esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Feature Extraction of Acoustic Signal
### Double-Domain Feature Extraction Method
#### 3.1.2 LPC  
LPC is a parameter model based on acoustic synthesis. The purpose of linear prediction is to predict the present or future sample values. Therefore, the current sampling value of the input signal is calculated by the relationship between the past sampling value, the current sampling value and the future sampling value [22]. In this paper, the relationship between the two was closer so that the error was also reduced to a minimum. Finally, a unique set of sixth-order linear prediction coefficients is obtained [23].  
For the sampled acoustic signal \(s(n)\) at a given time, the linear combination of the preceding \(p\) samples can be used for prediction. The expression is as follows:  
\[s(n)=a_{1}s(n-1)+a_{2}s(n-2)+\ldots+a_{p}s(n-p)+u(n) \tag{2}\]  
where \(a_{1}\), \(a_{2}\), \(\ldots\)\(a_{p}\) is the linear prediction coefficient of the previous \(p\) samples, and \(u(n)\) is the prediction error. Z transforms \(s(n)\) to obtain \(S(z)\).  
\[S(z)=\sum_{k=1}^{p}a_{k}z^{-k}S(z)+U(z) \tag{3}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Specification** & **Value** \\ \hline Column height & 2230 mm \\ Type of tray & Sieve tray \\ Tray spacing & 100 mm \\ Number of trays & 9 \\ Column diameter & 50 mm \\ Diameter of sieve pore & 2 mm \\ Opening rate & 8.7\% \\ Operation state & Total reflux \\ Reboiler duty & 3.19 kW \\ Gas flow velocity & 3.0–4.6 m/s \\ Top temperature & 81.5 \({}^{\circ}\)C \\ Bottom temperature & 93.4 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 1: The geometrical parameters of the sieve tray distillation columns and operational parameters.  
The transfer function of the linear predictive synthetic filter system can be written as  
\[H(z)=\frac{S(z)}{U(z)}=\frac{1}{1-\sum\limits_{k=1}^{p}{a_{k}z^{-k}}} \tag{4}\]
## 1 Introduction  
Atmospheric pressure glow discharge (APGD) is now a fascinating area of research in low temperature plasma physics. It provides a chamber-free and low cost route for industrial applications, such as surface modification, functional film deposition, and biological purification [1; 2; 3; 4; 5]. Compared with bare electrodes, dielectric barrier discharge (DBD) allow plasma to retain its large volume without constriction [6].  
On the other hand, radio frequency DBD is now a popular source used to achieve a stable and large-gap atmospheric pressure glow discharge with lower breakdown voltage and avoid emergence of arc discharge and filamentary discharge, compared with DC glow discharge and low and medium frequency DBD at atmospheric pressure [6; 7; 8; 9; 10; 11]. Therefore, a lot of study has been done on the discharge mechanism, simulation, and speckle pattern of radio-frequency dielectric barrier discharge (rf-DBD) at atmospheric pressure [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23].  
Many experiments demonstrated that there are \(\alpha\) mode and \(\gamma\) mode in rf-DBD [6; 8; 10]. The mechanism of discharge in \(\alpha\) mode is volume ionization, electron avalanche occurs under the acceleration of electric field. In every current cycle, there are two volume ionization process of opposite direction in the first half cycle and the second half cycle. The change from \(\alpha\) mode to \(\gamma\) mode happens when the electric field intensity reach a certain value to give the positively charged ions a certain speed in half a cycle, so they could break the sheath binding and bombard the dielectric on cathode, thus producing a large number of secondary electrons.  
Therefore, an important mark to distinguish the two discharge modes is the negative glow. [PERSON]'s team has earlier used ICCD camera (Andor i-Star DH720, Belfast, UK) to capture photos of negative glow at 1/4 t and 3/4 t of the discharge cycle (current) as evidence for mode transition of rf-DBD. The time characteristics, including the time and duration of occurrence of electron avalanche (volume ionization) and negative glow in one cycle have not been reported. In this paper, the ICCD camera was used to capture 36 photos in a radio frequency cycle (73.7 ns). The temporal characteristics of electron avalanche and negative glow were studied based on changes of the luminous intensity of different plasma position in one cycle under different discharge modes.
## 4 Discussion  
The experimental results underscore the effectiveness of the Cerberis3 Quantum Key Distribution (QKD) system by ID Quantique in generating and distributing secure cryptographic keys. The system consistently achieved a high secret key rate, crucial for supporting secure real-time communication. Additionally, the Quantum Bit Error Rate (QBER) was maintained at low levels throughout the testing period, signifying that the quantum channel was secure and reliable. This low QBER indicates minimal errors in the key generation process, reducing the compromised security likelihood.  
The high link visibility observed during the experiments reflects a favorable signal-to-noise ratio within the quantum channel. This metric is important, as it demonstrates that the quantum states used for key distribution were clearly distinguishable, minimizing the risk of key errors and enhancing the overall robustness of the encryption.  
The Cerberis3 QKD system, with its advanced technological features, proves to be a robust and reliable solution for organizations with stringent data security requirements. The system's ability to maintain high secret key rates, low QBER, and excellent link visibility highlights its suitability for deployment in environments where data integrity and confidentiality are paramount. These characteristics make the Cerberis3 QKD system an ideal choice for sectors such as finance, healthcare, and government, where protecting sensitive information is critical. The results of this study reaffirm the role of the Cerberis3 system as a leading solution in the field of quantum-safe encryption technologies.
# Finite Element Modeling of the Soil-Nailing Process in Nailed-Soil Slopes
## 2 Laboratory Model of the Soil-Nailing Process
### Nailed-Soil Slope Component Properties  
The laboratory models of the soil-nailing process consist of a Perspex sheet box filled with sandy soil, a Perspex sheet facing, steel bars for reinforcement and a steel plate for foundation. A special density control apparatus was designed using a sand-raining system by using a sand deposition device and controlling load-cells system by using four load cells below the corner of the model in order to obtain homogeneous sand beds and controlled relative densities. The sieve analysis test was conducted to determine the gradation of sand. The shear strength parameters of the used sand were determined by means of direct shear apparatus. A direct tension test was performed to determine the strength characteristics of the nail used in this study. The properties of the Perspex sheet facing were determined in the laboratory. The properties of the sand used in the laboratory model are given in Table 1. The thickness of the Perspex plate was 5 mm, the diameter of the nails was 5 mm and thickness of the footing plate was 22 mm. The engineering properties of the Perspex plate, steel plate and steel nails are given in Tables 2 and 3.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Property** & **Value** & **Property** & **Value** \\ \hline Percentage of clay & 0.00 & Coefficient of uniformity (C\({}_{\mathrm{u}}\)) & 1.99 \\ Percentage of silt & 1.33 & Coefficient of gradation (C\({}_{\mathrm{c}}\)) & 1.00 \\ Percentage of fine sand & 39.17 & Specific gravity (G\({}_{\mathrm{s}}\)) & 2.62 \\ Percentage of medium sand & 58.63 & Minimum unit weight (\(\gamma_{\mathrm{min}}\)) (kN/m\({}^{3}\)) & 15.30 \\ Percentage of coarse sand & 0.87 & Maximum unit weight (\(\gamma_{\mathrm{max}}\)) (kN/m\({}^{3}\)) & 17.80 \\ Percentage of fine gravel & 0.00 & Void ratio, minimum (e\({}_{\mathrm{min}}\)) & 0.472 \\ Effective diameter, mm (D\({}_{\mathrm{10}}\)) & 0.126 & Void ratio, maximum (e\({}_{\mathrm{max}}\)) & 0.712 \\ \hline \end{tabular}
\end{table}
Table 1: Properties of the sand used in the soil-nailing process [36].  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Plate Element** & **Material** & **Width (mm)** & **Thickness** & **Young’s** & **Bending** & **Stiffness** & **Stiffness (Axial, EA)**, \\  & & **(mm)** & **(kPa)** & **(kN\(\cdot\)mm\({}^{2}\))** & **(kN)** \\ \hline Foundation & Steel & 150 & 22.0 & 21.2 \(\times\)\(10^{7}\) & 188,186.6 & 4665.78 \\ Facing Plate & Perspex & 140 & 5.0 & 4200 & 0.04375 & 0.021 \\ \hline \end{tabular}
\end{table}
Table 2: Facing and foundation material properties [36].
# Quasi-All-Passive Thermal Control System Design and On-Orbit Validation of Luojia 1-01 Satellite
## 4 Thermal Analysis  
### Finite Element Modeling  
The finite element method is widely used in thermal analysis [11]. The finite element model is constructed in I-DEAS/TMG software shown in Figure 6. Since most of the satellite structures are in the form of a thin plate, the finite element model is majorly comprised of shell elements which can accelerate the computational efficiency. There are 3200 elements in total and the thickness of the elements is determined by equivalent structure thickness. In the process of finite element modeling,  
\begin{table}
\begin{tabular}{c l l l} \hline \hline
**No.** & **Subject** & **Location** & **Temperature** & **Nominal Power** \\  & & & **Threshold (\({}^{\circ}\)C)** & **Consumption (W)** \\ \hline
1 & Heating region of the camera & Tube of the camera & 11.5–12.5 & 2 \\
2 & Backup for the heating region of camera & Tube of the camera & 9.5–10.5 & 2 \\
3 & Heating region of the battery & Shell of the battery & 18.0–20.0 & 2 \\
4 & Backup for the heating region of the battery & Shell of the battery & 15.0–17.0 & 2 \\  & & & Sum & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Parameters of the heating regions of the Luojia 1-01 satellite.  
Figure 5: Radiating regions of the Luojia 1-01 satellite. (**a**) Radiating region of the +Y panel. (**b**) Radiating region of the \(-\)Y panel.  
thermal coupling method is introduced in order to simplify the calculation and there are 55 thermal coupling in total.
###### Abstract  
Seed inoculation of forage crops by arbuscular mycorrhizal fungi (AMF) generally results in higher profitability, but also modifies the chemical composition of silage in terms of increased biomass, protein, and dry matter. Raw milk aroma is affected by the type of feed. This work investigated the influence of ensiled forage obtained by seed inoculation with AMF on the volatile fractions and fatty acid composition of milk. Two experiments were carried out: in the first, buffaloes were fed maize silage, and in the second, cows were fed sorghum silage. The volatile fractions of milk were quantified by headspace solid-phase micro-extraction (HS-SPME), combined with gas chromatography-mass spectrometry (GC-MS), and fatty acids by gas chromatography (GC). The ensiled forage obtained with AMF increased saturated fatty acids (SFAs), and decreased monounsaturated fatty acids (MUFAs) and polyunsaturated fatty acids (PUFAs) in both experiments. The volatile fraction in milk samples obtained from bovines fed mycorrhizal ensiled forage showed an increase of free fatty acids and ketones, responsible for cheesy and fruity odors. Aldehydes, responsible for green and grassy notes, increased only in the milk from buffaloes fed ensiled maize. Our results suggest that inoculation of maize and sorghum seed with AMF, combined with a low rate of fertilizers, leads to ensiled forage that could slightly affect the FA profile and odor quality of milk.  
Keywords:bovine feed; maize; sorghum; arbuscolar mycorrhizal fungi; volatile compounds; SPME-GC/MS +
Footnote †: journal: molecules
## 3 Results and Discussion
### Validation and Verification  
In Table 5, the real power plant parameters given in Ogriseck [16] are compared to the conducted simulations. The represented results display a good agreement between the present work and real data of the existing Kalina power plant with a deviation of less than 0.9%. The percentage of relative deviation between available data in the literature and obtained data from simulation in the present work is calculated as follows:  
\[\text{Deviation }(\%)=\frac{\left|X_{reference\ data}-X_{simulation\ data} \right|}{X_{reference\ data}}\times 100 \tag{27}\]  
The cooling system (absorption refrigeration system) is verified using available data reported in literature [43]. In the absorption system, the verification results are obtained with some main design parameters such as absorber, condenser and generator temperatures and ammonia concentration. Figure 3 represents the data obtained in the present work and the data by [PERSON] [43] for the absorption refrigeration ammonia-water cycle. The represented results display a good agreement between the present work and literature with a deviation of less than 1%.  
Figure 3: Verification results for absorption refrigeration cycle for the present work and with previously published data by [43].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Point**} & \multirow{2}{*}{**Temperature** (K)} & \multirow{2}{*}{**Pressure** (kPa)} & \multicolumn{3}{c}{**Ammonia Concentration**} \\  & & & & \(\left(\mathbf{kg}\frac{\mathbf{NH_{3}}}{\mathbf{kg}}\mathbf{solution}\right)\) & \\ \hline  & (a) & (b) & (a) & (b) & (a) & (b) \\
1 & 281.15 & 281.15 & 4.6 & 4.6 & 0.82 & 0.82 \\
2 & 281.20 & 281.15 & 35.3 & 35.3 & 0.82 & 0.82 \\
3 & 314.26 & 314.15 & 34.3 & 34.3 & 0.82 & 0.82 \\
4 & 336.10 & 336.15 & 33.3 & 33.3 & 0.82 & 0.82 \\
5 & 389.15 & 389.15 & 32.3 & 32.3 & 0.82 & 0.82 \\
6 & 389.15 & 389.15 & 32.3 & 32.3 & 0.973 & 0.97 \\
7 & 389.15 & 389.15 & 32.3 & 32.3 & 0.498 & 0.5 \\
8 & 316.21 & 316.15 & 6.6 & 6.6 & 0.973 & 0.97 \\
9 & 319.11 & 319.15 & 31.3 & 31.3 & 0.498 & 0.5 \\
10 & 319.18 & 319.15 & 6.6 & 6.6 & 0.82 & 0.82 \\
11 & 303.16 & 303.15 & 5.6 & 5.6 & 0.82 & 0.82 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Validation results of the power ammonia-water Kalina cycle operating conditions gained from present work (a) with reported data for the existing power plant in Hüsavik [16] (b).
# Microbiome-Based Interventions for Food Safety and Environmental Health
## 4 Current Applications for Microbiome-Based Interventions  
Microbiome-based interventions have emerged as an important tool in food safety, environmental health, and treatment of infectious diseases, due to their positive effects on the immune system, overall human health, and the environment. Microbiome-based therapeutics find application in probiotics, prebiotics, and synbiotics, especially in ensuring improved gut health and food safety. They also find applications in fecal microbiota transplantation (FMT) and phage therapy for the treatment of diseases.
# Identification of Impact Frequency for Down-the-Hole Drills Using Motor Current Signature Analysis
## 4 Discussion
Considering that the impact frequency is related to drilling efficiency variables such us RoP, bit wearing, and rock properties of drilled formations, further works will also integrate the MCSA-based impact frequency identification into Measurement-While-Drilling (MWD) systems designed to monitor the drilling process in nearly real-time, providing complementary and valuable information to characterize the rock mass during the drilling operation.
## 4 Results  
The interaction system was subjectively evaluated (Table 1). The subjective evaluation results are visualized in Figure 4. The results show that, on average, the evaluations are at the upper end of the scale. Those participants who were selected to act as \"radiologist\" (who also happened to have the most planning experience) and one of the \"surgeons\" gave consistently high evaluations, while the other \"surgeon\" participant gave lower evaluations.  
The participants gave a few general comments. One comment was about the clarity of the user interface and about the practice session, suggesting improvements. The user interface itself was fully functional, but it was found to be complicated to adopt during the relatively short experiment. The same issue was mentioned also in informal discussions with other participants after the experiments. In another comment it was proposed that the user should be able to scale the skull model. In the current implementation, the skull model was of fixed size and enabling scaling would make it possible to study details more easily.  
Figure 4: The distribution of subjective evaluation values for each attribute (Table 1). The values selected by “radiologist” participants are marked by an “x” and the values selected by “surgeon” participants are marked by a dot. The “radiologist” participants and one of the “surgeon” participants consistently gave high evaluations for all attributes.
# Seismic Performance of Corroded Reinforced Concrete Columns Strengthened with Basalt Fiber Sheets
## 1 Introduction  
The increase in corrosive substances in the atmospheric environment and the pollution of the marine environment causes the corrosion of buildings, bridges, dams, etc. The research on the seismic performance of corroded structures is helpful for the evaluation and reinforcement of corroded reinforced concrete structures, so as to avoid the collapse of corroded structures during earthquake disasters [1, 2, 3, 4].  
Relevant experimental research mainly focusses on the quantitative influence of the seismic performance of corroded reinforced concrete columns (CRCC). [PERSON] [5, 6] and [PERSON] [7, 8] conducted two batches of tests on the reinforced concrete specimens to analyze the effect of different corrosion rates and axial compression ratios on the seismic performance of CRCC. [PERSON] [9, 10] carried out research on the \"+\" shaped reinforced concrete column with an axial compression ratio of 0.23 to analyze the effect of corrosion rate on the seismic performance of the CRCC. [PERSON] [11, 12] studied the effect of corrosion rate on the seismic performance of CRCC under artificial climate corrosion. The above studies have not achieved relatively consistent results.  
Scholars have carried out multiple studies on the seismic reinforcement of reinforced concrete columns, mainly using enlarged-section reinforcement, steel-frame reinforcement, bonded-steel reinforcement and fiber-sheet reinforcement. However, these studies mainly focus on the uncorroded columns, and the material mainly concentrated on is carbon fiber, while the reinforcement performance of the component after corrosion has rarely been studied. [PERSON] [13, 14, 15, 16] conducted experimental research on the seismic performance of corroded concrete square columns and circular columns reinforced with CFRP. [PERSON] [17, 18] studied the seismic performance of corroded concrete columns reinforced with hybrid fibers by considering the effects of axial compression ratio and stirrup reinforcement ratio. Carbon fiber has good physical and mechanical properties, but it depends on imports andis expensive. The price of basalt fiber is between 1/10 and 1/20 of carbon fiber, which has an absolute price advantage with high strength and good durability. However, there are few studies concerning the basalt fiber with better comprehensive performance.  
In order to study the seismic performance of corroded concrete columns reinforced with basalt fiber sheets, the low-cycle repeated test of four reinforced concrete columns was completed. The failure process and form of concrete columns under working conditions of no corrosion, corrosion and corrosion reinforcement were described. The effect of the layers of reinforced basalt fibers on the seismic performance indexes, such as hysteretic performance, bearing capacity, stiffness and ductility of the reinforced concrete columns, was compared and analyzed.
# Bushfire Disaster Monitoring System Using Low Power Wide Area Networks (LPWAN)
## 4 Application
To calculate the total velocity at a location, both the vector sum of horizontal and vertical components of velocity should be used  
\[\overrightarrow{v}=\overrightarrow{v}_{x}+\overrightarrow{v}_{y} \tag{11}\]  
Distances \(d_{x}\) and \(d_{y}\) can be thought of as distances by which a selected isovalue contour has shifted along a horizontal or vertical direction per measured time interval. The total velocity direction  
Figure 5: An example contour plot of the temperature distribution of the sensor network at time \(T_{2}\). The locations of sensors are overlaid on the plot. Direction of disaster propagation is shown with arrows at selected sensors. should coincide with the direction of disaster propagation, which is perpendicular to the isovalue contour at each sensor location. Figures 4 and 5 show the directions of disaster propagation for a few sensors. The spread of the disaster into a new location can be predicted by calculating the distance the fire will progress along each horizontal and vertical direction  
Figure 4:0 Figure 4:1  
where \((x_{0},y_{0})\) are coordinates of the initial fire location and \((x,y_{0})\) to \((x_{0},y)\) are the fire spread locations. Factors such as wind speed and direction will influence the bush fire behaviour, and an example of the effect of wind on fire propagation has been shown in Figure 4:2 A westerly wind direction (right to left) results in a faster velocity of propagation in the horizontal direction, as compared to Figure 5, which shows fire propagation without the wind variable. Figure 4:3  
where \(v_{yw}\) is fire velocity with wind, \(v_{y}\) is the original fire velocity and \(k\) is the coefficient of proportionality reflecting the contribution of wind velocity to the original velocity of fire propagation. The result is a change in both the magnitude and direction of the velocity, which is reflected by a distortion of the propagation front as compared to without the wind variable. Both the direction for cases with the wind and without have been shown in the figure to demonstrate the effect of rotation of all propagation fronts. However, there is no need to change the calculation approach. The same formulas can be used to calculate the fire propagation velocity. As the data are taken from real time sensor measurements, the effect of wind and other factors are already reflected in the parameter values \(P_{N}\) used for calculations.