esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Results and Discussion  
In this section, the average BER and outage probability of UOWC links under air bubble-induced scattering with different levels of water turbidity are evaluated. Furthermore, we provide Monte Carlo simulation results to verify the proposed analytical and asymptotic closed-form expressions.  
Regarding the numerical simulation, a quasi-analytical Monte Carlo simulation approach with an equivalent noise source to improve computational efficiency has been implemented. Due to the long simulation time involved, simulation results only up to \(10^{-9}\) are included in this manuscript. Therefore, the number of bits used in our simulations is \(10^{10}\) to ensure precision [34]. As mentioned above, OOK modulation is employed with a rectangular pulse shape. The output waveform takes predefined values corresponding to a 1 or 0, with each bit occurring with a probability of 0.5. It should be noted that no channel coding scheme was incorporated. The transmitted signal is detected using a matched filter at the receiver, with an impulse response matching the rectangular pulse shape. This filter maximizes the sampled SNR, and a maximum likelihood (ML) detector minimizes symbol error probability. Simulations are conducted in baseband, without considering carrier frequency and frequency band.  
Concerning the empirical-based channel model, it must be noted that different channel conditions and fitted parameters are obtained from empirical measurements at a link distance of 3 m and a wavelength of 520 nm, which are summarized in Tables 1 and 2 [22]. The measurement-based channel model allows for a more accurate estimation of the average BER and outage probability, accurately reflecting the complex relationships among absorption, particle-induced scattering, and air bubbles. Additionally, different levels of absorption and scattering were selected to represent a wide range of water types. For instance, for 0 mg/L of antacid, the extinction coefficient is \(c=0.16\) m\({}^{-1}\), which can be compared to the extinction coefficient of clear ocean water or the Jerlov IB water type [3; 35]. In the case of the most turbid scenario, for 14.5 mg/L of antacid, \(c=1.24\) m\({}^{-1}\), the corresponding extinction coefficient is near the Jerlov 5C water type [35].
# Issues with Modeling a Tunnel Communication Channel through a Plasma Sheath
## 4 Simulations of the Tunnel Radio Communication Channel
These curves are depicted in Figure 9. Each curve represents a given receiving antenna gain \(G_{Rx}\). Figure 9 shows that the communication range can reach 350 km using a 1 Watt transmitter and usual compact antennas, which is more than enough for communication with the space vehicle [26; 27; 28].
## 3 Difficulties and Mechanism of Gob-Side Entry Driving Under Goaf
### Control Mechanism of Roof-Cutting and Roadway Protection  
To conduct the gob-side entry driving operation with a retained pillar beneath the close-distance goat, it is crucial to determine the pillar's reasonable width, which has a direct impact on the stability of the next working face, as illustrated in Figure 7.  
Figure 6: The mechanism of pillar bearing-side mining action.  
Figure 7: The influence of the width of the pillar: (**a**) the pillar’s width is too narrow; (**b**) the pillar’s width is reasonable; (**c**) the pillar’s width is too long.  
If the width of the narrow pillar is insufficient, the roof's lateral fracture line extends deeply into the area adjacent to the gob-side entry's roof. As a result, the pillar's large area is jointly compressed by the key blocks and the caved blocks from the upper seam's goaf. This compression leads to severe deformation of the gob-side entry. Conversely, if the pillar's width is excessive, it will result in resource waste and economic losses. Therefore, it is essential to ascertain the pillar's appropriate width for gob-side entry driving under the goaf. This determination aims to maintain the pillar's stability and effectively control the deformation, ensuring the safe and efficient progress of mining operations.  
After determining the reasonable pillar width of the gob-side entry under the close-distance goat, it is necessary to avoid the double compression of the pillar caused by the side face's mining. Moreover, because the lower seam's roof is damaged by the mining of the upper seam, it is difficult to form a stable overburden structure in the end area, and the roof's lateral fracture can easily penetrate the pillar's roof, resulting in serious lateral dynamic pressure. To prevent the problem of severe deformation in gob-side entry, it is proposed to cut off the roof near the pillar in advance during the mining of the side face, to block the action path of the key block on the pillar in the side face's mining. Figure 8 illustrates the comparison of the roof breaking and caving in the working face end area without roof-cutting and after roof-cutting.  
Under the premise of reasonable pillar width, the roof-cutting operation is carried out; that is, the roof is pre-split by directional blasting technology in the haulage roadway. This will cause the roof of the end area to sink and collapse to the goat along the artificial fracture surface after the side face's mining, rather than the compression effect on the narrow pillar, thus blocking the transmission path of the mining stress, to improve the mechanical environment of the gob-side entry. On this basis, the gob-side entry is reinforced and supported to realize the common control of pressure-relief and support.  
Figure 8: Roof-cutting and roadway protection mechanism: (a) the key block situation when the roof is not cut; (b) roof condition after roof-cutting.
## 3 Dataset
### Data Processing
#### 3.2.2 Speech Feature Processing  
The literature review indicates that despite the exploration of diverse speech features and fusion techniques, Mel spectrograms and MFCCs have emerged as the predominant methods in speech emotion recognition due to their complementary analytical properties. While alternative approaches such as wavelet transforms and Linear Predictive Coding exist, research suggests that increasing feature dimensionality does not consistently enhance performance due to information redundancy from feature collinearity.  
Mel spectrograms offer comprehensive time-frequency representations that align with human auditory perception, enabling the effective differentiation of emotional states in the speech of children with autism. MFCCs, derived through discrete cosine transform operations on Mel spectrograms, yield cepstral analyses that capture speech signal resonance peaks and energy distribution across frequency ranges while reducing dimensionality. This methodological approach establishes a balance between feature richness and computational efficiency.  
Comparative analysis of Mel spectrograms from speech samples of children with autism across emotional states revealed discriminative patterns, as shown in Figure 2. In happy emotional states, higher energy values were observed in high-frequency regions, whereas in calm states, energy remained predominantly concentrated in low-frequency domains. These distinct spectral patterns validate the effectiveness of Mel spectrograms in representing speech features across different emotional states in children with autism. Future research could investigate additional time-frequency representations to potentially enhance emotion recognition performance.  
The generation of the Mel spectrograms and MFCCs is illustrated in Figure 3. The original audio file was subjected to sequential pre-emphasis, frame splitting, windowing, Fourier transformation, and Mel filter bank operations to produce Mel spectrograms. Finally, a discrete cosine transform operation was applied to generate MFCCs. In accordance with the preceding steps, 667 audio segments collected from the filming process were submitted to the speech feature extraction process. After manual screening, the final numbers of Mel spectrograms and MFCCs were 24,793 and 942,134, respectively.  
Figure 1: Modal timing matching method.
## 1 Introduction
### Main Volatile Compounds (HS-SPME-GC-FID) and Odour-Active Volatile Components (HS-SPME-GC-O)  
In order to analyze the main volatile compounds, a 2 mL sample of wort/beer and an internal standard solution (0.57 mg/L 4-methyl-2-pentanol, 0.2 mg/L anethol and 1.48 mg/L of ethyl nonanoate (Sigma-Aldrich, Saint Louis, MO, USA) were placed into a 10 mL vial with 1 g of NaCl. The conditioned (250 \({}^{\circ}\)C for 1 h) SPME device (Supelco Inc., Bellefonte, PA, USA) coated with PDMS (100 \(\upmu\)m) fiber was used for sampling. It was placed into the headspace under stirring (300 rpm) for 40 min at 40 \({}^{\circ}\)C. Next, the SPME device was desorbed in the injector port of the Hewlett Packard 5890 Series II chromatograph system for 3 min. A Rxi@-1 ms capillary column (Crossbond 100% dimethyl polysiloxane, 30 m \(\times\) 0.53 mm \(\times\) 0.5 \(\upmu\)m) was used for separation of the analyzed volatiles. The column was heated using the following program: 35 \({}^{\circ}\)C for 4 min at an increment of 5 \({}^{\circ}\)C/min to 110 \({}^{\circ}\)C and then an increment of 20 \({}^{\circ}\)C/min to 230 \({}^{\circ}\)C, then maintaining a constant temperature for 4 min, and the detector temperature was 250 \({}^{\circ}\)C. Helium was the carrier gas at a constant flow of 1.0 mL/min.  
Ethyl acetate, ethyl butanoate, ethyl 2-methylbutyrate, 3-methylbutyl acetate, ethyl 4-methylpentanoate, ethyl hexanoate, ethyl octanoate, 2-phenylethyll acetate, ethyl decanoate, acetaldehyde, 1-propanol, 2-methyl-1-propanol, 3-methyl-1-butanol, and 2-phenylethanol (Sigma-Aldrich) were used as standards for qualitative and quantitative evaluation, based on a comparison of their retention times and peak surface area reads with samples.  
Odour-active volatiles of wort/beers were identified by olfactometry in the same GC system, using the same chromatographic conditions as mentioned above. Two mL sample of wort/beer with 1 g of NaCl placed in a 10 mL vial were exposed to a 2 cm divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) SPME fibre 50/30 \(\upmu\)m (Supelco/SigmaAldrich, Bellafonte, PA, USA) for 40 min at 40 \({}^{\circ}\)C and were analysed using olfactory detection port (ODP-3, Gerstel, Linthicum Heights, MD, USA). Five trained analysts described the detected odours and their intensity using a 4-point scale (not detected, weak, moderate, and strong). For each wort/beer samples, the odour-active compounds were identified (based on the results of GC-MS analysis) and grouped into nine classes. We have distinguished fruity (FR), floral (FL), roasted (R), herbaceous (H), woody (W), vegetal (V), earthy (E), animal (A) and chemical (C) aroma group. The results of the aroma intensity of a given compound were calculated as the average intensities reported by the individual raters. Only those results with an average score of at least 0.6 points were presented.
## 4 Trajectory Prediction Model Based on AIS Data and ACDE-SVM
### Analysis of Experimental Process
The comparison shows that the data predicted with de-noising are more accurate. To reduce the influence of the order of magnitude on prediction accuracy, the training set and test set data were normalized to the interval [0, 1] and the RBF was selected as the kernel function. The ACDE algorithm was used to optimize the parameters of the longitude and latitude prediction models and the optimal penalty factor \(C\), insensitive factor \(\varepsilon\) and kernel function parameter \(g\) were searched. According to Section 2.4, the parameters of the ACDE algorithm were set as follows: population size \(D\) of the ACDE algorithm set to 30, maximum iteration number \(M\) set to 200, the upper and lower limits of the scaling factor are 1.2 and 0.3 and crossover probability \(cr\) set to 0.9. The optimization results are summarized in Table 3. As can be seen from Table 3, the parameters of the two models are different. As shown in Equation (24), the input variables of the two models are the same but the output variables are different and the difference of the output variables determines the difference between the optimized parameters of the two models. Figure 4: Example noise sequences. (**a**) Longitude noise sequence; (**b**) Latitude noise sequence. Figure 5: Comparison of original and de-noised sequences.
# A Green Routing Protocol with Wireless Power Transfer for Internet of Things
## 1 Introduction
In TS, two different time periods are used to switch from decode information to energy-harvesting while in PS the RF signal is divided into two parts with different power, one for information decoding and the other one for EH [15, 16]. The first is simpler to implement then PS which, however, provides a better energy efficiency. To the best of our knowledge, energy-harvesting mechanisms based on SWIPT have not been developed until now for RPL. Therefore, the paper proposes an optimal OF for the routing tree formation in order to preserve lifetime of the entire network. In the network a limited number of nodes with sufficient power to recharge the neighbor nodes (_child_) at the maximum level of battery are present and then, by using SWIPT, one intermediate node (_parent_) can recharge its own battery with a fraction of RF signal power received from its child node. As a consequence, the best route selection is influenced by choice of intermediate nodes forwarding data messages to the root node. For example, if an intermediate node has little residual energy (RE), a classical energy-based routing object function could specify a constraint such that any path with this node and other nodes with RE below a certain threshold should be avoided. By assuming that an intermediate node can be recharged with the power transferred by a child node, the route selection can instead include this node to recharge it for allowing an effective balance of the global energy in the network. The main contribution of the paper is to address the use of SWIPT method applied to RPL. To achieve this goal, a novel OF for RPL is proposed, which is based on a composite metric combining (i) the energy consumption of intermediate nodes, due to sensing/data processing and to transmission/receiving modes, and (ii) the quantity of recharging available from the received power of a child node. This novel OF, called in the following OF2, can avoid that more loaded nodes spend their energy more intensively than other nodes, e.g., mainly the nodes closer to the root node. A performance comparison of the default and proposed OFs is also provided in terms of the Jain's Fairness Index [17], to better analyze the behavior of the OF2 for making the best decision for route selection jointly to maximize the lifetime of nodes. The rest of the paper is organized as follows: Section 2 introduces a brief overview of RPL operations, while Section 3 reviews main papers related to RPL and SWIPT methodsand outlines the differences of some related works with respect to this paper; Section 4 shows the proposed scenario; in Section 5, the proposed OF for energy-harvesting RPL is described; Section 6 validates the proposed OF by means of numerical results, and finally, in Section 7, the conclusions are drawn.
## 4 Results and Discussion
### Discussion  
The application of the results presented above focuses mainly on the operation planning of a DN. The mathematical model provides the following five key points to face unexpected failures in the utility grid:  
* Simulate a failure scenario of 72 h.
* Low computational burden due to the linearity of the formulation.
* Obtain the virtual microgrids topology scheduling that should follow the DN to keep the system balanced at all times, considering a DSR factor.
* Quantify the amount of energy that must be reduced or traded an eventual energy flexibility market, at every bus, and at every hour.
* Identify the probability of every bus to present a load curtailment, which sizes the service level under the failure scenario.  
Nevertheless, there are some limitations that could emerge if the model is run for scenarios greater than 168 h or if the model is applied for DN with a high density of buses because the time response of the model could exceed the time response of the DSO for the operation planning. Therefore, future research could focus on testing the computational burden of the model for different test distribution systems greater than 33-buses.
# Water Quality Evaluation and Prediction Based on a Combined Model
## 4 Results and Discussion
### Water Quality Evaluation Model
#### 4.1.2 Model Building Process  
In this study, T-S fuzzy neural network is used to evaluate water quality. The number of input and output nodes of the model is determined by the input and output dimensions of training samples. According to the index data considered in this paper, the input and output dimensions are determined to be three and one, respectively, so the number of input and output nodes is three and one, respectively. Through trial-and-error method, the number of hidden layer nodes is determined to be six, so the structure of water quality evaluation model is as follows: 3-6-1 [32]. The four coefficients (P\({}_{0}\), P\({}_{1}\), P\({}_{2}\) and P\({}_{3}\)), the width (b) and center (c) of the membership function were randomly initialized.  
After normalizing the input data, training of each parameter in the model was started [33]. After 100 iterations, the network finally converged, and the training error was 3.46 \({}^{*}\) 10\({}^{-4}\).
## 3 Implementation and Validation of the Proposed Framework through a One-Inch Block Experiment  
### Experimental Setup  
The proposed image enhancement framework was experimentally evaluated using a one-inch steel block test in the Earthquake Engineering Laboratory at the University of Nevada, Reno. For the largest field of view, the vision-based system monitored the test approximately at a 5 m distance and was set on the top of a shake table as shown in Figure 4. The deployed vision systems consisted of two digital cameras with specifications listed in Table 1. Two high-speed (HS) cameras that required a host computer were triggered from the control room, while the second set consisted of two DSLR cameras that were operated manually (standalone DSLR, SD). A total of 28 templates were glued to the specimen as shown in Figure 4, with a radius of the white circle of 21 mm. They were not illuminated by extra lights so the monitoring completely relied on the ambient lighting. The HS camera exposures were also set such that the captured image was completely dark and underexposed as shown previously in Figure 2a. They were set as \(f/14\) and \(1/3940\) for the \(f\)-stop number and shutter speed settings, respectively. Regarding the SD cameras, the general setting for the ambient light environment was selected as given in Table 1 with ISO 400 \(f\)-stop number of \(f/14\), and shutter speed of \(1/50\), resulting in a normal image, as shown previously in the example in Figure 2g.  
The main component of the validation test model shown in Figure 4 is a sliding bar attached to a concrete column-capital-slab specimen. The sliding bar consists of a Novotechnik displacement sensor, an aluminum plate, and six circular templates. Other templates shown in Figure 4 were used for other static experiments; however, the minimum target constraints in the bundle adjustment process required them to be included in the photogrammetry images. A one-inch magnetic block was used in the static test by inserting it to the sliding bar that displaced the six templates by exactly one inch as read by the Novotechnik sensor. Three still images were recorded in the tests, i.e., two images without the block inserted (before and after) and one when the templates were moved by exactly one inch when the block was placed. Therefore, the accuracy measured from this test was based on an absolute single value of 25.4 mm; this value was compared with the six-points measurement shown in Figure 4.
## 4 Results and Analysis  
### Numerical Simulation Results and Analysis  
#### 4.1.1 Seismic Source  
In the numerical simulation study, the Ricker wavelet is used as the source signal. Its zero-phase characteristics and narrowband spectrum distribution are conducive to generating high-quality reflection data. As shown in Figure 4, the main frequency of the Ricker wavelet is set to 25 Hz, the time sampling interval is 0.0005 s, the total time length is 0.1 s, and its mathematical expression is as follows:  
\[w(t)=\Big{(}1-2\pi^{2}f_{0}^{2}t^{2}\Big{)}e^{-\pi^{2}f_{0}^{2}t^{2}} \tag{6}\]  
where \(f_{0}\) represents the dominant frequency.
# Performance Analysis of BDS-5G Combined Precise Point Positioning
## 3 Results and Discussion  
### Experimental Environment  
#### 3.1.1 BDS Test Environment  
The experiment used observation files from the JFNG station downloaded from the MGEX website for day 225 of 2021, using BDS data with a sampling interval of 30 s and a data length of 24 h. Two scenes are shown in Figure 1 with different numbers of BDS satellites involved in positioning, red for Scene 1 and blue for Scene 2. The number of visible satellites is greater in Scene 1 than in Scene 2.