esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Embedded Space with Low-Level Features for Speech Emotion Recognition
## 3 Experiments and Results
### Datasets  
The experiments were conducted on the Berlin Database of Emotional Speech (EmoDB) [31] and RAVDESS dataset [32].  
The EmoDB database contains 535 sentences in German language that are recorded as monophonic with a sample rate equal to 16 kHz. The utterances are spoken by ten actors, including five men and five women. These actors speak ten different sentences in the following emotional states: anger (_ang_), disgust (_dis_), fear (_fea_), happiness (_hap_), sadness (_sad_), boredom (_bor_) and neutral (_neu_). Figure 4 shows distributions of recordings for each  
Figure 3: Audio representation for selected recording (03a04 Nc.wav) with a neutral emotional state. From top to bottom: CQT spectrogram, the Mel spectrogram, fundamental frequency, and formants.  
emotion. Additionally, Table 1 shows the distribution of recordings for individual speakers and emotional states.  
The RAVDESS database contains 1440 monophonic sentences in English language with a 48 kHz sampling rate. The utterances are spoken by 24 actors, including 12 men and women, two sentences per actor in eight emotional states: neutral, calm (_cal_), happiness, sadness, anger, fear, disgust, and surprised (_sur_). Recordings representing particular emotions in the RAVDESS database have two levels of intensity, unlike the EmoDB database. For a more accurate comparison of the results obtained between databases, we used only recordings with the same emotional intensity equal to 2 from the RAVDESS database, except for the neutral state, where all recordings had the same intensity level. The final set contained 96 recordings for every emotional state, giving 768 examples.
# Advantages and Limitations of Current Microgravity Platforms for Space Biology Research
## 5 Orbiting Microgravity Facilities
### The International Space Station
#### 5.2.1 Mass and Volume  
Access to the ISS is limited by the cost of launching and returning the mass (and associated volume) of a payload and the operational constraints. Limitations in the \"up mass\" and \"down mass\" affect the quantity of experimental material that can be launched. In particular, mass and volume restrictions impact the complexity of scientific experiments in terms of the number of variables, sample types, number of replicates, etc.
## 2 Characterization of FBG
### Sensing Characteristics of FBGs During Temperature Changes  
Changes in temperature cause changes in \(\lambda_{B}\), which is due to the combined effects of thermal expansion and contraction, thermo-optic and elasto-optic effects [20; 21].  
A temperature change from (1) is shown as follows:  
\[\Delta\lambda_{B}=2\Delta n_{eff}\Lambda+2n_{eff}\Delta\Lambda=2\Delta n_{eff }\Lambda+2n_{eff}\Lambda\Delta T\alpha \tag{11}\]  
A change in \(n_{eff}\) at different temperatures is shown as follows:  
\[\Delta n_{eff}=\frac{\partial n_{eff}}{\partial T}\Delta T+\left(\Delta n_{ eff}\right)_{ep}+\frac{\partial n_{eff}}{\partial a}\Delta a \tag{12}\]From the above, we can obtain:  
\[\Delta\lambda_{B}=2\bigg{(}\frac{\partial n_{eff}}{\partial T}\Delta T+\left( \Delta n_{eff}\right)_{ep}+\frac{\partial n_{eff}}{\partial a}\Delta a\bigg{)} \Lambda+2n_{eff}\frac{\partial\Lambda}{\partial T}\Delta T \tag{13}\]  
Among them, \(\left(\Delta n_{eff}\right)_{ep}\) are the refractive index variables in the presence of bouncing light effect; \(\partial n_{eff}/\partial a\) is the waveguide effect; \(\partial n_{eff}/\partial T\) is the refractive index variable, denoted by \(\xi\); \(\partial\Lambda/\partial T\) is the coefficient of thermal expansion, denoted by \(\alpha\).  
Temperature sensitivity coefficient formula [22; 23]:  
\[\frac{\Delta\lambda_{B}}{\lambda_{B}\cdot\Delta T}=\zeta+\frac{1}{n_{eff}}K_{ avg}\cdot\alpha+\alpha \tag{14}\]  
Among them, \(K_{avg}\) is the wavelength variation coefficient of the waveguide effect. For waveguide effects, the impact is negligible.  
The relative temperature sensitivity factor is obtained from Equation (14):  
\[K_{T}=\zeta+\alpha \tag{15}\]  
Available to: \(\frac{\Delta\lambda_{B}}{\lambda_{B}}=K_{T}\Delta T\)  
The wavelength change versus temperature change equation can be expressed as in the above equation. From this, it can be concluded that \(\Delta\lambda_{B}\) and \(\Delta T\) are linearly related [24; 25; 26; 27; 28].
# A Novel Anti-Jamming Technique for INS/GNSS Integration Based on Black Box Variational Inference
## 4 Experimental Results and Analyses  
In a previous study, the performance of the VBAKF was compared with the existing AKF in dealing with time-varying MNCM, and VBAKF proved to be superior to the existing AKF [11]. Therefore, we only compared the BBVI with the VBAKF algorithm, whose prior distribution is an approximate Inv-Gamma and Inv-Wishart distribution. Furthermore, the performance of the BBVIF anti-jamming algorithm with Gaussian distribution was verified by simulation and trial data.
## 2 Materials and Methods
### Description of the Intelligent Irrigation Control System (IICS)
#### 2.3.1 Soil Moisture Monitoring System  
The soil moisture monitoring system consisted of 6 sets of data collectors (MC302 L), 48 Hydra Probe II (Stevens, Portland, Oregon, USA) soil moisture sensors and installation accessories (Figure 3a). The system automatically monitored soil moisture in the 12 plots. Four soil moisture sensors were installed in each plot at depths of 10, 20, 50 and 80 cm below the surface (Figure 3b). Each collector measured data in two control pots through a SDI-12 bus. Data were recorded every 5 min and sent to a cloud server simultaneously. Users can directly log into the website to view, download and analyze the data.  
Hydra Probe II is an improved version of Hydra Probe soil sensor. The Hydra probe II is calibrated to the soil moisture according to the true dielectric constant, and the calibration is less affected by the soil properties, so the measured data are more accurate. The Hydra Probe II soil sensor supports both SDI-12 and RS-485 communication protocols, providing users with more choices. Different from other soil sensors, the Hydra Probe II soil sensor integrates multiple measurement elements. One measurement can obtain multiple element data such as soil moisture, conductivity, salinity and soil temperature, and it responds quickly, making it easy for users to obtain more information. The sensor can be connected with various types of data collectors and has good compatibility. Hydra Probe II adopts a  
Figure 3: (**a**) Soil moisture monitoring system; (**b,c**) are the components of the soil moisture monitoring system. It mainly includes solar panel, data collector and soil moisture sensors. The sensors were installed in each plot at depths of 10, 20, 50 and 80 cm below the surface.  
compact integrated design and exquisites manufacturing technology to make it suitable for long-term field measurement work without special maintenance.  
MC302 L is a low cost, low-power consumption, multifunction data collector with an integrated solar charging controller, polymer lithium battery, GPRS/GPS, true color touch screen and other components. MC302 L can realize measurement and data storage with analogs, switches, frequencies and other interface sensors and is able to send the data remotely through the network.
## 3 Thermodynamic Modelling of Cement Hydration
### Oversaturation  
It has been reported in the literature [25] that solutions are oversaturated with respect to gypsum, portlandite, syngenite and ettringite during the first 12 h of cement hydration. To increase the solubility of these phases during the first 12 h of hydration, and to also  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Clinker**} & \multicolumn{8}{c}{**Oxide**} \\ \cline{2-7}  & **K\({}_{2}\)O** & **Na\({}_{2}\)O** & **MgO** & **SO\({}_{3}\)** & **K\({}_{2}\)O (wt.\%)** & **Na\({}_{2}\)O (wt.\%)** & **MgO (wt.\%)** & **SO\({}_{3}\) (wt.\%)** \\ \hline C\({}_{3}\)S & 0.1 & 0.1 & 1.1 & 0.1 & 5.26 & 7.69 & 18.33 & 33.33 \\ \hline C\({}_{2}\)S & 0.9 & 0.1 & 0.5 & 0.2 & 47.37 & 7.69 & 8.33 & 67.67 \\ \hline C\({}_{3}\)A & 0.7 & 1.0 & 1.4 & 0 & 36.84 & 76.92 & 23.33 & 0 \\ \hline C\({}_{4}\)AF & 0.2 & 0.1 & 3.0 & 0 & 10.53 & 7.69 & 50.0 & 0 \\ \hline Total & 1.9 & 1.3 & 6.0 & 0.3 & 100 & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Composition and percentages of oxides dissolved in cement clinker phases [20].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Clinker Phase** & **Model Approach** \\ \hline C\({}_{3}\)S & Kinetic \\ \hline C\({}_{2}\)S & Kinetic \\ \hline C\({}_{3}\)A & Kinetic \\ \hline C\({}_{4}\)AF & Kinetic \\ \hline K\({}_{2}\)O & Kinetic (dissolved in C\({}_{3}\)S, C\({}_{2}\)S, C\({}_{3}\)A and C\({}_{4}\)AF) \\ \hline Na\({}_{2}\)O & Kinetic (dissolved in C\({}_{3}\)S, C\({}_{2}\)S, C\({}_{3}\)A and C\({}_{4}\)AF) \\ \hline MgO & Kinetic (dissolved in C\({}_{3}\)S, C\({}_{2}\)S, C\({}_{3}\)A and C\({}_{4}\)AF) \\ \hline SO\({}_{3}\) & Kinetic (dissolved in C\({}_{3}\)S and C\({}_{2}\)S) \\ \hline Na\({}_{2}\)SO\({}_{4}\) & Equilibrium—immediately dissolved during step one \\ \hline K\({}_{2}\)SO\({}_{4}\) & Equilibrium—immediately dissolved during step one \\ \hline Lime & Equilibrium—immediately dissolved during step one \\ \hline Calcite & Equilibrium—reacting instantaneous equilibrium \\ \hline Gypsum & Equilibrium—reacting instantaneous equilibrium \\ \hline Periclase & Equilibrium—immediately dissolved during step one \\ \hline \hline \end{tabular}
\end{table}
Table 5: Modelling approach for the hydration behaviour of the clinker phases.  
include C-S-H and brucite, an oversaturation factor of 0.15(n) was applied, where \(n\) is the number of charged species involved in the dissolution reaction (Table 6). Furthermore, as a result, gypsum was inhibited from precipitating, as it would be fully depleted within this 12 h timeframe. In PHREEQC, the oversaturation factor was used as the target saturation index in the EQUILIBRIUM PHASES keyword, which then returned to zero after 12 h of hydration.
## 2 Materials and Methods
### Plasmid Biosynthesis and Recovery  
The 6.07 kbp plasmid pcDNA3-FLAG-p53 was amplified in a _Escherichia coli_ (_E. coli_) DH5\(\alpha\) cell culture at 37 \({}^{\circ}\)C in an Erlenmeyer flask with 250 mL of Terrific Broth medium (20 g/L of tryptone, 24 g/L of yeast extract, 4 mL/L of glycerol, 0.017 M KH\({}_{2}\)PO\({}_{4}\), 0.072 M K\({}_{2}\)HPO\({}_{4}\)) supplemented with 30 \(\upmu\)g/mL of ampicillin. The cells were grown until the late log phase (OD600 nm \(\pm\) 9), and then were collected by centrifugation and stored at -20 \({}^{\circ}\)C. For the recovery of nucleic acids, cells were lysed using the modified alkaline lysis method [17; 18]. Briefly, cell pellets were resuspended in 10 mL of solution I (50 mM glucose, 25 mM Tris-HCl and 10 mM ethylene-diamine tetra acetic acid (EDTA), pH 8.0). Alkaline lysis was performed by adding 10 mL of solution II (200 mM NaOH and 1% (w/v) sodium dodecyl sulphate). After 5 min of incubation at room temperature, cellular debris, gDNA, and proteins were precipitated by gently adding and mixing 10 mL of prechilled solution III (3 M potassium acetate, pH 5.0), followed by 20 min incubation on ice. Cellular debris was removed by centrifuging twice at 20,000 g for 30 min, at 4 \({}^{\circ}\)C. For the concentration and clarification [18], the pDNA in the supernatant was precipitated by adding 0.7% (v/v) of isopropanol and recovered by centrifugation at 15,000 g for 30 min at 4 \({}^{\circ}\)C. The pellets were then dissolved in 2 mL of 10 mM Tris-HCl buffer, pH 8.0. In the next step, ammonium sulfate was dissolved in the pDNA solution up to a final concentration of 2.5 M. Following a 15 min incubation on ice, precipitated proteins and RNA were removed by centrifugation at 10,000 g for 20 min at 4 \({}^{\circ}\)C. To obtain the native pDNA (oc + sc) the NZYtech Maxi Prep Kit was used, according to the manufacturer's instructions.
# Preliminary Impact Assessment of the Ad Hoc Separation Minima: A New Separation Mode
## 2 Ad Hoc Separation Conops
### Operational Scenario  
For the development of the methodology, the application of this concept in this first stage is limited to:  
* En-route airspace, sectors of medium-high complexity and traffic density.
* Upper en-route FLs are considered (FL 340-FL 660).
* Aircraft flying at the same FL. These are the majority of aircraft in the cruise segment. This means that it is just considered horizontal separation minima.
* A scenario of predefined routes.  
Nevertheless, once the feasibility of this novel concept is demonstrated, its application will be extended to 3D (aircraft in evolution) and to Free Route Airspace.
# Influence of Incident Shock on Fuel Mixing in Scramjet
## 5 Conclusions  
This study utilized the RANS equations to perform a numerical simulation of an X-51-like vehicle. With non-uniform incoming conditions, the research examined the behavior of fuel mixing in the combustion by varying the length of the isolator section and the number of injections. The following conclusions are obtained:  
1. The supersonic non-uniform incoming flow generates incident shock waves, resulting in an uneven pressure distribution across the flow cross-section. The selection of injection locations significantly impacts fuel mixing performance. When injection holes are placed in local high-pressure regions, the separation zone before the jet is smaller, leading to a 15% increase in penetration depth. Conversely, choosing injection holes in local low-pressure areas results in a larger separation zone and a broader spanwise propagation range, enabling a more uniform fuel distribution.  
2. The interaction between shock waves and the boundary layer can induce the formation of a separation shock. This intricate interplay among incident shocks, the bow shocks emanating from multiple jet injections, and the boundary layer-induced separation shock creates localized high-pressure zones downstream. These high-pressure zones behind the wave coupled with the low pressure on the leeward side of the jet form a pronounced adverse pressure gradient. The boundary layer separated on the leeward side of the jet, inducing a separation shock wave. As the fuel plume traverses this separation shock, it is redirected toward the core flow, achieving a deeper penetration. This process significantly enhances the mixing efficiency by nearly 50%.  
3. An increase in the separation zone ahead of the jet and a thickening of the jet boundary layer facilitate the spanwise transport of fuel by the horseshoe vortices. Nevertheless, in cases of multiple injections, an intensified spanwise transport can cause fuel to accumulate in the corner areas, ultimately resulting in a 10-25% reduction in mixing efficiency.  
Fuel mixing constitutes merely one aspect of scramjet performance research; the influence of shockwaves on combustion will be addressed in the subsequent phase of the work.  
Conceptualization, C.W., Y.Y. and H.W.; methodology, C.W. and Y.Y.; software, C.W. and H.W.; validation, C.W. and Y.Y.; formal analysis, C.W., H.W. and Y.Y.; investigation, C.W. and H.W.; resources, H.W. and Y.Y.; data curation, C.W., X.L. and H.W.; writing--original draft preparation, C.W.; writing--review and editing, H.W.; visualization, C.W.; supervision, Y.Y., X.L. and H.W.; project administration, Y.Y. and H.W.; funding acquisition, H.W. and Y.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under Grant number (Nos. 11925207, 12102471, and 92252206).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.
In relation to temperature prediction, in [10; 11; 12; 14; 15; 16], they have obtained good results using artificial neural networks (ANN) and support vector machines (SVM). In [15], the best result reaches a mean square error (MSE) of 0.136 \({}^{\circ}\)C.  
Concerning solar radiation predictions [17; 18; 19; 20; 21; 22; 23; 24; 25], classification systems were mainly used, such as ANN and SVM. In particular, in [25], the best result reached a 99.9% of accuracy.  
Respecting to wind speed predictions, works like [26; 27; 28; 29; 30] makes use of classifications systems like ANN and SVM. Best result was obtained in [26], where the mean absolute error (MAE) was 0.85 m/s.  
As seen on previous works, based on meteorological forecasting, it observes the great importance and application on different fields, as energy generation, or helping elderly people. For this reason, the purpose of this work merges with the motivation to collaborate with these applications. It consists of the design of a prediction system that can be used under different conditions to get a forecasting of different meteorological parameters as wind speed, temperature, solar radiation or precipitation. The innovative point of this work versus previous researches is the adaptability of the method to different meteorological phenomena. The correct meteorological characterization of the study area will allow to determine the moment in which appears a peak of energy demand. The information used in this study comes from the State Meteorological Agency (Agencia Estatal de Meteorologia, AEMET, in Spanish) that depends on the Spanish Government. The regression functions used in this study are presented below:  
* Support vector machines (SVM). Using different kernel functions to get an optimized result.
* Regression tree.
* Fit linear model (FITLM).  
As shown on this section, there are a lot of studies that carries out predictions of meteorological parameters. The statistical used to probe the proposed models varies between MAE and MSE and the classification systems vary between ANN and SVM. Many of them make short time predictions and even long time predictions too. In addition, errors reached in previous researches are higher than those obtained in this research.  
But the real proof of the goodness of the proposed model is that, as it is shown on the following sections, we only applied one model to the whole experiment regardless of the meteorological phenomena involved in the prediction. This is a scientific value added into literature.  
The novelty of the system consists of the feasibility to use it in different geographical location only using data from meteorological stations. The proposal does not need to introduce data from geopotential highs or sea level pressures (SLP) in order to obtain an accurate prediction in the case of wind and precipitation. Besides, this proposal can be used for different climate parameters. It shows the robustness and any similar works has not been found in the state-of-the-art.
### Virtual Telescope
#### 1.2.1 Long-Baseline Telescope  
To achieve longer focal lengths in the X-ray and gamma-ray regions of the electromagnetic spectrum, formation flying can be advantageous [19]. There have been numerous proposals for X-ray imaging missions, including XEUS (X-ray Evolving Universe Spectroscopy), Simbol-X, and FLIP-3 [16; 20; 21]. The XEUS was intended to place dual spacecraft in an L2 orbit, but it was absorbed into the Constellation-X project and transformed into a monolithic mission. The Simbol-X mission was supposed to operate in a highly elliptical orbit with a 4-day time span, but this plan was abandoned owing to budgetary constraints. For FLIP-3, the X-ray imaging lenses have a focal length of 100 m, which corresponds to the distance between the lens and the detector [16]. At the Sun-Earth L1 Lagrangian point, quasi-continuous aligned observation was attainable with a control accuracy of within 2 cm in the transverse direction and 25 cm in the axial direction. Milli-Arc-Second Structure IMager (MASSIM) is a mission concept that employs transmissive, refractive, and diffractive optics and aims for X-ray astrophysical measurements in space [15; 19; 22]. To achieve a milliarcsecond angular resolution, the MASSIM requires 1000 km of inter-spacecraft separation, within 25 mm of transverse displacement, and 10 km of axial offset. The formation will be located at L2 and will have 1-10 days to observe the celestial target. Fresnel and MAXIM were created as extreme versions of MASSIM, with the goal of achieving microarcsecond angular resolution [15; 19; 20; 21; 22; 23; 24]. With a 5-10 m-diameter lens and a focal length of 10\({}^{5}\)-10\({}^{6}\) km, gamma-ray detection will examine the event horizon surrounding supermassive black holes.
# Systematic Evaluation on Speckle Suppression Methods in Examination of Ultrasound Breast Images
## 3 Evaluation Criteria Analysis for De-Speckling Performance
### Tumor Region Segmentation Accuracy  
Image filtering is an important step in image preprocessing. It serves subsequence image analysis such as the segmentation. Speckle suppression filter is expected to debase the influence of speckle noise and improve the segmentation accuracy of region of interest (ROI). To this end, we apply Fuzzy C Mean (FCM) algorithm [51] in segmentation of tumor regions of BUS images which are filtered by the algorithms investigated in this study and evaluate their performances by comparing the segmentation accuracies.  
The performance of segmenting tumor region is evaluated by using a metric called averaged radical error (ARE). As reported in [52; 53; 54; 55], it is defined as  
\[ARE(n)=\frac{1}{n}\sum\limits_{i=0}^{n-1}\frac{\left|Cs(i)-Cr(i)\right|}{\left| Cr(i)-Co\right|}\times 100\% \tag{38}\]  
In Figure 1, the solid line denotes the \"true\" boundary of an object and the dashed line denotes the boundary produced by the image segmentation. A number of radius vectors which are evenly spread with \(Co\) as their center are emitted from the center of the object. The ratios of the distance from \(Cr(i)\) to \(Cs(i)\) to that from \(Cs(i)\) to \(Co(i)\), \(i=1\dots\ n\), is summed up and then averaged to obtain the ARE. The \"true\" delineation is obtained by manual tracing carried out by 3 radiologists. An example of estimated delineation is the boundary of the tumor region segmentation result, as illustrated in Figure 2. The red curve represents the \"true\" delineation, and the pink curve is the boundary of the segmentation result.
## 5 Experimental Verification  
### Bidirectional Fading Emulator  
Figure 13 shows the configuration of the developed two-dimensional bidirectional fading emulator. To achieve a good multipath propagation environment, 14 scatterers are arranged at equal angular intervals on a circle of radius 1.2 m [34]. The probes comprise vertically polarized half-wavelength sleeve dipole antennas. The MIMO terminal is located at the center of the emulator. This fading emulator functions as an operating algorithm based on the same formulation described in [24]. The instruments, such as the network analyzer (Keysight, HP8753E), power combiner/divider (mini circuit, ZAPD-2-S+ and ZB8 PD-252-S+), and phase shifter (mini circuit, JSPHS-23+), have bidirectional characteristics at the measurement frequency. This apparatus generates a fading environment by radio-frequency (RF) processing, and it can be implemented even if transmission and reception are reversed; thus, the performance of the uplink channel from the MIMO terminal can be evaluated.  
To measure the uplink channel properties, a wave transmitted from a network analyzer is emitted from the MIMO terminal antenna and is then received at each probe. The received signals are controlled using phase shifters operated by a digital-to-analog converter to emulate the Rayleigh fading channel. These are compounded by a power combiner and then measured using a network analyzer. This apparatus has a high time correlation characteristic of approximately 0.995; thus, each channel response can be measured individually.  
Figure 12: Arrangement of virtual point sources: (**a**) \(\phi\) = \(45^{\circ}\) in a circular array; (**b**) \(\phi\) = \(0^{\circ}\) in a circular array; (**c**) \(\phi\) = \(0^{\circ}\) in a linear array.