esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid LSTM-Attention and CNN Model for Enhanced Speech Emotion Recognition
## 3 Proposed Model
### Data Preprocessing and Feature Extraction  
The process of refining audio begins with a series of structured steps. Initially, using the AudioSegment library, the raw audio is efficiently loaded into an object known as the AudioSegment. Once this step is completed, the system initiates the normalization phase, ensuring that each AudioSegment object is calibrated to a standardized level of +5.0 dBFS to maintain consistency. Subsequently, it is imperative to transform this object into an array composed of individual samples. This transformation is pivotal, as it sets the foundation for all the subsequent preprocessing measures. Moving forward, the audio undergoes a trimming process, in which any extraneous silence at the start and finish is meticulously removed, ensuring that only the relevant portions of the audio are retained. To promote consistency and uniform processing across all audio files, every audio segment is then padded, ensuring that each one aligns with a standardized length. Wrapping up the procedure, a comprehensive noise reduction phase is executed, purifying the audio and enhancing its overall clarity and quality. Then, the features chosen for extraction using Librosa in our speech emotion recognition model include the following:  
_RMS_  
RMS, represented as \(f_{rms}\), quantifies the intensity or \"loudness\" inherent in the emotion signal. This metric is determined by evaluating the square root of the average of the squares of the amplitudes associated with the emotional speech samples, denoted as \(f_{i}\)[36]. In essence, RMS offers an aggregated measure of the energy present in the speech, which can be particularly pivotal in gauging the intensity of expressed emotions. By analyzing the RMS values, researchers can gain insights into the dynamism and fervor embedded in the speech, which can be instrumental in distinguishing subtle emotional nuances. Equation (1) calculates the RMS value for the emotional speech signal, which consists of N samples.  
\[\text{f}_{\text{rms}}=\sqrt{\frac{1}{\text{N}}\sum_{i=1}^{\text{N}}\text{f}_{ i}^{2}} \tag{1}\]  
Figure 1: Data preprocessing and feature extraction.  
ZCR captures the rate at which a signal crosses the zero line, serving as an indicator of the noisiness present in the speech signal. This metric can be particularly revealing because frequent zero crossings might suggest more abrupt changes in the signal, often associated with noise or certain speech qualities. By gauging the ZCR, one can glean insights into the signal's characteristics, potentially differentiating smoother speech patterns from those with more disturbances or rapid fluctuations. Equation (2) outlines the calculation of the ZCR within the time domain of the signal [35].  
\[\text{ZCR}=\frac{1}{2}\left(\sum_{n=1}^{N}\left(\text{f}(\text{x}[n])-\text{ f}(\text{x}[n-1])\right)\right) \tag{2}\]
# Clustering-Based Nonstationary Massive MIMO Channel Modeling at 1.4725 GHz
## 3 Channel Model
At the same time, the transformation can easily be performed forward and backward. In the mapping space, the algorithm will maximize the variance of the object, so that the projection of all samples can be separated as much as possible to complete the subset programing. Therefore, the ECD algorithm inherits the good performance of every parameter and simplifies the calculation. This method indicates that we can make up the whole through parts. In the above example, the multipath distribution on the array domain is shown in Figure 8a. By employing the ECD algorithm, the visible regions mapped on the array domain are shown in Figure 8b. In measurements, there are many multipaths within a cluster, and the processing of clustering will consume computing resources. As to the channel matrix, these correlated multipath components in the cluster will consume array degrees of freedom in some degree. Due to the high correlation of multipaths in a cluster, the harm to the channel gain caused by the clustering method is limited and acceptable. Therefore, modeling the Massive MIMO channel, we can consider the multi-path within a cluster as a single path to simplify the process. Here, the statistical method is replaced by searching technology to initialize the cluster core, ignoring the influence of dispersion. This method greatly simplifies the extraction of channel parameters, and it does not lose the accuracy of the model. The most important thing is that it greatly improves the efficiency of modeling. Although the correlation-based channel model ignores the small-scale dispersion characteristics of the channel in time, space and frequency domain, it can help us grasp the key items in the process of modeling. In practical applications, such as wireless communication system simulations at both the link level and system level, the Monte Carlo method is used to randomly generate multiple groups of model parameter samples on the user end. The randomness of the sample distribution can be used to simulate the small-scale dispersion characteristics and overcome the shortcomings. We also take the outdoor LOS scenario as an example. According to the above steps, we can extract the multipath cluster parameters to model the channel. The finished channel correlation matrix is shown in Figure 9. In the graph, the block effect of correlation matrix can be clearly observed. In the above analysis process, we only consider the situation that the transmitter is equipped with multi-antenna and the receiver holds a single antenna. Thus, we can only obtain the correlation matrix of the transmitter. However, because of the independence of the transmitter and the receiver, the correlation matrix of the receiver can also be obtained by the same method. Finally, according to the Kronecker modeling method, we can obtain the system matrix of Massive MIMO wireless channel as \(H=R_{Rx}^{1/2}\cdot WR_{Tx}^{1/2}\)
## 2 Materials and Methods
### Resistance Equation  
The flow-around motion of submerged structures is a classical subject in hydrodynamics, and the resistance equation for hydropower and coastal engineering problems has previously been summarized [29; 30]. As a type of water blocking structure, the TCC induces similar hydrodynamic characteristics as those of flow-around motion. The resistance equation is as follows:  
\[F=q*A=C_{d}{}^{1}/2\rho v^{2}*A \tag{8}\]  
where \(F\) is the time-averaged force (N); \(A\) is the water blocking area (m\({}^{2}\)); \(q\) is the time-averaged uniform pressure (N/m\({}^{2}\)); \(C_{d}\) is the time-averaged drag coefficient; \(v\) is the average cross-sectional velocity (m/s); and \(\rho\) is the water density (kg/m\({}^{3}\)).  
The drag coefficient (Cd) is the key factor in determining the load of a TCC. Cd is calculated according to the resistance equation, force F and velocity. Research on the drag coefficient of different TCC arrangements has an important influence on the design, construction and safety evaluation of TCC engineering.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & **Depth** & **Flow Depth Above** & **Water** & **Average** & **Load of TCC** \\  & **(m)** & **TCC (m)** & **Blocking Rate** & **Velocity (m/s)** & **(N)** \\ \hline A1 & 1.44 & 0.16 & 0.828 & 0.0069 & 4.09 \\ A2 & 1.44 & 0.16 & 0.828 & 0.0078 & 5.44 \\ A3 & 1.44 & 0.16 & 0.828 & 0.0094 & 8.25 \\ A4 & 1.44 & 0.16 & 0.828 & 0.0102 & 10.35 \\ B1 & 1.30 & 0.25 & 0.716  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Number** & **Description** & **Water Blocking Rate Pr\({}_{h}\)** & **Area Ratio Pr\({}_{s}\)** & **Inclination Ratio Pr\({}_{f}\)** & **Inflow Velocity v\({}_{\text{th}}\) (m/s)** & **Water Temperature** \\ \hline C1-C11 & Varying water blocking rates & Range: 0.3-0.8 step size & 1.0 & 0 & 0.05 & 0 \\  & \(P_{h}\) (basic working condition) & 0.05 & & & & \\ \hline D1-D6 & \multirow{2}{*}{Varying area ratios Pr\({}_{s}\)} & 0.6 & \multirow{2}{*}{Range: 1.0-1.5, step size: 0.1} & 0 & 0.05 & 0 \\ \cline{3-5}  & & 0.7 & & 0 & 0.05 & 0 \\ \cline{3-5}  & & 0.8 & & & 0.05 & 0 \\ \hline E1-E11 & \multirow{2}{*}{Varying inclination ratios Pr\({}_{f}\)} & 0.6 & 1.0 & \multirow{2}{*}{Range: 0.1-0, step size: 0.1} & 0.05 & 0 \\ \cline{3-5}  & & 0.7 & 1.0 & & 0.05 & 0 \\ \cline{3-5}  & & 0.8 & 1.0 & & 0.05 & 0 \\ \hline F1-F8 & \multirow{2}{*}{Varying inflow velocities v\({}_{\text{th}}\)} & 0.6 & 1.0 & \multirow{2}{*}{Range: 0.025-0.2, step size: 0.2} & 0 \\ \cline{3-5}  & & 0.7 & 1.0 & 0 & 0.025 & 0 \\ \hline F1-F24 & \multirow{2}{*}{Varying water temperature stratification ratios \(\beta\)} & 0.8 & 1.0 & 0 & 0.05 & Range: 0.1-0, step size: 0.1 \\ \cline{3-5}  & & 0.7 & 1.0 & 0 & 0.05 & size: 0.1 \\ \cline{3-5}  & & 0.8 & 1.0 & 0 & 0.05 & \\ \hline G23-G33 & \multirow{2}{*}{Varying water temperature stratification ratios \(\beta\)} & 0.8 & 1.0 & 0 & 0.05 & 0 \\ \cline{3-5} \cline{4-5}  & & 0.8 & 1.0 & 0 & 0.05 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Numerical simulation model scenarios.
# Sand and Dust Storms: Impact Mitigation
## 4 SDS Hazards and Their Mitigation
### SDS Source Mitigation
#### 4.1.4 Controlling Wind Erosion at Mining Operations  
The range of options available to mining companies to prevent dust generation from tailings dumps includes many of the techniques detailed for controlling blowing sand and mobile dunes [93]. They include wetting mine tailings with water, and physically covering or capping them with gravel, topsoil or synthetic materials, and the erection of protective barriers to act as windbreaks. In drylands where water is at a premium, chemical suppressants developed from petroleum are widely used to mitigate fugitive dust, and renewable biopolymers may offer a more sustainable alternative [94]. Stabilizing such dust-emitting surfaces can be achieved over the longer term by promoting the establishment of a vegetative cover, typically with plants that are native to the area as well as drought-, salt-, and metal-tolerant [95].
## 1 Introduction
This is caused by several complex phenomena that depend on the composition of oil, brine, and rock. These effects include element changes on the rock surface by chemical reaction [36], surface charge modification [37], desorption of adsorbed oil on the surface of the rock [38], and destabilization of polar compounds in the oil [39], and they will be further discussed throughout this work. CO\({}_{2}\)-EOR projects have been applied on the offshore Pre-salt carbonate fields of Brazil for over a decade [40] to improve oil recovery while avoiding the emissions from the high CO\({}_{2}\) volume in the produced gas reported in some of the fields [41; 42]. However, given the recent development of these fields and their complex rock-fluid characteristics, the impact of CO\({}_{2}\) dissolution on the flow properties and ultimate recovery is still a subject of active research. In this context, this work presents the experimental measurement of the effect of CO\({}_{2}\) dissolution on the wettability of a carbonate rock analogous to some Pre-salt reservoir fields through contact angle measurement at reservoir conditions. Wettability was altered from intermediate to water-wet by the injection of CO\({}_{2}\). In addition, the rock was characterized through routine core analysis and its pore structure was evaluated and represented using nuclear magnetic resonance and micro-computed tomography (microCT). This porous medium, which captures the dominating flow system, was used to perform pore network modelling of the immiscible flow under the capillary limit. The simulations allowed the construction of the pore-scale imbibition relative permeability curves for both systems, before and after CO\({}_{2}\) dissolution, showing evidence of the effect of the wettability shift in the flow capacity of oil and brine. The obtained plots show an increase in the crossover saturation, indicating an incremental increase in the relative permeability of oil for a greater range of saturations. To the best the author's knowledge, this is the first work applying experimental measurements of wettability in pore-scale flow simulations of flow for carbonated brine EOR in complex carbonate systems analogous to the Pre-salt reservoirs. The results suggest an incremental improvement in the flow of oil, which contributes to increased production. However, the goal of this study is to analyze the separate effect of wettability alteration on the immiscible flow in the porous medium. Other effects such as viscosity reduction and mineral dissolution should be considered when performing oil recovery numerical calculations.
# Emergency Communication System Based on Wireless LPWAN and SD-WAN Technologies: A Hybrid Approach
###### Abstract
This method is particularly suited for metering applications of non-critical parameters, such as weather meters, air quality or other environmental meters, smart agriculture, connected farms, as well as in-person, animal, and vehicle tracking [8]. As a result, ECSs may exchange messages, measure health sensor data, and track people or cars using LoRaWAN in combination with other LPWAN technologies. Operation scenarios and emergencies set strict requirements for long-distance data transmission, driving researchers to design highly prospective wide-area networks. SD-WAN technology provides a promising architecture for those wide-area networks [9]. It is regarded as a pioneering architecture of next-generation WANs, as it provides network operators with a new tool to build networks in off-grid areas. SD-WANs are applied to software-defined techniques in networking connections to cover a wide geographical area, and they can be combined with LPWAN technologies to build hybrid emergency networks. They also simplify the connection core and management between users and providers and allow the necessary monitoring, flexibility, and centralized control with lower costs. Compared with the public WAN, SD-WAN has two suitable superiorities for developing autonomous communication systems. First of all, it offers a user-friendly programmatic framework for hosting and controlling IoT applications that are created in a centralized manner while taking into account the IoT application-level needs to ensure the quality of experience for users (QoE). Second, it enables centrally defined network policies and network traffic management without needing manual configuration of user equipment [9, 10]. Based on network functionality, virtual modules can be added to overlay network combinations and edge gateways as part of the SD-WAN operating principle. In this article, we provide an overview of the state of research in the field of ECSs based on LPWAN and SD-WAN technologies. First, we introduce the evolution of current IoT implementations, then we present the basic features of these networks, and finally, we quote a simulation and a hybrid IoT application in the area of ECSs, before concluding with the targets of our future research.
## 4 Results and Discussions
### Surface Velocity Validation
Comparison between satellites (LANDSAT4-5/LANDSAT7-8/SENTINEL2) and resolutions (30/15/10 m). Averages of all images for each satellite are reported (see Table 1). Moreover, we report the surface profile of the glacier along the transects, as from ASTER Global Digital Elevation Model (Version 2), and an interpolated bedrock profile, according to [PERSON] et al. [27] and [PERSON] [29]. Figure 5: Transect TA3. Comparison, SENTINEL2 (Res. 10 m) vs. LANDSAT8 (Res. 15 m), average during three periods in 2016 (winter 2016, ID3, ID19; end of summer 2016, ID15, ID24; start–mid-summer 2016, ID16, ID5; see Table 1). Also; we report the surface profile of the glacier along the transect, as from ASTER Global Digital Elevation Model (Version 2) and an interpolated bed profile, according to [PERSON] et al. [27] and [PERSON] [29]. Figure 6: Transect B in [PERSON] et al. [27], here TB62. Comparison between surface flow velocities from (i) tracking of ablation stakes (1995–2003), (ii) velocity field from SIR-C/SAR-X (7, 9, and 10 October 1994) and those (average of all images) from (iii) SENTINEL2 (2016–2017, all 9 images, Res. 10 m) and (iv) LANDSAT7-8 (L-8, 12 images 2014–2016, Res. 15 m, L-7, 3 images 2000–2001, Res. 15 m). Sigma bounds added for SENTINEL2 and LANDSAT7-8. Notice that the estimated velocities from _TemplateMatch_ were lumped as a mean value from those of the adopted satellite, while the velocity field extracted from the SIR-C/X-SAR refers to one short period only (7, 9, and 10 October 1994). Tracking of ablation stakes as shown in Figure 6 covered a period of 8 years or so. We estimated flow velocities within the areas where [PERSON] et al. (2017) mapped movement of their ablation stakes, and accordingly, we could compare our estimates against their stakes' velocity data. In Table 2, we report goodness of fit indicators of our estimated flow velocity of the PMG.
## 2 Materials and Methods  
### Bacterial Strains, Media and Culture Conditions  
All bacterial strains used in this study are listed in Supplementary Table S1. _Salmonella enterica_ serovar Typhimurium (ST) 1120, which is an isolate from swine feces in Korea [19], was used as the parent strain for the generation of the _$fruR_, _$ssrAB_, and _$hfq_mutant strains. _Salmonella_ strains lacking _fruR_, _ssrAB_, and _$hfq_ were originally constructed in ST14028, as described in a previous study [20], and the mutated genetic alleles marked with a kanamycin resistance cassette were transferred to ST1120 using P22 HT105/1 intro-201-mediated transduction [21]. The antibiotic cassette was subsequently removed by introducing pCP20, which produces a flip recombinase [22; 20]. The removal of the kanamycin resistance cassette was examined by incubating the recombinants on an agar plate containing kanamycin at 50 \(\mathrm{\SIUnitSymbolMicro g}\)/mL. The final non-polar deletions without the antibiotic cassette were verified using diagnostic PCR. Primers used in the diagnostic PCR are listed in Table S2. ST2173 used in the challenge experiments was constructed by transforming ST14028 with the pBBR1-MCS4 plasmid as described previously [23]. _Salmonella_ strains were cultured in Luria-Bertani (LB) medium at 37 \(\mathrm{\SIUnitSymbolCelsius}\) with antibiotics when required, unless otherwise specified. All antibiotics were purchased from Sigma-Aldrich (St. Louis, MO, USA). The biochemical characteristics of bacteria were identified using the analytical profile index (API) 20E kit (bioMerieux, Inc., Durham, NC, USA).
# An Image Processing Method for Extraction of the Stress Wave Reflection Period
## 2 Algorithm for the Automatic Extraction of the Reflection Period Based on Image Processing
### Step 5: Image Binarization  
Since the object of the binarization operation is a grayscale image, it is necessary to grayscale the color image. The method of image graying is a maximum value method, which takes the maximum values of the R, G, and B components in the color image as the values of the gray image, as shown in Equation (5).  
\[I^{\prime}_{X}(i,j)=\max(R(i,j),G(i,j),B(i,j)), \tag{5}\]  
where \(I^{\prime}_{X}\) represents the grayscale image of \(I_{X}\), and \(R(i,j),G(i,j),B(i,j)\) represent the red, green, and blue pixel values at the position \((i,j)\) of the image, respectively. The maximum method preserves the periodic information of the image to the utmost extent. In order to filter out the interference of fine texture and save useful information, binary filtering needs to choose the appropriate threshold. Since the value of the grayscale image is the maximum of the three components, the gray histogram distribution of the grayscale image is biased to the right of 128. Thus, the threshold of binarization is selected to be 128.
## 3 Methodology
### Extraction of the LOS and Azimuth Coseismic Deformation Field
In particular, we used the estimated differential ionospheric phase to mitigate the relative azimuth  
Figure 2: The coseismic deformation maps in the LOS directions. (**a,b**) obtained from ALOS-2 DInSAR processing. (**c,d**) obtained from Sentinel-la DInSAR processing. The red star represents the epicenter of the mainshock; The red solid squares represent the city Halabja and Sarpol-e-Zahab. The black line AB is the deformation bound. shift caused by ionosphere in the MAI interferogram. The AT deformation measured by the MAI can be defined by the following equation [30]:  
\[\Delta x=-\frac{\partial_{MAI}v_{g}}{2\pi nK_{a}T_{C}} \tag{1}\]  
where \(\Delta x\) is the AT deformation, \(\partial_{MAI}\) is the phase of the MAI interferogram, \(v_{g}\) is the velocity of the beam footprint on the ground, \(K_{a}\) is the azimuth frequency modulation (FM) rate, \(T_{C}\) is the burst cycle length, and \(n\) is the difference of burst numbers. Figure 3 shows the AT deformation maps. (a) and (b) are the MAI results based on ascending and descending images, respectively. The displacements of ascending and descending are a little different, because the projected thrust motion vectors along the ascending and descending azimuth direction are different. Figure 3 indicates that the AT deformation is concentrated in the lobe G, and the measured maximum surface displacement is approximately 50 cm away from the SAR sensor. It reflects the earthquake has some thin features of strike slip.
## 3 Controller Design
### Stability Analysis
\begin{array}{l}\dot{V}_{1}\leq-\min(M,K)\big{(}{s_{1}}^{2}+{s_{2}}^{2}+{y_{1}}^{ 2}\big{)}^{\frac{{{\tau_{1}}+1}}{2}}\\ -2^{\frac{{{\tau_{2}}}}{2}}\min\Big{(}2^{\frac{{{\tau_{1}}-{{\tau_{2}}}}}{2}}N,K \Big{)}\big{(}{s_{1}}^{2}+{s_{2}}^{2}+{y_{1}}^{2}\big{)}^{\frac{{{\tau_{2}}+1} {2}}}+\frac{1}{2}{\xi_{{\omega_{z}}}}^{2}\\ \leq-2^{\frac{{{\tau_{1}}+1}}{2}}\min(M,K)V_{1}\frac{{{\tau_{1}}+1}}{2}\\ -2^{\frac{{{\tau_{2}}}}{2}}\min\Big{(}2^{\frac{{{\tau_{2}}-{{\tau_{2}}}}}{2}}N,K \big{)}\cdot 2^{\frac{{{\tau_{2}}+1}}{2}}V_{1}\frac{{{\tau_{2}}+1}}{2}+\frac{1}{2}{\xi_{{ \omega_{z}}}}^{2}\\ \leq-\Gamma_{1}V_{1}\frac{{{\tau_{1}}+1}}{2}-\Gamma_{2}V_{1}\frac{{{\tau_{1}}+1 }}{2}+\Delta\end{array}  \tag{61}\]where \(\Gamma_{1}=2^{\frac{\gamma_{1}+1}{2}}\min(M,K)\), \(\Gamma_{2}=2\min\left(2^{\frac{1-\gamma_{2}}{2}}N,K\right)\), \(\Delta=\frac{1}{2}{\varsigma_{\omega_{2}}}^{2}\). Following Assumption 2 and Lemma 2, we can conclude that the system is fixed-time stable and the system states will converge to an arbitrarily small neighborhood of the origin. The attractive region is \(V_{1}\leq 2\gamma\) with \(\Gamma_{1}\gamma^{\frac{1+\gamma_{1}}{2}}+\Gamma_{2}\gamma^{\frac{1+\gamma_{2} }{2}}=\Delta\), and the convergence time \(t_{2}\) is bounded by  
\[t_{2}\leq\frac{1}{\Gamma_{2}}\frac{2}{r_{2}-1}+\frac{1}{\Gamma_{1}\left(2^{ \frac{1+\gamma_{1}}{2}}-1\right)}\frac{2}{1-r_{1}} \tag{62}\]  
In summary, the closed-loop system is fixed-time stable with the convergence time \(t\) bounded by \(t\leq t_{1}+t_{2}\). The proof is completed. \(\square\)