esa-sceva/satcom-chunk-collection huggingface dataset:
# A Post-Processing Multipath/NLoS Bias Estimation Method Based on DBSCAN
## 5 Conclusions
The correction for the Shapiro effect can be compensated by  
\[\delta\mathbf{t}_{\text{stc}}^{s,\text{rel}}=\frac{2\mu}{c^{3}}\ln\left(\frac{\| \mathbf{r}^{s}\|+\|\mathbf{r}_{r}\|+\rho_{r}^{s}}{\|\mathbf{r}^{s}\|+\|\mathbf{r}_{r}\|-\rho_{ r}^{s}}\right) \tag{1}\]  
where  
* \(\mu\) is the gravitational constant of Earth. Appendix B Relativistic Clock Correction for \(\delta\mathbf{t}_{\text{clk}}^{s,\text{rel}}(\mathbf{t})\)  
The relativity affects the satellite clock through both the satellite motion and the unsteady gravitation field. Although the oscillator frequency of the satellite clock has been intentionally offset to compensate for the relativistic effect, the elliptical satellite orbits cause deviations to the set offset. The compensation due to the orbit eccentricity can be calculated from  
\[\delta\mathbf{t}_{\text{clk}}^{s,\text{rel}}=-\frac{2}{c^{2}}\sqrt{a\mu}e\sin E \tag{2}\]  
* \(a\) is the orbit semimajor axis;
* \(e\) is the orbit eccentricity;
* \(E\) is the eccentric anomaly of the satellite.
## 2 Materials and Methods
### Cerebral hNIRS Setup and Measurements  
Hyperspectral NIRS (hNIRS) enables the direct quantification of the absolute levels of HbO\({}_{2}\) and HHb in the tissue as well as the difference between the oxidized and reduced cytochrome C oxidase, \(\Delta\)Cyt-ox [19; 20]. To carry out the measurements of the cerebral variables, a custom-designed hNIRS system [19; 20] was utilized. This involved positioning optodes on the dura mater and maintaining a spacing of 20 mm between them. This was achieved by drilling holes in the skull above the brain. With a differential path length factor of 5 and an interoptode distance of 20 mm, the average optical path length was estimated to be around 100 mm, with an approximate penetration depth of 16 mm [21; 22; 23]. This optical path length was chosen to mitigate the interference from the \(\sim\)1 mm thick dura. The hNIRS system included a highly sensitive spectrometer (AvaSpec, Avantes, Lafayette, CO, USA) which recorded spectral data in the 700-1000 nm range. The hNIRS diffusion model analyzed the data to determine changes in cerebral HbO\({}_{2}\), HHb, and Cyt-ox [17]. The dynamic fluctuations in chromophore concentrations were deciphered using a multi-step data-fitting algorithm which was grounded in the analytical solution to the diffusion equation for a semi-infinite medium and which integrated the extrapolated boundary condition. This algorithm yielded values for the tissue concentrations of HbO\({}_{2}\), HHb, and \(\Delta\)Cyt-ox [19; 24]. The total hemoglobin and tissue oxygen saturation (tSO\({}_{2}\)) was computed using the following equation:  
\[\text{Total [Hb]}=\text{[HbO}_{2}]+\text{[HHb]}\quad\quad\text{tSO}_{2}=\text{[HbO}_{ 2}]/\text{(Total [Hb])}\times 100\%\]  
In addition to hNIRS, the cerebral blood flow velocity index (CBFV) was measured using a laser Doppler flowmeter (LDF) (Periflux; Perimed Inc., Ardmore, PA, USA), for which purpose a probe was placed on the dura mater via a hole drilled in the pig's skull. Mean aortic pressure and right atrial pressure were also monitored during the experiments using catheters equipped with micro-manometer tips.
# Influence of the Slope and Gate Offset on Movement Variability and Performance in Slalom Skiing
## 2 Materials and Methods
### Data Analysis  
#### 2.3.1 Skiers' Performance  
The skiers' performance was evaluated with the descent time of each run defined as the time elapsed between the first and tenth gates and was calculated through a validated Magnet-Based Timing System (M-BTS) [50].  
Figure 2: On-hill measurements. Flat-turned course (pink): 12\({}^{\circ}\) slope, 4 m gate offset (GO); flat-straighter course (blue): 12\({}^{\circ}\) slope, 3.25 m GO; steep-turned course (yellow): 21\({}^{\circ}\) slope, 4 m GO; steep-straighter course (green): 21\({}^{\circ}\) slope, 3.25 m GO.
## 3 Results and Discussion
### Description of the Input Data  
Twenty-one frequent PV faults (\(Fault_{1}\), \(Fault_{2}\)..., \(Fault_{21}\)) are treated in this current work.  
As shown in Table 1, we used five different types of faults to introduce various scenarios into the \(PV_{1}\) and \(PV_{2}\) systems in this work; for example, PV1's simple faults include four possible fault scenarios: \(Fault_{1}\) (line-to-line fault) is injected betwirat two distinct points; a line-to-ground fault (\(Fault_{2}\)) is considered in String1 (\(Str_{1}\)) positioned betwirat one point and the ground; \(Fault_{3}\) (connectivity fault) is injected in the first string between two modules; \(Fault_{4}\) impacts the bay-pass diodes by injecting a variation in resistance, the diverse positions of the aforementioned failures are shown in Figure 8.  
The second PV array receives the same simple fault injections. Then, numerous defects that present multiple faults are introduced into one PV array (\(PV_{1}\) or \(PV_{2}\)). In addition, we simultaneously injected mixed faults, which reflect numerous faults in both PV arrays.  
Figure 7: Schematic of a parallel system on a direct bus.  
The used simulated variables, which are gathered in order to assess FDD performance, are presented in [48].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of Fault** & **Fault Label** & **Fault Description** \\ \hline Simple faults in \(PV_{1}\) & \(Fault_{1}\) & Line-to-Line fault (LL1) \\  & \(Fault_{2}\) & Line-to-Ground fault (LG1) \\  & \(Fault_{3}\) & Connectivity fault (Cn1) \\  & \(Fault_{4}\) & Bypass fault (BP1) \\ \hline Simple faults in \(PV_{2}\) & \(Fault_{5}\) & Line-to-Line fault (LL2) \\  & \(Fault_{6}\) & Line-to-Ground fault (LG2) \\  & \(Fault_{7}\) & Connectivity fault (Cn2) \\  & \(Fault_{8}\) & Bypass fault (BP2) \\ \hline Multiple faults in \(PV_{1}\) & \(Fault_{13}\) & LL1 + LG1 + Cn1 \\ \hline Multiple faults in \(PV_{2}\) & \(Fault_{14}\) & LL2 + LG2 + Cn2 \\ \hline Mixed faults & \(Fault_{17}\) & LL1 + Cn2 \\  & \(Fault_{19}\) & LL1 + LG2 \\  & \(Fault_{20}\) & LG1 + LL2 \\  & \(Fault_{21}\) & Cn1 + BP2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed description of the diverse injected labeled faults.  
Figure 8: PV panel association structure.
# Multi-Label Emotion Recognition of Korean Speech Data Using Deep Fusion Models
## 3 Data Preparation  
### Korean Speech Emotional Database  
To obtain a reliable database, this study used the Korean speech emotional database provided by the AI-Hub ([[https://www.aihub.or.kr](https://www.aihub.or.kr)]([https://www.aihub.or.kr](https://www.aihub.or.kr)) (accessed on 20 November 2023)) of the National Information Society Agency (NIA). The AI-Hub speech emotional database consists of speech recordings covering people aged between 20 and 50 without limitations on age group or gender. Furthermore, it is a multi-label emotion database that includes utterances from various contexts. Table 2 provides an example of the dataset, where each speech file reflects the judgments of five experts who listened to the corresponding utterance and recognized the emotions. Since each speech file is labeled with between one and five emotions, the authors determined that the AI-Hub database is suitable for performing multi-label classification. Therefore, this study uses the database labeled with five emotions ('Happiness', 'Sadness', 'Anger', 'Neutral', 'Disgust') to perform the analysis.  
This study employs a hard labeling approach to perform SER exclusively on specific emotions. Specifically, only emotions unanimously identified by at least two experts within each audio file are used. The hard labeling approach employed in SER utilizes only emotions judged by the majority of experts to eliminate ambiguous emotions [17]. Some studies have demonstrated high SER performance by applying a hard-labeling approach [15; 29]. Figure 1 illustrates the process of applying hard labeling to each speech file. Through hard labeling, each audio file is labeled with one or two emotions based on the agreement of at least two out of five judges. In this process, speech files labeled with 'Happiness and Disgust' and 'Happiness and Anger' are excluded because of the small amount of data and the fact that these combinations are not logically appropriate for expressing emotions simultaneously. Consequently, experiments are conducted using 40,645 speech data with hard labeling. Table 3 lists the number of data for each emotion label in the dataset. As shown in Table 3, a large variation is identified in the number of data for each emotion label.
# Design and Analysis of a Low Latency Deterministic Network MAC for Wireless Sensor Networks
## 2 Related Works
In addition, one prediction model is proposed to predict the optimal number of required retransmission slots based on the historical data of the previous transmissions, and one analytical model is proposed to study the performance of different network parameters such as reliability, throughput, energy consumption and delay.
## 1 Introduction
However, the above models could only compute the average drainage radius for the gas well with a single layer [2; 3; 4; 5; 6; 7; 8]. Although some studies [9; 10; 11; 12; 13] have involved multi-layer commingled gas production wells, a clear calculation formula of the drainage radius of these wells was not proposed. In addition, although the well test data required for calculation of the drainage radius can be obtained by using the pressure drawdown test and pressure buildup test, its accuracy from pressure drawdown test is easily affected by many factors, while the long shut-in time required for pressure buildup test can seriously impact the production of gas field and the economic feasibility of gas reservoir development [14; 15; 16; 17]. Therefore, it remains unknown how to calculate the drainage radius of each layer of a multi-layer commingled gas production well. In order to address these issues, this study proposes a new, convenient and rapid calculation method of the drainage radius in a closed constant-volume gas reservoir. For a single-layer gas well, this study utilizes a modified material balance method to derive a calculation model of the drainage radius in a closed constant-volume gas reservoir. This obtained model provides a quick and efficient calculation method needing only two flow pressure tests, and resolves the drawbacks of previous calculation methods requiring extensive well test data and the adverse effects of a long shut-in period during pressure buildup test on gas production. For a multi-layer commingled gas production well, this paper establishes a calculation model and solution method of the drainage radius in a closed constant-volume gas reservoir by coupling production capacity equation with the material balance principle. By using only two gas production profile tests, this proposed calculation model can address the current challenge of obtaining the drainage radius of each layer of a multi-layer commingled gas production well. The main purpose of this study is to calculate the drainage radius of single-layer/multi-layer commingled gas production wells in a closed constant-volume gas reservoir, providing a theoretical basis for determining reasonable well spacing.
###### Abstract  
Dynamic Adaptive Streaming over HTTP (DASH) offers adaptive and dynamic multimedia streaming solutions to heterogeneous end systems. However, it still faces many challenges in determining an appropriate rate adaptation technique to provide the best quality of experience (QoE) to the end systems. Most of the suggested approaches rely on servers or client-side heuristics to improve multimedia streaming QoE. Moreover, using evolving technologies such as Software Defined Networking (SDN) that provide a network overview, combined with Multipath Transmission Control Protocol (MPTCP), can enhance the QoE of streaming multimedia media based on scalable video coding (SVC). Therefore, we enhance our previous work and propose a Dynamic Multi Path Finder (DMPF) scheduler that determines optimal techniques to enhance QoE. DMPF scheduler is a part of the DMPF Scheduler Module (DSM) which runs as an application over the SDN controller. The DMPF scheduler accommodates maximum client requests while providing the basic representation of the media requested. We evaluate our implementation on real network topology and explore how SVC layers should be transferred over network topology. We also test the scheduler for network bandwidth usage. Through extensive simulations, we show clear trade-offs between the number of accommodated requests and the quality of the streaming. We conclude that it is better to schedule the layers of a request into the same path as much as possible than into multiple paths. Furthermore, these result would help service providers optimize their services.  
algorithm design and analysis; Dynamic Adaptive Streaming over HTTP; MPEG-DASH standard; scalable video coding (SVC) video streaming +
Footnote †: journal: Applied Soft Matter
## 4 Discussion  
Regarding the implementation of the proposed method and the experimental process, several aspects need to be further explained and discussed.  
1. Measurement accuracy. The STBF-accelerated spatiotemporal correlation matching strategy proposed in this paper differs from the test results in the literature [23]. First, the literature [23] did not consider the human face as the analysis object and did not use a coarse-to-fine matching strategy, and its calculation speed was not as good as the method in this paper. Furthermore, based on the comparison of the calculation results, it was found that the combination trend between the selected matching window size and the number of speckle patterns was different. In [23], when the number of projected speckle patterns exceeded three frames, the change in the size of the matching window had only a small effect on the reconstruction accuracy. The test results in this study show that, as the number of projection patterns increases, the 3D reconstruction accuracy continues to improve. When the number of patterns is greater than six, the trend of accuracy improvement gradually slows down. This phenomenon may be caused by differences in the measurement object.
2. Balance of measurement accuracy and time cost. A 3D face acquisition device with a rotating speckle projector is more suitable for use in scenes where the accuracy requirements are strict, and there is no clear limitation on the acquisition time. To obtain better reconstruction accuracy, more stereo image pairs were selected to participate in the spatiotemporal stereo-matching process, which is more suitable for scenes where objects remain stationary. According to the research results of this study, six sets of image pairs met the requirements for high-precision modelling. When reconstructing fast-moving objects, the authors attempted to reduce the number of stereo image pairs and employed 3D reconstruction equipment with fixed speckle projectors for 3D face image acquisition.
3. Real-time acquisition, reconstruction and display. Using the single-shot speckle structure [17; 18; 19], the acquisition time of the stereo image pair was short, but the real-time reconstruction effect was usually not achieved. Although the literature [27] achieved a real-time reconstruction frequency of 30 fps, the accuracy was only 0.55 mm, which is far from the accuracy of the proposed method. Aiming at 3D face acquisition equipment using a fixed speckle projector, the method in this study implements real-time image acquisition and real-time 3D face reconstruction. However, during the 3D data display process, the point-cloud structure can be used to display 3D data in real time through the openGL interface. If the triangular facet structure of texture mapping is adopted, it is limited by openGL's utilisation of the low-level image cache and does not display 3D data in real time. In our next step, the authors will study the openGL parallel display strategy, which has achieved a more realistic display effect of 3D data.
# Reasonable Support Parameters for Loess Tunnel Under Different Water Contents: A Case Study
## 3 On-Site Test of Mechanical Characteristics  
### Test Contents  
According to the water content test for the surrounding rock of Fengshouling Tunnel, the mechanical behavior tests of the support structure under general water content were conducted within the range of mileage pile ZK90+041-ZK90+071, where the deformation monitoring sections are spaced every 15 m. Among these, the deformation measurement points of the surrounding rock were installed after the completion of the steel frame construction, and the deformation measurement of the surrounding rock began after the completion of the shotcrete construction. The entire line was excavated by the method of three-bench seven step, as shown in Figure 5.  
The test section, ZK90+056, was selected for the monitoring of mechanical characteristics of supporting structures. According to the research findings of [PERSON] [42], total station free standing to edge measurement (RDM) was utilized as the method for measuring rock deformation, which included crown settlement and horizontal convergence. Highly sensitive and stable steel string sensors were utilized to measure the surrounding rock pressure, as well as the stresses in other supporting structures. The deformation measurement points and stress monitoring measurement points are presented in Figure 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{3}{c}{**Water Content**} \\ \cline{2-4}  & **16.1\% (Natural)** & **21.5\% (Median)** & **26.9\% (Saturated)** \\ \hline \(c/\)kPa & 14.3 & 8.1 & 4.6 \\ \(\varphi/^{\circ}\) & 22.1 & 21.1 & 20.2 \\ \(E_{0}/\)MPa & 24.7 & 20.2 & 11.2 \\ \(\gamma/\)kN\(\cdot\)m\({}^{-3}\) & 18.2 & 19.2 & 20.1 \\ \(\mu\) & 0.3 & 0.37 & 0.43 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Surrounding rock parameters at different water contents.
## 4 Adaptive Actor-Critic Learning for ORM Tracking Control
### Adaptive Actor-Critic Design
: \(\hat{Q}_{0}(\mathbf{x},\mathbf{u})\) is initialization for Equation (16) and obeys the Bellman Equation (11) for the admissible controller \(\mathbf{C}_{0}\), i.e.,  
\[\hat{Q}_{0}(\mathbf{x}_{k},\mathbf{u}_{k})=V(\mathbf{x}_{k},\mathbf{u}_{k})+ \gamma\hat{Q}_{0}(\mathbf{x}_{k+1},\mathbf{C}_{0}(\mathbf{x}_{k+1})). \tag{19}\]Starting the AAC update law from initial state \(\mathbf{x}_{0}\), \(\mathbf{C}_{1}(\mathbf{x})\) is updated first by Equation (15), then it follows that  
\[\hat{Q}_{1}(\mathbf{x}_{0},\mathbf{u})=\hat{Q}_{0}(\mathbf{x}_{0},\mathbf{u})+ \alpha_{c}\left(\underbrace{V(\mathbf{x}_{0},\mathbf{u})+\gamma\hat{Q}_{0}( \mathbf{x}_{1},\mathbf{C}_{1}(\mathbf{x}_{1}))}_{\stackrel{{( \mathbf{x})}}{{=}}\hat{Q}_{0}(\mathbf{x}_{0},\mathbf{c}_{1}(\mathbf{x}_{0}))}- \hat{Q}_{0}(\mathbf{x}_{0^{\prime}},\mathbf{u})\right)=\hat{Q}_{0}(\mathbf{x}_{0 ^{\prime}},\mathbf{u})\leq\hat{Q}_{0}(\mathbf{x}_{0^{\prime}},\mathbf{u}), \gamma\mathbf{u}. \tag{20}\]  
Since \(\mathbf{x}_{0}\) can be any state \(\mathbf{x}\in X^{E}\), then Equation (18) holds for \(k=1\). Assume by induction that (18) holds for some \(k\). Using _Comment 3_, it follows that  
\[ \begin{array}{l}\hat{Q}_{k+1}(\mathbf{x}_{k},\mathbf{u}_{k})=\hat{Q}_{k}( \mathbf{x}_{k},\mathbf{u}_{k})+\alpha_{c}\big{(}V(\mathbf{x}_{k},\mathbf{u}_{ k})+\gamma\hat{Q}_{k}(\mathbf{x}_{k+1},\mathbf{C}_{k+1}(\mathbf{x}_{k+1}))- \hat{Q}_{k}(\mathbf{x}_{k},\mathbf{u}_{k})\big{)}\\ \leq\hat{Q}_{k}(\mathbf{x}_{k},\mathbf{u}_{k})+\alpha_{c}\big{(}V(\mathbf{x}_ {k^{\prime}},\mathbf{u}_{k})+\gamma\hat{Q}_{k}(\mathbf{x}_{k+1},\mathbf{C}_{ k}(\mathbf{x}_{k+1}))-\hat{Q}_{k}(\mathbf{x}_{k^{\prime}},\mathbf{u}_{k})\big{)} \leq\\ \stackrel{{(\ref{eq:2011})}}{{\leq}}\hat{Q}_{k-1}(\mathbf{x}_{k^{ \prime}},\mathbf{u}_{k})+\alpha_{c}\big{(}V(\mathbf{x}_{k^{\prime}},\mathbf{u} _{k})+\gamma\hat{Q}_{k-1}(\mathbf{x}_{k+1},\mathbf{C}_{k}(\mathbf{x}_{k+1}))- \hat{Q}_{k-1}(\mathbf{x}_{k},\mathbf{u}_{k})\big{)}=\hat{Q}_{k}(\mathbf{x}_{k ^{\prime}},\mathbf{u}_{k}),\end{array}  \tag{21}\]  
and since \(\mathbf{x}_{k},\mathbf{u}_{k}\) can be any pair \((\mathbf{x},\,\mathbf{u})\in X^{E}\times U\), the conclusion of _Lemma 1_ follows. _Theorem 2_. Let \(\hat{Q}_{0}(\mathbf{x},\mathbf{u})>0\), finite for any finite argument) be an initialization for the Q-function of an initial admissible controller \(\mathbf{C}_{0}\). Starting with any \(\mathbf{x}_{0}\) the control \(\mathbf{u}_{0}=\mathbf{C}_{0}(\mathbf{x}_{0})\) is applied to the process. Specifically, the AAC update laws ((15), (16)) ensures that at time \(k=1\), \(\mathbf{C}_{1}\) is updated from \(\mathbf{C}_{0}\), \(\hat{Q}_{0}(\mathbf{x},\mathbf{u})\) is updated with (16) using \(\mathbf{u}_{1}=\mathbf{C}_{1}(\mathbf{x}_{1})\) in the right-hand side, the control \(\mathbf{u}_{1}\) is sent to the process, and then \(k\gets 2\), with the above strategy repeated for subsequent times. Claim: The feedback control system under time-varying control \(\mathbf{C}_{k}\) is stabilized for \(\gamma<1\) and asymptotically stabilized for \(\gamma=1\).
## 2 Materials and Methods
### Methodology
#### 2.1.2 Microplate Incubation  
The following materials are used: multichannel pipet and sterile tips, sterile plastic multichannel reservoir, Biolog(r) EcoPlates(tm) (Biolog Inc., Hayward, CA, USA). The apparatus includes laminar flow hood, incubator, agitator and Biolog(r) Microplate Reader(tm) equipped with 750-nm and 590-nm filters.  
For preparing the microplate, 10 mL of the dilution that was chosen in plate counting was poured into a sterile reservoir of an 8-channel pipet (be careful there are no bubbles in the dilution) and 120 \(\upmu\)L of the dilution were inoculated into each well of a microplate. Not the same dilutions of the counting assay were used but new fresh dilutions were prepared. Then, the microplate was placed in its bag to avoid desiccation and incubated at 25 \({}^{\circ}\)C in dark, continuously shaking at 50 rpm on an agitator with tilting platform to obtain a uniform distribution of triphenyl formazan. Finally, spectrophotometric readings at both 590 nm (OD\({}_{590}\)) and 750 nm (OD\({}_{750}\)) were taken at time 0 and at 12-h increments (\(\pm\)12\({}_{Xh}\)) up to a 144-h incubation.  
In order to select the optimal incubation time for microplate analyses (as explained later in the Results and Discussion Section), it is recommended to follow this pattern for each incubation time (as an example, measuring times at 0 h and X h are given here):  
* calculate a colour value for each substrate well \(i\) and the blank (water) well \(b\) for each incubation time by subtracting the OD\({}_{750}\) value from the OD\({}_{590}\) value: \[0\ \mathrm{h}\mathrm{:}\] \[i_{0h}=\mathrm{OD}_{590}-\mathrm{OD}_{750}\] and \[b_{0h}=\mathrm{OD}_{590}-\mathrm{OD}_{750}\] \[\mathrm{X\ h}\mathrm{:}\] \[i_{Xh}=\mathrm{OD}_{590}-\mathrm{OD}_{750}\] and \[b_{Xh}=\mathrm{OD}_{590}-\mathrm{OD}_{750}\]
* subtract the blank well OD reading from the OD value of each substrate well to obtain a blank-corrected value (\(i_{bc}\)) for each well: \[0\ \mathrm{h}\mathrm{:}\] \[i_{bc0h}=i_{0h}-b_{0h}\] \[\mathrm{X\ h}\mathrm{:}\] \[i_{bcXh}=i_{Xh}-b_{Xh}\]
* subtract the blank-corrected OD reading at time 0 from subsequent blank-corrected readings at \(\pm\)12\({}_{Xh}\) to obtain colour development values (\(c_{i}\)) for each well for each incubation time: for example, \(c_{iXh}=i_{bcXh}-i_{bc0h}\) and set negative values to 0
* calculate the average well colour development (_AWCD_) for all incubation times separately using the equation: \[AWCD=\sum_{i=1}\frac{c_{i}}{93}\]