esa-sceva/satcom-chunk-collection huggingface dataset:
# HTTP Adaptive Streaming Framework with Online Reinforcement Learning
## 3 Framework Design
### MDP Problem Formulation
#### 3.2.3 Reward Space  
Setting the reward in reinforcement learning is a very important task for learning the optimal policy. As mentioned in Section 3.2.1, the optimization goal of the proposed scheme is to create a policy to improve the QoE value. Therefore, in order to define the reward function, QoE should be modeled and reflected. Factors such as video quality, quality variations, and playback interruption affect a client's QoE. The existing method for modeling the client's QoE function linearly combines factors that generally affect the QoE value, as shown in Equation (5)  
\[QoE=\sum_{k=1}^{K}r_{k}+\lambda\sum_{k=1}^{K-1}|r_{k+1}-r_{k}|+\rho T_{n} \tag{5}\]  
\(r_{k}\) denotes a bitrate of the selected quality, \(T_{n}\) denotes a reproduction interruption time, and \(\lambda\) and \(\rho\) denote weights used to control the effects of quality change and playback interruption, respectively.  
In the existing RL-based adaptive streaming techniques, a situation of requesting a low-quality segment occurs in a situation where a higher-quality segment can be requested to maximize the expected cumulative reward. Therefore, if these techniques don't operate in the targeted network environment, performance degradation occurs. To solve this problem, we extend the QoE function by considering the bandwidth usage rate when designing the reward function. The reward function is designed using Equation (6).  
\[r=\mu_{q}q(R_{i})+\mu_{S}T_{i}+\mu_{QS}|q(R_{i})-q(R_{i-1})|+\mu_{G}G \tag{6}\]  
\(q(R_{i})\) means the quality of the \(i\)-th video segment with bitrate \(R_{i}\), \(G\) is the bandwidth usage rate, and \(\mu_{q}\), \(\mu_{S}\), \(\mu_{QS}\), and \(\mu_{G}\) represent the normalization weights for reward used to scale between each QoE metric. In adaptive streaming, if a client requests a higher quality than the measured bandwidth, the probability of playback interruption increases due to the decreased buffer occupancy. If the difference between the bitrate of the requested quality and the measured bandwidth is large, the QoE is degraded because the bandwidth is not fully utilized. Therefore, in order to check whether high quality can be requested without any playback interruption, \(G\) is defined as shown in Equation (7).  
\[G=|\frac{BW-R_{q,i}}{R_{q+1,i}-R_{q,i}}| \tag{7}\]  
\(R_{q,i}\) means the size of the \(i\)-th segment corresponding to the quality level \(q\), and \(BW\) means the measured bandwidth. That is, \(G\) is a value obtained by normalizing the measured bandwidth based on the segment size of the requested quality and the segment size of a higher quality than the requested quality. When the \(G\) approaches 1, it means that the bandwidth utilization rate is small, and a penalty is given accordingly.
## 5 Experimental Validation, Using Power-Cycling Converters
### Validation Setup Presentation  
#### 5.1.1 Opposition Method  
The test bench that will be used for the experimental validation of the design optimization is based on the opposition method. It is made out of two equivalent converters, one feeding the other as illustrated in Figure 25. The first conversion unit is a DC to DC step down converter with energy flowing from the source to the central capacitor. The second one works as a step up converter sending back the energy from the central capacitor to the source. Tow DM filters are also inserted between the two converter stages in order to take the corresponding losses into account. A voltage source is used to supply power losses, to maintain the voltage at the right level. Thanks to this test bench, measuring the power delivered by the DC source and measuring the power transferred to the middle point DC link, it is possible to derive the global efficiency of the two converters and from this to derive the efficiency and the losses of each of them, making the assumption that the losses are equally shared between the two conversion stages. A picture of the test bench is given in Figure 26. For these study cases, the main inductors \(L\) are the ones named over each result cur. Each \(C_{x}\) capacitors are 20 \(\mu\)F ceramic capacitors, and each filtering inductor is taken from the XGL606 series, chosen with Equation (16).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Series** & **MSS1210** & **MSS1260** & **MSS6060** \\ \hline Inductor losses (W) & 0.37 & 0.40 & 0.32 \\ \hline Thermal rising (\({}^{\circ}\)C) & 27.8 to 29.6 & 31.6 to 33.2 & 8.3 to 9.3 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Thermal rising of a 22 \(\mu\)H selected during optimization process of the 50 W converter.  
Figure 25: Schematic of the opposition method to characterize converter losses.
# Facial Expression Recognition Based on Multi-Features Cooperative Deep Convolutional Network
## 1 Introduction
### Motivations and Contributions  
In this paper, we present a new algorithm that performs facial expression recognition with video and achieves satisfactory accuracy on standard datasets. Our work is inspired by an ensemble of regression trees [19] and 3-Dimension Convolutional Neural Networks (3 DCNN) [20].  
Some of the above algorithms have shown a promising performance in facial expression recognition. Notwithstanding, in terms of video with tiny movement, as well as a database with fewer video or pictures, face recognition is still a challenge. Therefore, exploiting the limited dataset to more effectively improve the recognition accuracy is a problem worth exploring.  
This paper proposes using an ensemble of regression trees to annotate corresponding facial features. The network, which is called Multi-features Cooperative Deep Convolutional Network (MC-DCN), captures global and local features by using two small 3 DCNNs. These parallel networks provide a better balance between global and local features. The batch normalization was connected behind the convolutional layer to adapt to the characteristics of a small sample dataset.  
The algorithm uses a cascaded network to address FER problem. The main contributions of this work can be summarized as follow:  
* Firstly, the ensemble of regression trees is applied to achieve facial location. Furthermore, an alternative to the attention mechanism is added. The influence of different facial organs on expressions was analyzed, and the features of facial organs selected. This net can extract the contours of face accurately. The application of facial features allows the network to be fully trained under tiny movements. Meanwhile, the weight analysis of different organs and the entire face can effectively improve the recognition ability of expressions which are not obvious;
* Secondly, a new network was proposed, called Multi-features Cooperative Deep Convolutional Network (MC-DCN), that can dynamically obtain expression features from image sequences. The network combines the face part and the local feature map, and can sense the deformation process and trends of important expression features excellently. Meanwhile, a part called the CNN block is used. The CNN block is improved on the basis of Resnet, which means that the network as a whole has a stronger generalization ability, enhances the compatibility of the algorithm in different scenarios, and improves recognition accuracy.  
The rest of this paper is organized as follows: In Section 2, the source of the datasets is given. Section 3 details the entire framework of the algorithm. Qualitative and quantitative experimental results, obtained from three public datasets, are shown in Section 4.
# Characterization of the Intelligibility of
## 2 Materials and Methods
### Analysis of Performance  
To extract the speech reception threshold (SRT) and slope for each VCV, the average psychometric functions of VCVs in the five languages were estimated by fitting the mean STOI values (pooled, for each language, across VCVs) with a cumulative normal model (sigmoid function) [41]. The SRTs (dB) were estimated as the SNR values where the psychometric functions reached 79.4%, as per the definition of SRT in a 3 AFC test [29]. The slopes (%/dB SNR) of the psychometric functions were estimated by using the following formula, which returns the value of the maximum slope on the cumulative normal function, i.e., at the point of inflection [42]:  
\[slope=\frac{y_{N}-y_{1}}{\sqrt{2\pi}}\frac{1}{\sigma} \tag{1}\]  
where \(y_{1}\) and \(y_{N}\) are the minimum and the maximum value of the psychometric function, respectively, and \(\sigma\) is the standard deviation of the cumulative normal function used to fit data.  
To assess the quality of the estimates of intelligibility provided by the STOI measure, the Pearson product moment correlation coefficient, the Spearman correlation coefficient, and the root mean square error (RMSE) between mean STOI and mean VCV intelligibility from speech recognition tests were computed, as suggested by [43; 44; 45]. The mean and variance of STOI as a function of SNR and the estimated SRT and slope were compared across languages to characterize the intelligibility of VCVs in the five languages and to identify the set of stimuli that fulfilled the three criteria listed in Section 1.2.
# Airbag Protection and Alerting System for Elderly People
###### Abstract  
In the elderly population, falling is an important cause of severe injuries like hip injuries. Injury- or unconsciousness-related immobility implies that the affected are unable to seek assistance. Building fall detection systems is essential since it is usual for senior individuals residing alone to go many hours without being located after a fall, considerably increasing the impact of injuries caused by falls. The primary goal of this paper is to implement an airbag protection and alerting system for elderly people. The system can be installed on a waist belt as an airbag. It is connected wirelessly to the elderly person's mobile, where an Android mobile application is created to receive alert notifications from the system. The system will detect the fall using a gravity sensor that is connected to an Arduino board. If a fall is detected by the gravity sensor, then the system will activate an air valve and inflate the airbag from the air tank. The system will also send a warning notification to the elderly person's mobile application via Bluetooth. Then, the elderly person's GPS location will be determined from their mobile phone and an SMS will be transmitted to a mobile phone belonging to his/her emergency contact of choice. The system is tuned to provide a proper fall detection sensitivity with good accuracy (90%). It has a relatively low cost, very quick response time (only 150 ms delay from the electrical grid), and low power consumption (11.1 Wh).  
fall detection; elderly; alerting; fall protection; global positioning system (GPS); good health and wellbeing
## 3 Results and Discussion
### Adsorption Isotherm Models
#### 3.3.1 Langmuir Model Isotherm for WPET-SiAMPy Composite Nanofibres
The adsorbed single Cu\({}^{2+}\) ions could be exchanging with the H\({}^{+}\) ions which have a lower affinity for the WPET-SiAMPy nanofibres surface. This model indicates that both the active adsorption sites and nanofibres' surface area contributed equally. The Langmuir isotherm model was also investigated for the study of adsorption from theory to practice and the reported adsorption of Cu\({}^{2+}\) metal ions on Si-AMP-M-H as a selective adsorbent, with the adsorption equilibrium fitting the Langmuir isotherm model [48].
## 2 Materials and Methods
### Films Characterization
#### 2.4.6 Thermal Analysis  
Differential scanning calorimetry (DSC) tests were conducted by using a TA DSC Q-2000 instrument (New Castle, DE, USA) under N\({}_{2}\) atmosphere (50 mL min\({}^{-1}\)). Films (3 mg) were introduced in aluminium pans (40 \(\mu\)L) and they were submitted to the following thermal program: \(-80\)\({}^{\circ}\)C to 160 \({}^{\circ}\)C (3 min hold),cooling to \(-80\)\({}^{\circ}\)C (3 min hold) and heating to 160 \({}^{\circ}\)C, all steps at 10 \({}^{\circ}\)C min\({}^{-1}\). Thermal parameters were determined from the second heating scan. Crystallization and melting temperatures (T\({}_{\rm c}\) and T\({}_{\rm m}\)) were determined at peak temperatures of the corresponding transitions, while the crystallization and fusion enthalpies (\(\Delta H_{\rm c}\) and \(\Delta H_{\rm m}\)) were calculated from the area of the corresponding peaks. The glass transition temperature (T\({}_{\rm g}\)) was also determined for all film samples.  
Thermogravimetric analysis (TGA) of ASE and films was performed in a TGA/SDTA 851 Metler Toledo (Schwarzenbach, Switzerland) thermal analyzer. Then, 4 mg of the samples was heated from 30 to 700 \({}^{\circ}\)C at 10 \({}^{\circ}\)C min\({}^{-1}\) under N\({}_{2}\) atmosphere (50 mL min\({}^{-1}\)). The initial degradation temperature (T\({}_{\rm ini}\)), calculated at 5% of weight loss; and temperature of maximum decomposition rate (T\({}_{\rm max}\)) were determined. DSC and TGA analyses were performed in triplicate.
# The Dependence of Ultrasonic Velocity in Ultra-Low Expansion Glass on Temperature
## 2 Principle and Method of Ultrasonic Velocity Measurement  
### Principle of the Ultrasonic Velocity Measurement  
Many instruments are now employed to measure ultrasonic velocity, most of which use the pulse reflection method [19; 20; 21]. This method usually involves time-domain analysis based on the ultrasonic time of flight (TOF). In this analysis, the ultrasonic velocity is obtained by the ratio of the material thickness to the ultrasonic TOF [13; 22]. On this basis, we built a fully integrated high-precision system to measure the ultrasonic velocity in ULE glass using the immersion pulse reflection method.  
Figure 1 depicts the schematic diagram of the immersion pulse reflection method. The time of S\({}_{\text{F}}\) relative to the time base \"0\" point is called \(t_{1}\). The time interval between B\({}_{1}\) and S\({}_{\text{F}}\) is recorded as \(\Delta t_{1}\), and the time interval between B\({}_{2}\) and B\({}_{1}\) is recorded as \(\Delta t_{2}\), which have the following relationship:  
\[\begin{split} t_{1}=2H/c_{\text{L}}^{\text{water}},\\ \Delta t_{1}=\Delta t_{2}=2d/c_{\text{L}}^{\text{sample}}.\end{split} \tag{1}\]The ultrasonic longitudinal wave velocity (\(c_{\rm L}\)) of a sample can be calculated from Equation (1):  
\[c_{\rm L}^{\rm sample}=2d/\Delta t_{1}=2d/\Delta t_{2}. \tag{2}\]  
Using Equation (2), we can determine the \(c_{\rm L}\) of the tested sample by first determining \(\Delta t_{1}\) or \(\Delta t_{2}\) of the ultrasonic waveform and the known thickness \(d\).
# An Interactive Real-Time Locating System Based on Bluetooth Low-Energy Beacon Network +
## 3 System Design
### System Architecture  
As shown in Figure 1, the overall system consists of five roles: Mobile App, Mesh Beacon, Mesh Router, Application Server, and Cloud Database. A number of Mesh Beacons and a Mesh Router form a Bluetooth beacon network. The five roles are elaborated as follows:  
1. Mobile App: a locating application running on mobile users' smartphones. It listens to the beacon messages sent by the Mesh Beacons to figure out the current location of the mobile user. It responds to the received beacon message by broadcasting the mobile user's encrypted presence message.
2. Mesh Beacon: a beacon device that can send, receive, and relay messages in the Bluetooth beacon network. Specifically, it not only broadcasts beacon messages, but also receives presence message from Mobile Apps, and relays the presence message to the Application Server.
3. Mesh Router: a gateway device between the Bluetooth beacon network and the rest of the network. It is responsible for collecting the presence messages from the Mobile Apps within the range of the Bluetooth beacon network. Once the Mesh Router receives a presence message, it will upload the presence state to the Application Server through hypertext transfer protocol (HTTP), and send an acknowledgement message back to the Mobile App in response to receiving the presence message from a specific user.
4. Application Server: a shared HTTP server for the Mesh Routers from several Bluetooth beacon networks. It buffers the presence states of the mobile users, and forwards the presence states to the Cloud Database.
5. Cloud Database: a real-time database in the cloud environment. It is responsible for keeping the history records of all the mobile users' presence states.  
In Figure 1 we can see two Bluetooth beacon networks, in which all communications take place over the transport of the BLE technology. As for the communications outside the Bluetooth beacon networks, one may choose whatever technologies are available such as ethernet or Wi-Fi. In our system, the Mesh Router connects to the Application Server by using its built-in Wi-Fi interface.
## 2 Short-Circuit Calculations
### Short-Circuit Flow Formulation
Although the focus of this paper is on fault current contributions from inverter-interfaced generation, it is important to validate the developed short-circuit flow formulation (defined by constraints (16)-(25)) as PMsP relies on it. Results are provided in \[V^{\phi}_{g\bar{f}}=V^{\phi}_{g0f}-Z^{\phi}_{g\bar{f}}I^{\phi}_{g\bar{f}} \tag{17}\]0 The produced fault currents were compared to results from OpenDSS [63] and provided by the IEEE Power and Energy Society [65]. The IEEE 123-Node System (shown in Figure 5) was used, with faults placed at Buses 13, 67, and 113. The results of the formulation were within 5% of both the results produced by OpenDSS and the results provided by IEEE for this Short-Circuit Test Case. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Fault Node** & **Fault Phasing** & \begin{tabular}{c} **IEEE PES** \\ **Fault Current (A)** \\ \end{tabular} & \begin{tabular}{c} **OpenDSS** \\ **Fault Current (A)** \\ \end{tabular} & \begin{tabular}{c} **IEEE-DSS** \\ **Difference (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **PMsP** \\ **Fault Current (A)** \\ \end{tabular} &
\begin{tabular}{c} **IEEE-PMsP** \\ **Difference (\%)** \\ \end{tabular} \\ \hline
13 & LG & 4400.1 & 4422.0 & +0.50 & 4444.9 & +1.02 \\
13 & LL & 4886.6 & 4736.0 & \(-\)3.08 & 4954.2 & +1.38 \\
13 & 3P & 5435.3 & 5305.0 & \(-\)2.40 & 5517.3 & +1.51 \\
67 & LG & 2339.5 & 2392.0 & +2.24 & 2362.2 & +0.97 \\
67 & LL & 2890.0 & 2879.0 & \(-\)0.38 & 2933.1 & +1.49 \\
67 & 3P & 3236.6 & 3251.0 & +0.44 & 3285.6 & +1.51 \\
113 & LG & 1382.6 & 1390.0 & +0.54 & 1392.2 & +0.69 \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of results for short-circuit flow formulation. Figure 4: Sub-figures (**a**–**c**) present two adjacent three-, two-, and single-phase buses that are connected via a distribution line. Sub-figure (**d**) presents two adjacent three-phase buses that are connected via an ideal transformer [64].
###### Abstract  
As part of a seismic monitoring project in a geothermal field, where the feasibility of re-injection and storage of produced CO\({}_{2}\) is being investigated, a P- and S-wave seismic velocity characterisation study was carried out. The effect of axial (up to 95 MPa) and radial (up to 60 MPa) stress on the seismic velocity was studied in the laboratory for a broad range of dry sedimentary and metamorphic rocks that make up the Kzzidze geothermal system in Turkey. Thin section texture analyses conducted on the main reservoir formations, i.e., marble and calcschist, confirm the importance of the presence of fractures in the reservoir: 2D permeability increases roughly by a factor 10 when fractures are present. Controlled acoustic-assisted unconfined and confined compressive strength experiments revealed the stress-dependence of seismic velocities related to the several rock formations. For each test performed, a sharp increase in velocity was observed at relatively low absolute stress levels, as a result of the closure of microcracks, yielding an increased mineral-to-mineral contact area, thus velocity. A change in radial stress appeared to have a negligible impact on the resulting P-wave velocity, as long as it exceeds atmospheric pressure. The bulk of the rock formations studied showed reducing P-wave velocities as function of increasing temperature due to thermal expansion of the constituting minerals. This effect was most profound for the marble and calcschist samples investigated.  
acoustic measurements; seismic velocity characterisation; geothermal reservoir; CO\({}_{2}\) storage; seismic monitoring
# Ionospheric Error Models for Satellite-Based Navigation--Paving the Road towards LEO-PNT Solutions
## 4 Results
### Ionospheric Delay Results  
After validating the IAMM model using TEC values, the next step was to verify that the model produces valid ionospheric delay predictions that can improve the positioning accuracy. Consequently, we acquired Receiver Independent Exchange format (RINEX) observation files at various random locations and times from fixed GNSS receivers and from GPS-enabled Android smartphones. Then, we calculated the 3D positioning errors (see Equation (9)) using the four methods below:  
* Method 1: No ionospheric correction;
* Method 2: Klobuchar ionospheric correction;
* Method 3: IRI ionospheric correction;
* Method 4: IAMM ionospheric correction. Methods 1-3 are benchmarks for our proposed model (Method 4).