(r) calculates the aerodynamic forces of a flying multibody mechanism in the air for a low simulation cost than the constraint force equation (CFE) method. The results could have implications in designing and analyzing flying robots to help human rescue teams, and nonlinear dynamic analyses of the aerodynamic forces applying on a moving object in the air, such as airplanes, birds, flies, etc.  
CFD/multibody mechanisms; nonlinear dynamics; user-defined functions; ANSYS Fluent(r); constrained dynamic problems; Matlab(r) +
Footnote †: journal: Applied Sciences
# 3-D People Counting with a Stereo Camera on GPU Embedded Board
## 2 Proposed Method
### Object Tracking and People Counting  
The person detected in the top view was tracked using the object tracker. The object tracker also has a long history where various algorithms exist. The proposed method required a tracker with low computational complexity because it was intended to be driven by the Jetson TX2, which is an embedded board. In addition, because of the characteristic of the top view, only the head was displayed, so a tracker using color information cannot be used. Therefore, we tracked the detected head by applying the Kalman tracker [16] and satisfying the above two conditions. Since the Kalman tracker uses only positional information without using color information, the amount of computation is smaller than that of other tracking algorithms.  
In order to count a person, it is necessary to define the relationship between the counting line and the trajectory of the object. In the proposed method, the direction of the object was calculated when the trajectory of the object and the counting line intersected. Figure 10 shows two cases where the trajectory of the object crossed the counting line. Figure 10a shows the direction in which the object's last position and the counting line draw was clockwise. Figure 10b shows the counterclockwise  
Figure 9: Top-view projection results: (**a**) color image; (**b**) height map; (**c**) occupancy map; (**d**) likelihood map; and (**e**) detection result.  
direction in the same way. We used this method to calculate which direction the object crossed the counting line and increased the count for that direction.  
Figure 11 shows the User Interface (UI) of the proposed people counting system. After projecting the side view onto the top view, the head was detected and tracked to determine whether to count it or not.