esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Model of Underwater Wireless Sensor Network  
A 3D UWSN is used to detect and observe situations that cannot be fully observed through a submarine sensor node (i.e., for collaborative sampling work in a 3D marine environment). In a 3D underwater network, sensor nodes are suspended at different depths in the water for detection. In this structure, each sensor node is anchored to the bottom of the sea and equipped with buoys that can be inflated with pumps, as shown in Figure 1. The buoys can push sensors to the surface. The depth of a sensor can be adjusted by adjusting the length of the cord connecting the sensor and the anchor via an electric motor mounted on the sensor. Each sensor network has a surface receiver for collecting data and a surface base station for collecting, processing, integrating, and transmitting information.  
The sensor nodes in this paper use the 3D Boolean sensing model. Assuming that the coordinates of underwater sensor node \(n_{i}\) in the 3D coordinate system of the monitoring area is \((x_{i},y_{i},z_{i})\), the sensing radius is \(r_{s}\). Therefore, the sensor node \(n_{i}\) sensing model is a sphere with a spherical center \((x_{i},y_{i},z_{i})\) and a radius \(r_{s}\), labeled a sensing ball in Figure 2. For sensor node \(n_{i}\), when the target object is in the sensing ball of \(n_{i}\), then the target can be sensed by; if it is outside the sensing ball, \(n_{i}\) cannot detect this target object.  
We set the coordinates of any point \(Q\) in the 3D coordinate system of the monitoring area as \((x_{q},y_{q},z_{q})\). The Euclidean distance from point \(Q\) to sensor node \(n_{i}\) is \(d(n_{i},Q)=\sqrt{(x_{q}-x_{i})^{2}+(y_{q}-y_{i})^{2}+(z_{q}-z_{i})^{2}}\); therefore, the probability that the sensor node \(n_{i}\) can perceive point \(Q\) is shown in Equation (1).  
Figure 1: 3D model of underwater wireless sensor network.  
Figure 2: Boolean sensing model.  
\[P(n_{i},Q)=\{\begin{array}{ll}1,&d(n_{i},Q)\leq r_{s}\\ 0,&d(n_{i},Q)>r_{s}\end{array} \tag{1}\]  
According to the sensor node composition, wireless sensor sensing modules that consume energy consist of the module, calculation module, and wireless communication module. The energy consumption of each part of the node is presented in Figure 3. The wireless communication module consumes most energy, whereas the sensor node's sensing module and calculation module consume less. Wireless communication modules are usually divided into four states: sending, receiving, idle, and sleep. Among them, energy consumed in the sending state is the highest of the four, energy consumed in the receiving state and the idle state is moderate, and energy consumed in the sleep state is minimal. When the node is idle, it must also monitor the channel constantly to determine whether data are being sent to itself; thus, it consumes energy. In the sleep state, the node turns off the sensing and communication modules, greatly reducing energy consumption.  
In this paper, the energy consumption model only considers energy consumption when sending data, receiving data, and when in an idle state.
## 4 Case Study  
### Description of the MS Monitoring System and Actual MS Signal  
The actual MS signal comes from the safety construction monitoring system of the underground chamber of Shuangjiangkou Hydropower Station in Southwest China. In view of the severe problem of rock mass deformation and rockburst disasters in the underground main powerhouse, the seismic monitoring system of the Canadian Engineering Seismology Group (ESG) was adopted [38; 39; 40]. The seismic monitoring system mainly includes Paladin digital signal acquisition system, Hyperion digital signal processing system, acceleration sensor, cable fiber, and so on, as shown in Figure 10a. In the underground main powerhouse, two groups of acceleration sensors were installed; each group has six. They were installed in the upper and middle drainage gallery, as shown in Figure 10b. The monitoring region mainly covers the main powerhouse, involving the main transformer chamber, the surrounding rock of part of the pressure pipeline.  
Figure 9: The comparative experiments under different noise levels. **(a)** The original signal. **(b)** The EMD_CS_ST denoised signal. **(c)** The DWT denoised signal. **(d)** The EEMD denoised signal. **(e)** The VMD_DWT denoised signal. **(f)** The _SNR_ before and after denoising.  
The acceleration sensors with a frequency response of 20 Hz to 5000 Hz were arranged in a spatial network distribution. The sensitivity of the acceleration sensor is 30 V/g, and the Paladin digital signal acquisition system has a sampling frequency of 10 kHz. According to the Short Time Average and Longtime Average algorithms (STA/LTA), the threshold of the trigger channel is set to 4. If multiple sensors detect the seismic waves generated by a rock fracture and the corresponding value exceeds the threshold, the sensors record them immediately. These recorded electrical signals will be sent to the digital acquisition system for processing and transmitted to WaveVis software via fiber optic cables to obtain a valid MS signal [41]. Often, the signal received by the system is not only the signal generated by rock fracture but also some blasting, mechanical vibration, current sound, and other noise signals. These mixed signals need to be denoised. After the acquisition, these electrical signals are transmitted to the processing system via fiber optic cables.  
Figure 10: The layout of the MS monitoring system in the main powerhouse. (**a**) The MS monitoring system network topology. (**b**) The layout of the acceleration sensor.
## 5 Discussion  
This paper proposes a study on generating sports commentary by analyzing and tracking broadcast videos using deep learning technologies and generating commentary based on generative AI techniques. To generate sports broadcasts, the integration of court recognition, player behavior recognition and tracking, game event recognition, and generative AI-based commentary generation is essential. This study presents a complete process for commentary generation through the integration of these individual technologies. Analyzing sports videos requires stable recognition and tracking technologies, considering domain knowledge about the game, complex backgrounds, occluded movements, and irregular lighting conditions. Additionally, the rapid pace of the game results in frequent movements of the camera and players, making detection challenging. Pre-existing knowledge of teams, referees, and players is also necessary to differentiate between them accurately. In this study, domain knowledge is utilized, including information on team colors, the number of players and referees, team associations, and court layout, to support player recognition. Homography is calculated using panoramic images, and multi-view camera footage is aligned to enable tracking from a bird's-eye view. Furthermore, court recognition is enhanced by applying YOLO-based segmentation to identify court areas. For player actions and ball recognition, the YOLO algorithm is fine-tuned, and recognition is performed using the R(2+1)D action recognition model. The system demonstrates high performance, achieving an average accuracy of 97% in court calibration, 92.5% in player and object detection, and 85.04% in action recognition. Key game events are identified based on positional and action data, and broadcast commentary is generated using GPT APIs and converted into natural audio commentary through Text-to-Speech (TTS). The limitations of this study lie in its focus on basketball games and rules, which constrain the scope of predictions. Future research will explore domain adaptation to apply the proposed approach to various sports. Additionally, while some examples of generated commentary were evaluated to assess naturalness, future work will include quantitative evaluations and detailed analyses of the naturalness of the generated commentary.  
Figure 11: AI commentary generation result.
# Unmanned Aerial Vehicle Based Wireless Sensor Network for Marine-Coastal Environment Monitoring
## 2 SIMMA Functional Overview  
The implementation of this multi-device solution was sought out to cover the necessities of the SIMMA research project in Mexico. The aim of this project is to monitor different variables in the Mexican shoreline, using low-power and low-cost sensing buoys which can be scattered across a wide area. The buoys, as stated before, are to be used mainly as drifting devices, although a scenario where they are used as stationary buoys, by anchoring them to the seafloor, has also been contemplated within this project. To be able to access their data, a UAV is to be used as a mobile data harvester and network manager. By design, UAVs can be controlled across great lengths using different wireless communications, using mostly different types of UHF transmissions for video and flight data feeds, as well as flight controls. To be able to act as a network router, it was important to select a wireless communication technology which would not affect the operation of the UAV, as any interference could have catastrophic consequences. One of the key characteristics of LoRa is its unique modulation, which allows its coexistence and operation with other RF technologies presumably without interference [34], thus the use of this type of transmission seems adequate for its interaction with the UAV and its multiple radio modules.  
Under the SIMMA project, there are two main functional scenarios: data extraction and search and rescue. In both cases, the buoys will be periodically collecting data regarding water temperature, wind speed and its GPS coordinates, as well as air temperature, pressure, and humidity. In the data extraction scenario, after the buoys have been set on the shore line and ambient variables have been collected, the UAV will fly in the last known direction of the buoys, periodically searching for them. Once the buoys have been found, the UAV will start transmitting data acquisition messages to collect the data stored in the buoys. After the completion of this task, or if it starts running low on battery, the UAV will return to home. Given that the buoy recruiting and data collection process is fully automated, to be able to monitor the network activity of the SIMMA system, a secondary optional ground station can be set on the beach. This ground station will not interact with the UAV, it is only meant to be used as a network analyzer or sniffer (Figure 1).  
In the search and rescue scenario, the UAV will be used to locate missing buoys that may have drifted away from the rest of the swarm. Given that the buoys may be either stranded or in a distant location, the UAV will be flown in the suspected direction of the buoys and will try to collect the position of any buoy in its path before its return to home. This functionality simplifies the recovery of the buoys, which, in many cases, are lost to strong currents or vandalism [10; 35; 36].
## 2 Materials and Methods
### Methods
#### 2.2.3 Characterization of the Coated Scaffolds  
The PCL scaffolds were measured and weighed for characterization. The strand width, strand height and pore size were determined using a VKX-210 3D laser scanning microscope (KEYENCE, Osaka, Japan). Scaffold dimensions were measured using a digital caliper (Burg-Wachter, Wetter-Volmarstein, Germany). The weight was determined using a Practum analytical balance from Satorius. A minimum of five scaffolds were measured by the different characterization methods. The surface roughness was measured for coated and pure PCL scaffolds. The 3D laser scanning microscope VKX-210 from KEYENCE was also used for this purpose. The images for evaluation were taken 400x magnification. The surface roughness was evaluated and calculated using the VK Analysis Module (V3.5.0.0). In order to be able to make a statistically significant statement, at least five scaffolds per group (coated/uncoated) were measured at five different locations. To analyze the microstructure of the PCL scaffolds, images were taken with an ESEM (FEI Quanta 250 FEG, Thermofisher Scientific, Waltham, MA, USA). For this purpose, the scaffolds were glued with a carbon conductive pad to the sample pin holder and examined with an excitation voltage of 10-20 kV. The magnifications HFW (Horizontal Field Width) were 746 \(\upmu\)m and 373 \(\upmu\)m.  
To determine the integrity of the collagen coating, the collagen-I coated scaffolds were first washed with Phosphate Buffered Saline (PBS). After washing, they were incubated for 45 min at room temperature in a so-called blocking solution. The blocking solution consists of PBS, 1% BSA and 0.1% Triton X-100. Subsequently, the scaffolds are washed again 3 times with PBS. The primary antibody was then added, this was previously dissolved 1:1000 in a buffer (lowcross buffer). For the detection of collagen, the antibody \"Mouse anti-human collagen 1 (ab6308)\" at a concentration of 1.5 mg/mL was used as primary antibody. After addition of the primary antibody, the scaffolds are incubated at 4 \({}^{\circ}\)C overnight. Subsequently, the scaffolds were washed again 3 times with PBS. Now the scaffolds were incubated in the secondary antibody (dissolved 1:500 in lowcross buffer) for 60 min at room temperature. The secondary antibody is \"Goat anti mouse Alexa 488\" antibody from ThermoFisher Scientific. These antibodies are labeled with a fluorescence chrome, allowing them to be examined later with a fluorescence microscope (Olympus BX-53, Olympus, Shinjuku, Japan) with 100-500 ms illumination time (excitation at 495 nm (blue), emission 519 nm (green)) [21]. The thickness of the collagen-I coating was examined with ESEM and image analysis with ImageJ (FIJI modification).
# Fusion of Telecommunications and IT Services Boosted by Application Programming Interfaces
## 4 Fusion of Telco and IT Services
### IMS Data Channel  
The 3 GPP has recently introduced a new concept that enables the usage of a data channel in IMS (IP Multimedia Subsystem) call sessions to enrich the end user call experience [49]. It enhances the native dialect application of mobile devices with browser capabilities, which could receive HTML and JavaScript content over the IMS Data Channel, which is yet another channel/media type besides regular voice and video. It allows MNOs to provide in-call web applications without the need to install OTT apps from the Google Play Store or the Apple App Store. An IMS Data Channel (DC) can be established between the device and the network, between the device and an application server, or between devices, and can be used for sending or receiving any kind of data (for example the HTML page itself, Augmented Reality- or Virtual Reality-related data, database queries, or content sharing). Different applications may have different bandwidth requirements; therefore, the guaranteed bandwidth of an IMS Data Channel may be controlled by the network.  
Like API-based services, IMS Data Channel applications may also be easily integrated with IT services. However, they provide different services. API-based services are more telecommunications-oriented: At certain call events, they can affect routing and charging, and they may implement interaction with the end user using network announcements. On the other hand, an IMS Data Channel extends the phone capabilities with a browser that can deliver interactive solutions to the end user during calls without any effect on the routing or charging but opening the door towards augmented communication [65; 66; 67; 68; 69]. Therefore API-based services are device-agnostic, while an IMS Data Channel requires special capabilities from the device(s).  
The GSMA proposes an initial list of use cases, which can be extended with even more exciting ones [66; 67], including the following:  
* A visual menu for the support center;
* Call queue information;
* An interactive catalog;
* Content sharing;
* Online gaming;
* Personalized targeted advertisements;
* Real-time speech transcription and translation [35].
## 3 Results and Discussion  
### Characteristic of InGaN-Based Micro-LED  
The results of the light-output power versus current (LI) and current versus voltage (IV) test of InGaN-based Micro-LED are shown in Figure 5a. It can be observed that the threshold voltage of the micro-LED is about 2.4 V, and as the voltage increases, the LI curve gradually becomes linear. When a small AC modulated signal is added, the optical power will change linearly; therefore, micro-LED modulation performance is very suitable for UWOC systems [32; 33].  
The electroluminescence (EL) spectra in Figure 5b were measured using a JETI Spectraval 1501 Spectrometer. The experimental results show the InGaN-based mrcro-LED peak wavelength at 80 mA is around 522 nm with a full-width at half-maximum (FWHM) of 33 nm.  
Figure 6a presents the performance of the InGaN-based micro-LED frequency response and summarizes the characteristic of \(-3\) dB modulation bandwidth at different injection currents from 10 mA to 100 mA [34; 35]. The dashed line indicates the \(-3\) dB bandwidth level. Figure 6b shows that with the injection current increasing from 10 mA to 100 mA; the \(-3\) dB bandwidth is also increasing from 26.3 MHz to 98.1 MHz. Note that the APD photodetector used for the frequency response test only has a bandwidth of 100 MHz, which limits the maximum measured \(-3\) dB modulation bandwidth of the InGaN-based micro-LED. In consideration of the working life of the InGaN-based micro-LED, we chose 80 mA, which corresponds to a voltage of 5.1 V combined with the alternating voltage of 1.4 V for the operation point of the UWOC system.  
Figure 5: (**a**) The I-V and L-I characteristics of the InGaN-based Micro-LED, (**b**) electroluminescence spectra of the InGaN-based Micro-LED.  
Figure 6: (**a**) Normalized frequency responses of the InGaN-based micro-LED and (**b**) the extracted \(-3\) dB modulation bandwidth characteristics of the InGaN-based micro-LED at different bias currents from 10 mA to 100 mA.  
Moreover, we put forward a UWOC system using InGaN micro-LED arrays, including 5000 pixels packaged by flip-chip technology. The pitch size of the micro-LED is 15 \(\upmu\)m by 15 \(\upmu\)m. The InGaN micro-LED arrays in parallel have an optical power of up to 3.5 mW at a 300 mA drive current. The system achieved a high data rate of 92 Mb/s with a BER of 3.41 \(\times\) 10\({}^{-3}\) and was estimated to reach 50 m in tap water and 10 m in seawater. Thus, we balanced the high bandwidth and the optical power in UWOC links.
# Analysis and Demonstration of First Cross-Support Interferometry Tracking in China Mars Mission
## 3 Results
### Correlation of Data Processing  
#### 3.1.1 Clock Estimation  
Interferometric fringe of CEB and NNO could be obtained with apriority clock information supplied (if there is any), while when it comes to correlation raw data from different agencies, such as baseline CEB and KSH, NNO and KSH, a high-quality fringe could not be identified. Algorithm researched in Section 2.3.2 is adopted to perform the clock estimation, and the clock offset could be acquired effectively between the cross baseline. Figures 8 and 9 show the iteration statistics and interferometric fringe for cross-baseline CEB-KSH when doing clock estimation.  
\begin{table}
\begin{tabular}{c c c} \hline
**Time (Minute)** & **Target** & **Station** \\ \hline T0-T0 + 5 & Spacecraft & CEB/NNO/KSH \\ T0 + 7-T0 + 17 & Quasar & CEB/NNO/KSH \\ T0 + 19-T0 + 29 & Spacecraft & CEB/NNO/KSH \\ T0 + 31-T0 + 41 & Quasar & CEB/NNO/KSH \\ T0 + 43-T0 + 53 & Spacecraft & CEB/NNO/KSH \\ T0 + 55-T0 + 65 & Quasar & CEB/NNO/KSH \\ T0 + 67-T0 + 77 & Spacecraft & CEB/NNO/KSH \\ T0 + 79-T0 + 89 & Quasar & CEB/NNO/KSH \\ T0 + 91-T0 + 101 & Spacecraft & CEB/NNO/KSH \\ T0 + 103-T0 + 113 & Quasar & CEB/NNO/KSH \\ T0 + 115-T0 + 120 & Spacecraft & CEB/NNO/KSH \\ \hline \end{tabular}
\end{table}
Table 2: Typical cross-support session schedule.  
Figure 7: Tracking station distribution and intercontinental baseline: KSH and CEB forming a baseline of about 6400 km, KSH and NNO forming a baseline of about 8000 km, CEB and NNO forming a baseline of about 11,650 km.
# Multitask Learning with Knowledge Base for Joint Intent Detection and Slot Filling
## 1 Introduction
In recent research, [PERSON] summarized the joint model, which has become the mainstream method, and some scholars have adopted the external knowledge base (knowledge graph) in applied research [15]. Based on this, we introduce a solution for the above problems by improving the loss function to optimize the joint model and verify the validity of an external knowledge base in the joint model with intent detection and slot filling. In order to solve the above problems, we propose a joint model of intent detection and slot filling based on multitask learning (MTL) with a knowledge base, which makes full use of the external knowledge and the high-quality relationship information between intents and slots. This is particularly appropriate as the latest method to pre-training language representation, bidirectional encoder representations from transformers (BERT) is used to convert text inputted into a vector in this model. Compared with alternative methods, BERT can be applied to extract the semantic features of the text inputted. Furthermore, the shared parameters and features between the two modules are extracted. This part is introduced to make use of mutual promotion and shared resources between the two modules of intent detection and slot filling. Then, the text that the user inputs is too short to fully express information in practice. In order to increase information other than the text, the external knowledge, such as the WordNet knowledge base, is employed as well. To the best of our knowledge, this is the first time that a knowledge base has been introduced to build an intent-detection and slot-filling model. In addition, a weighted-loss function based on a weighted self-learning algorithm is built to optimize the joint model. Considering the strong correlation between the intents and the slots, our model achieves better performance by using external knowledge and mutually promoting the two modules. Our main contributions are threefold:  
1. Sharing information between the intention-detection module and slot-filling module by the multitask learning method, which avoids the problem of error propagation easily caused by the traditional pipeline method. 2. We introduce the external knowledge base, which can effectively improve the defects of the model, such as lack of semantics and poor generalization performance under the training of specific datasets. We establish the weighted-loss function based on the weighted self-learning algorithm, which plays a strong role in promoting the optimization of the joint model and the improvement of accuracy.
# Analysis of the Quantization Noise in Discrete Wavelet Transform Filters for 3D Medical Imaging
## 3 Results
### Experiments of the 3D Medical Tomographic Images DWT
Thus, the requirements for the digit capacity of wavelet filter coefficients can be relaxed, depending on the ability to take into account many factors related to the nature of the images obtained as a result of medical tests. Summarizing, the quality of 3D medical tomographic images DWT primarily depends on their bits per color, on average voxels brightness, on the number of wavelet filters coefficients and to a lesser extent on the type of wavelet. Minimum bit-width \(r\) of wavelet filters coefficients for 3D medical tomographic images DWT is defined as follows: determine BPC of images (for example, 8, 12 or 16 BPC); select a quality threshold of image processing (for example, \(PSNR=40\) dB, \(PSNR=60\) dB, \(PSNR=80\) dB or \(PSNR=\infty\)); choose the wavelet with the number of coefficients \(k\); calculate bit-width \(r\) of wavelet filters coefficients by Formulas (5)-(9) depending on the quality threshold of image processing selected.