esa-sceva/satcom-chunk-collection huggingface dataset:
# Large-Scale Aircraft Pose Estimation System Based on Depth Cameras
## 2 Measurement Principles  
As illustrated in Figure 1, the proposed pose estimation method for large-scale aircraft can be divided into the following four steps:  
1. Point cloud collection: The aircraft model is mounted on a turntable and scanned with depth cameras to obtain the original point cloud.
2. Point cloud preprocessing: The coordinate system normalization of the aircraft model point cloud is calibrated using principal component analysis (PCA). A calibration plate is used to fuse the aircraft model point clouds scanned by several depth cameras in different positions and poses.
3. Point cloud registration: Intrinsic shape signatures (ISS) are used to extract the key points of the aircraft point cloud. Coarse registration is performed using signature of histograms of orientations (SHOT) feature description and the random sample consensus (RANSAC) feature-matching method, and the result of the coarse registration is used as the initial value of the fine G-iterative closest point (ICP) registration.  
4. Model pose solution: The quaternion matrix of the registration result is obtained, and the Euler angle is solved and output. The registration results are used to guide the next frame of point cloud registration.
# Evaluation of a Microporous Acoustic Liner Using Advanced Noise Control Fan Engine +
## 6 Test Results
### Comparison of ANCF Test Results of Acoustic Liners  
In this section, a comparison of the acoustic liner performance of two technologies recently tested on the ANCF with the same test conditions as the current work is provided. The two technologies tested on the ANCF engine are as follows:  
1. 3D-printed acoustic metamaterial liner: A multi-degree-of-freedom acoustic metamaterial that is a modification of the traditional perforated panel over a honeycomb liner called a T-liner or Trinity liner that was manufactured using additive manufacturing (3D-printed) and was tested on the ANCF [33].  
Figure 24: Broadband dB loss of microphone 21 at shaft speeds of 1500 to 2000 RPM [29].  
2. Stepwise gradient metal foam liner: Stepwise gradient metal foam liners of two different configurations were tested using the ANCF at the Notre Dame Turbomachinery Laboratory. The details of the assessment are provided in reference [34]. Table 3 summarizes a comparison of the current work microporous liner, 3D printed metamaterial T-Liner, and stepwise gradient metal foam liner.  
Thus, the comparative study clearly demonstrates that the graded microporous material offers superior and consistent SPL reductions across multiple locations and is effective at all of the tested RPMs (1500-2000 RPM), making it a highly effective and promising solution for aircraft noise mitigation. In contrast, the metamaterial T-liner, while showing a higher tonal noise reduction at a single location, was effective only at 1800 RPM; this may limit its broad applicability. The step-wise metallic foam was not effective in reducing the SPLs and enhanced the noise levels at some locations for the reported 2000 RPM and may be unsuitable for these applications. Among the three, the graded microporous material stands out as the most effective noise mitigation technology. Therefore, we conclude that a microporous material with controlled porosity, pore size, and its distribution through the thickness to achieve effective static airflow resistivity and tortuosity is critical in noise attenuation.
# Quantifying Auditory Presence Using Electroencephalography
## 1 Introduction  
Presence is used to assess the participants' sense of \"being there\" in the virtual environment [1]. It can be defined explicitly as the subjective experience of being in one place or environment, even when physically situated in another [2]. In other words, it reflects the extent to which the participants' cognitive and perceptual systems are tricked into believing they are somewhere other than their physical location [3]. Generally, the term presence refers to physical presence in many applications, though it can also be classified as social presence or co-presence [4, 5].  
Many researchers have assessed presence earlier, but most of them focused on the visual aspect of the stimuli, for example, the size and resolution of the screen [6], or the visual fidelity of the animation [7]. Less attention was paid to the auditory aspect of the stimuli. Regarding research focusing on the auditory cues of presence [8, 9, 10, 11, 12, 13], most experiments still used visual display to accompany auditory stimuli. Besides, the audio stimuli these researchers used were mostly stereophonic or binaural recordings, whereas a loudspeaker array is used in the present work.
## 3 Experimental Results and Analysis  
We implemented our algorithm using C++ and Python programming languages and collected our experimental results on a Personal Computer equipped with an i7-10610U CPU, 16 GB RAM, and Windows 10 operating system. Unlike LDR data hiding, no standard image database is available for HDR image, and we instead collect our 20 test images form the Internet [36] and indexed them from test image 1 to test image 20. We remark that some of these HDR images have been adopted as test images in the literature [18; 19; 20; 29; 30]. In the following, we first report the secret message embedding results and then present the security analysis of the encrypted stego HDR images.
## 5 Performance Evaluation
### Localization Success
In contrast, RLS with only 5% of anchor ratio and the same node number can achieve this same level of localization ratio. Moreover, under the same grid deployment conditions, our scheme to reach 80% of localization ratio needs only 3% anchor ratio. For localization success of all 250 sensor nodes in DNRL and LSL, 35 beacon percentage is required, while in RLS the anchor ratios required are 12% and 8% in random and grid anchor deployments, respectively. DNRL and LSL pose high cost related with the high number of anchor nodes needed to achieve a proper localization ratio.
# BSTCA-HAR: Human Activity Recognition Model Based on Wearable Mobile Sensors
## 4 Experiment and Results
### Experimental Setup  
Utilizing an RTX 3090 GPU for effective computation, experiments were carried out on a Windows 10 operating system and implemented using the TensorFlow and Keras deep learning frameworks. Python 3.8 was utilized as the programming language. All layers commenced their training with random weights and biases during the fully supervised training phase of the model. To calculate the error between the expected and actual numbers, the cross-entropy function was utilized. The network used the Tanh function specifically for its attention layer while utilizing the ReLU nonlinear activation function for the network; the Adam optimizer was chosen for training. During the data preparation phase, signal interference or equipment malfunction during data collecting led to missing values, which were first filled using linear interpolation. To lessen the training bias, the data characteristics were then standardized to a range of 0 to 1. Ultimately, a sliding window with a 50% overlap was utilized to partition the data, ensuring continuity across the sequential elements. For the WISDM and UCI-HAR datasets, the sliding window length was set to 128, while for the ISLD dataset it was set to 28. The user ID of the subject served as the basis for data segmentation. To achieve optimal performance, extensive hyperparameter tuning was conducted, including a grid search to determine the optimal learning rate of 0.001, batch size of 192, and dropout rate of 0.2. The number of training epochs was set to 100, with a learning rate scheduler employed to gradually reduce the learning rate of training, ensuring the accuracy and stability of the model.
# Optimizing Ambiance: Intelligent RGB Lighting Control in Structures Using Fuzzy Logic
## 4 Results
### Software  
Designing software to control an RGB lighting system based on AIoT technology that works both online and offline requires special considerations. The Algorithm 1 describes the necessary functions and steps to achieve this purpose.  
```
1:procedureCONTROLAIoT\(\triangleright\) System Operation
2:\(credentialWiFi\gets user\And key\)
3:while\(CONNECTED_{WiFi}\
eq 0\)do\(\triangleright\) Try to connect
4:\(State\gets Neutral\)
5:\(control_{AIoT}\gets READ(Temp,RH,UV,AP)\)
6:\(RGB\gets DESFUZZY(control_{AIoT})\)
7:\(delay(1000)\)
8:\(WiFi_{BEGIN}\)
9:endwhile
10:\(influxDBClient\gets IP\And PORT\And DB\)
11:while\(CONNECTED_{influxDB}\
eq 0\)do\(\triangleright\) Try to connect
12:\(State\gets Select_{OFFLINE}()\)
13:\(control_{AIoT}\gets READ(Temp,RH,UV,AP)\)
14:\(RGB\gets DESFUZZY(control_{AIoT})\)
15:\(lpDisplay(Temp,RH,UV,AP,State,RGB)\)
16:\(delay(1000)\)
17:\(influxDB_{BEGIN}\)
18:endwhile
19:On_Line\(CONNECTED\)\(\triangleright\) Dashboard
20:\(State\gets Select_{ONLINE}()\)
21:\(control_{AIoT}\gets READ(Temp,RH,UV,AP)\)
22:\(RGB\gets DESFUZZY(control_{AIoT})\)
23:\(GRAFANA\_{Disp}(Temp,RH,UV,AP,State,RGB)\)
24:UPDATE_DESFUZZY
25:endprocedure
```  
**Algorithm 1****RGB lighting AIoT control**  
Some of the specific objectives of the algorithm are as follows:  
* Controlling RGB lighting: The \(DESFUZZY\) function is responsible for controlling RGB lighting, allowing the user to turn on, turn off, and adjust the color, intensity, and other parameters related to lighting.
* Communicating with the online AIoT system: The \(influxDB_{Begin}\) function establishes communication with the online AIoT system, which may involve sending commands and receiving responses or updates on the status of the lights.
* Storing and loading the configuration in offline mode: The \(WiFiBegin\) functions are used to store the configuration locally when there is no internet connection and to load it later. This ensures that lighting settings are preserved and can be applied in the absence of an online connection. This connects to the INTRANET.
* Initializing and running the main loop of the system: The \(CONTRLAIoT\) algorithm describes the steps to initialize the system, including loading the previous configuration in offline mode. Then, the main loop is executed, where the user's input is read and the corresponding actions are performed. Finally, a proper termination of the system is ensured, storing the updated configuration in offline mode.  
The general purpose of this algorithm is to provide a comprehensive design for software that allows for controlling an RGB lighting system using AIoT technology, providing the ability to operate online and offline. In addition, it allows the user to control and adjust the lighting interactively and with standard operation for the system's basic connections by simply powering the controller.
## 3 Results and Discussion
### Calorimetry: Heat of Reaction Curves, Reaction Enthalpy
The recorded reaction enthalpy was 75.4 \(\pm\) 3.6 kJ/mol, which aligns with the literature data as well but is marginally higher than the enthalpy found for the polymerisation in isopropanol. Figure 10: Heat of reaction curves of acrylic acid polymerisations in isopropyl alcohol (150 g/L, 0.5 mol% AIBN). Here, the reaction times are aligned to the beginning of the reaction according to the calorimetric global conversion. The inner graph is a copy using a logarithmically scaled time axis and displays the negative range of the reaction heat. Assuming that the polymerisation reaction is the main reaction occurring, the polymerisation rate \(R_{\text{P}}\) can be defined as the quotient of the reaction heat \(\dot{Q}_{r}\) and the reaction enthalpy \(-\Delta H\)[37]:  
\[R_{\text{P}}=\frac{\dot{Q}_{r}}{-\Delta H}. \tag{5}\]  
Furthermore, the calorimetric global conversion \(X_{\text{calor}}\) can be calculated by integrating the polymerisation rate over the time \(t\) and dividing it by the initially fed amount of monomer \(M_{0}\)[37; 38]:  
\[X_{\text{calor}}=\int_{0}^{t}\ \frac{R_{\text{P}}}{M_{0}}. \tag{6}\]  
The calorimetric conversion was used here as an aid to determine the start of polymerisation in the plotted curves for reaction heat. However, since only Raman spectroscopic conversion determination was used for the evaluations within this article, the following figure compares the conversion curves that can be obtained from calorimetry with those from Raman spectroscopy. Figure 11 shows an example of this for the polymerisations in isopropanol with 1.0 mol% AIBN. It is noticeable that the conversion curves coincide somewhat better at higher temperatures than at lower temperatures. The course at 75 \({}^{\circ}\)C is almost completely congruent. Since isothermal calorimetry in this case involved small differences between \(T_{\text{R}}\) and \(T_{\text{J}}\), and only Raman spectroscopy specifically measures the decrease in the monomer band in the reaction mass, deviations are conceivable here.
# Open-Set Specific Emitter Identification Based on Prototypical Networks and Extreme Value Theory
## 4 Experimental Results Analysis
### Comparison of Recognition Performance of Different Models  
#### 4.3.1 Comparison of Recognition Accuracy  
The comparison of the results from the ZigBee devices is shown in Figure 9. The model in this paper is called EVT-Shuffle-SE. To verify the recognition performance of EVT-Shuffle-SE, the model was first compared with EVT-Shuffle without the SE module and EVT-SE without group convolution and channel shuffle. Then, EVT-Shuffle-SE was also compared with OpenMax [26; 27], Center_Loss [29], and CPN [33]. We used the same network structure as CPN, OpenMax, and Center_Loss. It can be seen that the recognition accuracy of the model was effectively improved by introducing the attention mechanism after adding the SE module. EVT-Shuffle-SE with group convolution and channel shuffle also showed a slight improvement over EVT-SE with fewer network parameters. Meanwhile, the model proposed in this paper had an advantage over the other models at a lower SNR. When the SNR was greater than 0 dB, the recognition accuracy of our model reached more than 95%.  
Figure 8: Comparison of recognition accuracy with different feature dimensions.  
In Figure 10, the confusion matrix for open-set recognition is plotted at \(-6\) dB, \(-2\) dB, 2 dB, and 6 dB, respectively. It can be seen that device 1 and device 2 were easily confused, and device 3 was more independent. Even at a lower SNR, the signals from device 4 and device 5 were successfully rejected as an unknown class.  
We also conducted experiments on the USRP 310 device. As shown in Table 1, the experimental results demonstrated that the recognition accuracy of the model still reached  
Figure 10: Confusion matrix under different SNRs: (**a**) confusion matrix at \(-6\) dB; (**b**) confusion matrix at \(-2\) dB; (**c**) confusion matrix at 2 dB; (**d**) confusion matrix at 6 dB.  
Figure 9: Comparison of recognition accuracy of different models: (**a**) comparison under different modules; (**b**) comparison with other models.  
more than 90% for the 10 types of devices under mixed SNRs. The model also had better recognition performance.  
Figure 11 shows the confusion matrix for 10 types of devices. It can be seen that the rejection rate for the three unknown devices reached 84%.
## 4 Results: The Usage of CARIMO
### Results of the CARIMO Usage Data Analysis  
#### 4.5.1 Usage Frequencies of CARIMO  
Table 4 shows the participant flow and the median use of CARIMO (total and per country). The take-up of the CARIMO app remained quite stable over time while the median use per user of the activity tracker declined in the last two months.  
Country-specific analysis revealed different use patterns over time in Austria and in Italy. In the first four months, the median use of the tablet in Austria was twice as high as in Italy. In month six, the use of the CARIMO app in Italy even slightly exceeded the Austrian level. The activity tracker use was higher in Austria than in Italy, while in the last two months, it decreased significantly in both countries (Table 4).  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & **Test Month** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline Total users & 104 & 97 & 94 & 89 & 89 & 85 & 85 & 85 \\ \hline Users per country & & & & & & & & \\  & Austria & 64 & 60 & 57 & 55 & 55 & 54 & 54 & 54 \\  & Italy & 40 & 37 & 37 & 34 & 34 & 31 & 31 & 31 \\ \hline Median usage of CARIMO app: median visits & 32 & 36 & 27 & 30 & 29 & 27 & 33 & 29 \\  & Activity tracker; median days & 14 & 18 & 15 & 20 & 17 & 15 & 3 & 0 \\ \hline Median usage of CARIMO in Austria (per user/month) & & & & & & & & \\  & CARIMO app median visits & 44 & 45 & 38 & 38 & 39 & 30 & 33 & 35 \\  & Activity tracker median days & 17 & 20 & 20 & 28 & 22 & 20 & 16.5 & 0 \\ \hline Median usage of CARIMO in Italy (per user/month) & & & & & & & & \\  & CARIMO app median visits & 18 & 18 & 18 & 23 & 26 & 32 & 17 & 32 \\  & Activity tracker median days & 6 & 10 & 5 & 11 & 9 & 3 & 0 & 0.5 \\ \hline \hline \end{tabular}  
Notes: Source: SRFG, CIM usage data 2017/2018.  
\end{table}
Table 4: General use of CARIMO per test month.
# The Use of Artificial Intelligence in eParticipation: Mapping Current Research
## 4 Results
### Mapping the Domain
#### 4.2.6 AI Areas  
Most studies explicitly mention a specific AI area with the exception of only two (see Table 6). Machine Learning and Natural Language Processing (NLP) are mentioned in 43 and 37 studies, respectively. Notably, all articles published in the last three years refer to at least one Machine Learning or NLP sub-area. An AI area mentioned in only one study is Robotics. This paper was published in 2023, suggesting that there might be more studies in this area in the future.  
Different AI sub-areas are identified in 44 studies. Deep learning, a technique used to train AI models [60], has been applied in 18 studies. In addition, classification is used  
\begin{table}
\begin{tabular}{l l} \hline \hline
**AI Area** & **Paper ID** \\ \hline Machine Learning & [41; 42; 43; 44; 45; 46; 47; 48; 49; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 62; 63; 64; 65; 66; 67; 68; 69; 70; 72; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 86] \\ \hline Natural Language Processing & [41; 42; 44; 45; 47; 48; 51; 53; 55; 56; 57; 58; 59; 60; 63; 70; 72; 74; 75; 76; 77; 78; 79; 81; 82; 84; 86] \\ \hline Robotics & [50] \\ \hline \hline \end{tabular}
\end{table}
Table 6: AI areas.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Evaluation** & **Paper ID** \\ \hline Quantity & [41; 44; 60; 62; 65; 66; 69; 74; 81; 86] \\ \hline Tone and style & [41; 53; 54; 61; 63; 65; 69; 70; 71] \\ \hline Demographics & [41; 49; 60; 74; 79] \\ \hline Transparency and openness & [41; 60; 65; 76] \\ \hline \hline \end{tabular}
\end{table}
Table 5: eParticipation Evaluation.  
in 28 studies. The extensive use of classification is due to the fact that the main source of communication between citizens and governments is social media or other similar platforms. Consequently, AI methods are employed to analyze people's emotions and thoughts expressed via posts and text in general. Apart from classification, other techniques related to text processing are frequently used, such as sentiment analysis, topic modeling, and text mining.  
Classification constitutes a supervised learning technique. Most articles refer to text classification. Therefore, supervised learning is the machine learning sub-area with the highest frequency, appearing in 31 studies. Unsupervised learning is also preferred, as it is closely linked with topic modeling. Finally, chatbots seem to have hopeful prospects, since four out of the five studies about chatbots in eParticipation activities were published in the last 3 years.
###### Abstract  
The Huailai area is rich in geothermal resources, but the formation mechanism of its deep heat source is still unclear. In this paper, based on 16 broadband magnetotelluric sounding points, the two-dimensional electrical structure of the crust and mantle in the Huailai area was obtained. Combined with deep seismic reflection and P-wave seismic tomography, the geophysical characteristics of deep heat sources and reservoirs in the Huailai area are described. The Huailai area is characterized by low resistivity and layered reflection above 2 km in depth, which shows the distribution of the Cenozoic sedimentary cover layer. The upper crust is characterized by high resistivity without an obvious reflector, corresponding to the crystalline basement of the basin, whose main lithology is Archean genesis. There is a highly conductive and bright-spot-reflective structure under the basement, which extends to 100 km, indicating the upwelling of mantle-derived material. Combined with the results of helium isotope tracing, a magma-type geothermal model in the Huailai area is proposed. The upwelling mantle-derived magma material is enriched under the basement to form a heat source. The heat is transferred to the upper crust through heat conduction along the crystalline basement. Then, groundwater circulation brings deep heat to the surface, forming hydrothermal resources.  
geothermal formation; mantle-derived materials; deep structure; magnetotelluric sounding; Huailai area +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Link Budget Simulation
#### 3.7.2 Link Margin  
Using the link budget analysis conducted in Section 2.6, the link margin was calculated and plotted in Figure 20 for different satellite elevation angles while varying the SF. It is noticeable that increasing the elevation angle will enhance the link margin so that at 90 degrees, the link margin will be maximum for a certain SF. In addition, increasing the SF will also lead to an increase in the link margin. Additionally, for small elevation angles with an SF of 7 or 8, the results show that the link margin will be negative, which is undesirable.