esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background  
### Air-to-Air Refueling--Brief History  
On 17 December 1903, [PERSON] and [PERSON] executed the first controlled flight in a heavier-than-air aircraft under power [15]. Five months prior to the 20 th anniversary of that historic day, another first was accomplished in the world of aviation. On 27 June 1923, over Rockwell Field in San Diego, Lt. [PERSON] and 1 st Lt. [PERSON] flew a DH-4B to serve as a tanker aircraft for Capt. [PERSON] and Lt. [PERSON] in their DH-4B to complete the first recorded instance of air-to-air refueling [1]. Using a fifty-foot rubber hose trailing from the tanker, the receiver aircraft grabbed the hose to complete the refueling connection for the second DH-4B to receive gas. Until the late 1950's, all United States combat aircraft used a method of air-to-air refueling known as \"hose-and-drogue\", also commonly referred to as probe-and-drogue. In 1950, Boeing demonstrated a boom-equipped tanker in response to challenges controlling the hose in bad weather and to support advancing and faster aircraft, which the US Air Force (USAF) pursued for future platform [16]. Today, NATO combat aircraft use a mix of the probe-and-drogue and boom-equipped tanker methods. The USN fixed wing aircraft, such as the F/A-18 E/F or F-35 B/C, rely on the probe-and-drogue method to receive fuel from drogue configured aircraft, including the KC-130 or Air Refueling Store (ARS) equipped F/A-18E/F. The USAF fixed wing aircraft, including the F-15E and F-22A, rely on boom-equipped tankers, such as the KC-10, KC-135, and the new KC-46, to complete air-to-air refueling [6].  
The primary difference between probe-and-drogue refueling and boom-equipped refueling derives from the aircraft completing the engagement. During a probe-and-drogue configured engagement, the pilot in the receiving aircraft must guide the probe tip into the coupler of the drogue to complete the engagement (Figure 1).  
For boom-equipped refueling aircraft, such as the KC-10, KC-135, and KC-46, the receiving aircraft maintains formation based on a relative position to the tanking aircraft (Figure 2). Inside the aircraft providing fuel, a crewmember maneuvers the boom towards a receiver port on the receiver aircraft to complete the engagement.  
Figure 1: Example of probe and drogue refueling.  
Figure 2: Example of boom-equipped refueling.
# Secure Automatic Identification System (SecAIS): Proof-of-Concept Implementation
## 5 How the SecAIS Works  
### SecAIS Source Authentication  
In Authenticated-SecAIS (mode 2), each AIS device will be able to sign the broadcasted AIS messages with its mIBC private key (SSK); the receivers will be able to authenticate the signed AIS data by using the MMSI of the broadcasting vessel. In brief, the above scenario works as follows (see Figure 2):  
1. The vessel that broadcasts the AIS message (hereafter \"the Transmitter\"), uses the SecAIS-App to sign its positional ID1 AIS message before it is transmitted;
2. Any AIS receiver (hereafter \"the Receiver\") uses its SecAIS-App to validate the signature and, thus, the authenticity of the received positional ID1 AIS message.
# Research on Advancing Radio Wave Source Localization Technology Through UAV Path Optimization
## 4 Results  
### Circular Trajectory Placement with a Radius of 100 m  
Here, we analyze the localization results for different types of radio signal information with a radius of 100 m. The primary reason for setting the radius to 100 m is that it comprehensively covers the Ookayama campus. Additionally, as will be discussed later in Section 4.2, the results of localization with different radii indicate that 100 m is a reasonable choice for comparing different types of radio signal information. The results show that when the radius is set to 100 m, the localization accuracy of RSSI, AOA, and HYBRID is relatively unaffected by the radius. The following Sections 4.1.1-4.1.3 discuss the localization results and the trends in estimation errors for each type of radio signal information.
# Optimizing Roller Design to Improve Web Strain Uniformity in Roll-to-Roll Process
## 2 Strain of Web in Roll-to-Roll Process
### Verification of FE Model for Analyzing Web Strain Deviation  
Before using the FE model to design the roller, we verified the model. Figure 4 shows a schematic of the arrangement of the rollers and web of the experimental equipment. In general, the web is transferred by an unwinding roller and a rewinding roller. In a single span, one side uses a nip to simulate the fixed end, and the other side uses a dancer roller to simulate the load end. The tension acting on the web was measured with load cells (TS-100, Bongshin Loadcell Inc., Osan-si, Korea) at both ends of the dancer roll. The strain measurement was calculated from the relative displacement of reference marks on the web when tension was applied to the web, as shown in Figure 5. The mark displacement was measured with a vision camera (MV-BX30A, Crevis Inc., Yongin-si, Korea, pixel size = 4.65 \(\upmu\)m).  
To measure the relative displacement of the web, 18 marks were used--nine marks near the fixed end and nine marks near the load end. Each set of nine marks is aligned along the CD, parallel to each other. The marks were fabricated by using a CO\({}_{2}\) laser on the web. The marks were approximately \(1\times 1\) mm\({}^{2}\) and cross-shaped.  
Figure 6 shows the relative displacement from the marks at the fixed end to the marks at the load end; the experimental results are similar to the results of the FEA. However, even if the transfer of the web was stopped during the experiment, displacement due to the inertia of the roller occurred; thus, it was impossible to stop it at the same position every time. Additionally, because the dancer roller used a hydraulic drive to add tension, it was difficult to compare quantitatively because there is a tension error for each test case. The experimental results were not symmetrical about the center because the position of the reference mark was not symmetrical.  
Figure 4: Schematic of equipment for web strain measurement.  
Figure 3: MD strain distribution along the printing line with respect to the web width.  
Based on the results in Figure 6, we confirmed that the strain deviation occurred in the MD under uniform tension conditions; the strain in the MD was larger near the center of the CD and decreased toward both ends. Since non-idealities exist in reality, there are some errors; however, the FEA and the experiment largely agree. Thus, we used the FE model to design the compensation roller for minimizing the strain deviation in the MD.
# Comparative Study for Multi-Speaker Mongolian TTS with a New Corpus
## 4 Speech Synthesis Experiments
### Experimental Setup
#### 4.1.2 Experimental Steps of TTS Based on the VITS Model  
VITS is a generation-based, parallel, fully end-to-end TTS system. We built a fully end-to-end TTS model using code publicly available from the VITS authors. Since the model uses conditional VAE as the generative model and uses normalized flow to enhance the expressiveness of the prior and posterior distributions, it can produce speech that is closer to the real person. The model can synthesize the waveform of natural speech directly from the text. Therefore, there is no need to obtain waveforms from the generated Mel spectrum. The model links two modules, acoustic and vocoder, through VAE, learning hidden variables to achieve a full end-to-end effect. VITS has the features of easy training, fast synthesis, good stability of long text, and rich speech diversity. This is the main reason why we chose VITS. As shown in Figure 5, In the text encoder, the embedding dimension of the text was 192, the number of heads in the attention module was 2, the kernel size was 3, and the dropout was 0.1. To implement a multi-speaker TTS model, the speaker encoder module was added to encode the speaker information, the number of speakers was 3, and the speaker embedding was 256 dimensions. The decoder was the same as HiFi-GAN V1. In the generator, the kernel size was 7 and the upsampling ratio was (8, 8, 2, 2). In the discriminator, the output channel was 1, the kernel size was 5, and the list of discriminators for the cyclic scale was (2, 3, 5, 7, 11).  
Figure 4: The structure of the HiFi-GAN model.  
Since the Mongolian text represented by Latin sequence does not require text processing, the data of each speaker in the dataset was manually divided into a training set, a test set, and a validation set in the ratio of 93:5:2. The entire VITS model was trained on two Tesla P100 GPUs.
# A Service-Efficient Proxy Mobile IPv6 Extension for IoT Domain
## 5 Results and Analysis  
Using the aforementioned criteria for comparing the proposed and existing PMIPv6 extensions, the schemes are compared and results are analyzed for their effective performance efficiency in the context of the IoT domain. Performance efficiency is obtained in the context of the buffering of each scheme, the handover latency, and the signaling required for initiating the handover process. Furthermore, along with the above performance criteria, other aspects during mobility of MN are also considered to have direct or indirect effects on the performance. These include the variations in the velocity of MNs that directly affect the signaling cost. In addition, the number of MNs has a direct relationship with the signaling required. Furthermore, RSS error probability has a severe effect on signaling efficiency due to early handoff. Early handoff also increases the additional buffering and the handover latency. The results are analyzed in the presence of RSS error, velocity, and number of MNs. Detail of such analysis is provided as follows:
# Energy Consumption Framework and Analysis of Post-Quantum Key-Generation on Embedded Devices
## 5 Evaluation
### Energy Results--Key Generation  
The experiments were conducted on a Raspberry Pi 5 platform under the previously outlined controlled conditions, with fixed clock speeds and 100% fan to establish a consistent baseline for the energy consumption measurements across each of the selected algorithms.  
The bar chart in Figure 4 visualises the energy efficiency of the various key generation algorithms across the different NIST Security Levels. The horizontal axis represents the NIST Security Level, ranging from 1 to 5. There may be one or more algorithm in each level. The vertical axis displays the energy efficiency in Joules per 1000 key generations, using a logarithmic scale to accommodate the wide range of values, particularly relating to RSA. Each bar on the chart corresponds to a specific key generation algorithm. The colour of each bar indicates the category of that algorithm: blue represents the recently standardised post-quantum algorithms, green: elliptic curve cryptography, and orange: classic RSA technology. The name of each algorithm and its efficiency is printed above the corresponding bar for identification and comparative purposes.
# Implementation of Context Aware e-Health Environments Based on Social Sensor Networks
## 2 Related Work
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**System** & **Transmission System** & **Architecture** & **Application Scenario** & **Detected Variables** & **Reference** \\ \hline LiveNET & PDA Connectivity & Centralized & Detection of Epilepsy Seizure, Parkinson symptom detection, Soldier Health Monitoring & 3D Accelerometer, ECG, EMG, galvanic skin conductance & [25] \\ \hline AMON & Wrist bracelet, GSM & Operator Based & Estimation of Patient Health Conditions & Blood Pressure, Blood Saturation, Skin Temperature, ECG & [26] \\ \hline LifeGuard & Bluetooth to a base station & Centralized & Multiparameter Wearable Monitoring System & ECG, respiration rate, heart rate, oxygen saturation, body temperature, blood pressure, body movement. & [28] \\ \hline Real Time Wireless Physiological Monitoring System & Low Power Cordless Phone To Base Station & Centralized & System Aid in Nursing Centers and Hospitals & Blood Pressure, Heart Rate and Temperature & [27] \\ \hline Brain Injury Monitoring System & Bluetooth to Home PC & Centralized & Monitoring of Brain Injured Infants & Blood Saturation, Heart Rate, Respiration, Body Movement. & [29] \\ \hline MyHeart & Communication to Data Logger & Off-line & Wearable System for Heart Disease Monitoring, Sensors & ECG, Activity Sensor. & [30] \\ \hline Wearable Health Care & Bluetooth/GPRS & Centralized or & Application to clinical patients during rehabilitation, elderly people, patients with chronic diseases & ECG, EMG, thoracic and abdominal respiration rate, body position, movement & [31] \\ \hline MagIC & Bluetooth & Centralized & Woven textile sensors in a washable vest & ECG, respiration rate, motion level. & [32] \\ \hline Medical Remote Monitoring of Clothes (MERMOTH) & RF Link to PDA & Data Logger & Wearable and Stretchable Sensing & ECG, respiratory inductance plethysmography, skin temperature, activity. & [33] \\ \hline NASISTIC & Bluetooth/2G-4G Connection & Locally Distributed/Centralized & Social Sensor Node Deployments & Combination of Biophysical signals (ECG) with user habits. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Health monitoring system comparison.
# Deep Interest Context Network for Click-Through Rate
## 4 Experiments and Analysis
### Results and Analysis
#### 4.5.1 AUC and Relalmpr  
This subsection compares the AUC and Relalmpr indicators of the six models, including the DICN model, and demonstrates the superiority of the DICN model through the indicator data values. The results are shown in Table 2.  
From Table 2, we can see that the comprehensive performance of the DICN model proposed in this paper is the best in the CTR prediction scenario. Under the Taobao user historical behavior dataset, the AUC of DICN is 1.22% higher than the original model DIN; under the Amazon dataset, the AUC of DICN is 0.12% higher than the original model DIN. Moreover, compared with the DIN model, the Relalmpr index of DICN is 6.2% and 0.79% in the two different datasets, which proves the superiority of DICN compared to DIN. The effective performance of DICN is attributed to the addition of a new local activation unit. On the basis of DIN, it again focuses on locally relevant user interests through soft searches for user historical behaviors related to contextual features, and enhances the adaptive change in user representation. This improves the expressive ability of the model. In addition, for a more visual and clearer representation of the experimental results, we constructed a bar chart of the data in the Tab 2 section. This is shown in Figure 3. From Figure 3a,b, we can clearly visualize that the AUC of DICN is higher than that of DIN and other classical models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Taobao**} & \multicolumn{2}{c}{**Amazon**} \\  & **AUC** & **Relalmpr** & **AUC** & **Relalmpr** \\ \hline Base Model & 0.5326 & \(-87.97\%\) & 0.6133 & \(-25.61\%\) \\ PNN & 0.5433 & \(-84.02\%\) & 0.5166 & \(-89.10\%\) \\ DeepFM & 0.5465 & \(-82.84\%\) & 0.5232 & \(-84.77\%\) \\ xDeepFM & 0.5542 & \(-80\%\) & 0.5252 & \(-83.44\%\) \\ DIN & 0.7710 & 0.00\% & 0.6523 & 0.00\% \\ DICN & 0.7832 & 4.5\% & 0.6535 & 0.79\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: AUC and Relalmpr predicted by CTR.
###### Abstract  
Wireless technologies that use radio frequency (RF) waveforms are common in wireless communication systems, such as the mobile communication, satellite system, and Internet of Things (IoT) systems. It is more advantageous than wired communication because of the ease of installation. However, it can negatively impact human health if high frequencies are used to transmit data. Therefore, researchers are exploring the potential of optical wireless communication as an alternative, which uses the visible light bandwidth instead of RF waveforms. Three possibilities are being investigated: visible light communication, light fidelity, and optical camera communication. In this paper, we propose a multiple-input multiple-output modulation scheme using a light-emitting diode (LED) array, which is applicable to the IoT system, based on on-off keying modulation in the time domain. This scheme is compatible with the two popular types of camera in the market, rolling shutter cameras and global shutter cameras, as well as the closed-circuit television camera, which is used in factories, buildings, etc. Despite the small size of the LED array, implementing this scheme with 10 links in different positions at a communication distance of 20 m is possible for efficient performance (low error rate) by controlling the exposure time, shutter speed, focal length, channel coding and applying the matched filter.  
Keywords:internet of things; multiple-input multiple-output; on-off keying; optical camera communication +
Footnote †: journal: _Sensors_
## 2 STAR Algorithms to Convert Carrier Phase to Excess Phase and Bending Angle Profiles
### Preparation of COSMIC-2 L0 and L1a Data  
While COSMIC-1 POD has a 50 Hz sampling rate for reference for single differencing, the COSMIC-2 POD antenna only has a 1 Hz sampling rate. COSMIC-2 occultation (OCC) antenna also records the 100 Hz sampling rate, which is different from COSMIC-1. The high-rate OCC antenna observations are in the opnGns files (see Figure 2). The COSMIC-2 RO events can be easily separated based on the discontinuity in the measurements (say 30 s or longer) and tracking GNSS PRNs. Each GNSS satellite has an orbital period of about 12 h; the LEO satellite's orbital period is usually less than 2 h. One OCC antenna can track only one GNSS satellite in its view zenith for a time window. The opnGns dataset includes the receiver time (as in GPS seconds since 1 January 1980, 00:00:00 UTC), the L1 and L2 carrier phase, the L1 phase model, the SNR, low rate (1 Hz) pseudo-range and range model, and tracking status (open loop or close loop). Like COSMIC-1, the COSMIC-2 phase model is provided only for L1. However, the phase model is provided for the whole profile in the COSMIC-2 data set. The COSMIC-2 attitude file follows the data format as CHAMP mission, where we can directly feed them into the Bernese GNSS software [40] for POD.  
The GNSS ephemeris and clock information are needed for POD and excess phase calculation. We used the Center for Orbit Determination in Europe (CODE) GNSS final orbit products, which provide a 15 min resolution of GNSS orbits. Like the International GNSS Service (IGS), CODE can provide final, rapid, and ultra-rapid GNSS orbit products  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Processing Step** & **Input Data** & **Main Output Data** & **Processing Methods/Libraries** \\ \hline COSMIC-2 receiver clock bias removal for excess phase calculation & COSMIC-2 carrier phase model(opnGns), 1 Hz POD observation (RINEX); Corrected receiver time tag, geometric range  
but with a different latency. Since we processed the COSMIC-2 datasets in a post-processed mode, we used the most accurate final CODE orbit solution, which usually has two weeks of latency but generally within 2 cm accuracy [41]. We also downloaded the 30 s GNSS CLOCK and the earth orientation information from the CODE website for COSMIC-2 POD and the excess phase calculation.
## 3 Numerical Modeling
### Gas-Phase: Friction and Heat Transfer Terms  
Although the boundary layer along the tube wall is not completely modeled in the formulation of the gasdynamic equations, its effects are modeled by the addition of a wall shear stress in the momentum equation and heat transfer in the energy equation.  
The wall-skin stress on the gas is given by: \(\tau=-0.5f\rho\tau u|u|D\). The skin friction coefficient for the pipe flow is then computed as [12]:  
\[\left\{\begin{array}{rcl}f^{\prime}&=&0.049\Big{(}\text{Re}^{\prime}_{D} \Big{)}^{-0.2}&&\text{Re}^{\prime}_{D}\geq 5507\\ f^{\prime}&=&0.00875&&5507\geq\text{Re}^{\prime}_{D}\geq 1828\\ f^{\prime}&=&\frac{16}{\text{Re}^{\prime}_{D}}&&1828\geq\text{Re}^{\prime}_{D} \end{array}\right. \tag{5}\]  
\(f\ =\ f^{\prime}\frac{T}{T^{\prime}}\) is evaluated at the reference temperature as follows: \(T^{\prime}\ =\ 0.9T+0.03M^{2}T+0.46T_{w}\), where \(T\) is the cell temperature and \(T_{w}\) is the specified wall temperature, \(M\) is the Mach number. The Reynolds number is calculated as: \(\text{Re}^{\prime}_{D}\ =\ \frac{\rho^{\prime}uD}{\mu^{\prime}}\), where \(u\) is the cell velocity and \(D\) is the tube diameter of the cell. \(\rho^{\prime}\) and \(\mu^{\prime}\) are evaluated at the reference conditions: \(\rho^{\prime}\ =\ \rho\frac{T}{T^{\prime}}\), \(\mu^{\prime}\ =\ \mu^{\prime}(T^{\prime},\rho^{\prime})\).  
The heat transfer from the gas to the walls is given by: \(q~{}=~{}h\pi D(T_{w}-T^{\prime})\), where the heat transfer coefficient is \(h=\rho C_{p}|u|S_{t}\) and the Stanton number is given by the modified Reynolds analogy for pipe flows [27]:  
\[S_{t}~{}=~{}\frac{f}{8}\mathrm{Pr}^{-\frac{3}{3}} \tag{6}\]  
In the treatment of the viscous gas mixture, [PERSON]'s mixture rule is applied with Maxwell's Power Law to calculate the dynamic viscosity of the gas [28]:  
\[\mu~{}=~{}\mu_{0}\Bigg{(}\frac{T}{T_{ref}}\Bigg{)}^{n}~{}~{}n~{}=~{}0.76~{}( \mathrm{air})~{}\mathrm{or}~{}0.83~{}(\mathrm{detonation~{}gas}) \tag{7}\]  
According to Ref. [27], the Prandtl number is given approximately as:  
\[\mathrm{Pr}~{}=~{}\frac{20\gamma}{39\gamma-15} \tag{8}\]  
where \(\gamma\) is the ratio of the specific heat for gases.
## 2 Materials and Methods
### Specimens Preparation  
Based on the results of our previous study, the geopolymer composites, with mix proportions, were established [25]. An alkaline activator was prepared by mixing 10 M NaOH with liquid Na\({}_{2}\)SiO\({}_{3}\) in a weight ratio (10 M NaOH/Na\({}_{2}\)SiO\({}_{3}\)) of 2. The fly ash to sand ratio was 1:3. Different concentrations (0%, 3%, 5%, 7%) of PVA fibers were mixed with fly ash by using a Hobart mixer for 3 min. Natural sand was then added to the fly ash-PVA fiber mixture for further 3 min. Silica nanoparticles (6% of total fly ash) were mixed to the alkaline activator through continuous mechanical stirring and kept at (30 \(\pm\) 2) \({}^{\circ}\)C for 30 min. Then the alkali activator solution was added into the mixture for 5 min, while the mixture was running. The fly ash and alkali activator ratio was affixed at 0.75. Different trial mixes of the fly ash-based geopolymer mortar activated by the alkali activator solution exhibited that this ratio delivered near-optimum workability and strength. After casting the specimens, compaction was made on a vibrating table for 30 s. The specimens were cured at ambient temperature for 56 days. The samples were labelled as GP0, GP3, GP5, and GP7, depending upon the fiber concentrations (0%, 3%, 5%, and 7%). The sample GP0 is taken as reference.