esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions  
The results of the measurement details summarized in the previous section are listed in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Code** & **d (mm)** & **x (\%)** & **\(\rho\) (g/cm\({}^{3}\))** & **\(\Phi\) (\%)** & **\(k\) (W/mK)** & **\(f_{cs}\) (MPa)** & **WAR (\%)** \\ \hline
1 & 2–4 & 0 & 1.608 & 3.42 & 0.452 & 25.82 & 22.5 \\
2 & 2–4 & 10 & 1.395 & 10.56 & 0.381 & 23.54 & 23.0 \\
3 & 2–4 & 20 & 1.175 & 14.82 & 0.319 & 21.77 & 23.8 \\
4 & 2–4 & 30 & 0.958 & 18.66 & 0.275 & 19.45 & 24.3 \\
5 & 2–4 & 40 & 0.820 & 22.45 & 0,241 & 17.17 & 25.1 \\
6 & 2–4 & 50 & 0.731 & 26.14 & 0.220 & 15.64 & 25.6 \\
7 & 4–6 & 0 & 1.608 & 3.42 & 0.452 & 25.82 & 24.4 \\
8 & 4–6 & 10 & 1.319 & 11.38 & 0.364 & 22.68 & 25.0 \\
9 & 4–6 & 20 & 1.124 & 16.62 & 0.305 & 19.46 & 25.6 \\
10 & 4–6 & 30 & 0.892 & 21.13 & 0.258 & 17.74 & 26.3 \\
11 & 4–6 & 40 & 0.757 & 24.88 & 0.230 & 15.61 & 27.1 \\
12 & 4–6 & 50 & 0.685 & 28.10 & 0.207 & 14.55 & 27.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Thermal and mechanical properties of the samples.
## 2 Proposed Framework and Experimental Methodology
### Proposed Encryption Workflow with Hybrid CPU-GPU Strategies
#### 2.5.5 Bit-Slicing Parallelization  
As shown in Algorithm 5, bit-slicing parallelization breaks data into slices at the bit level, allowing simultaneous AES operations in the GPU. The CPU prepares the slices, and the GPU processes them concurrently, achieving a speedup approximated by \(O(N/k)\), where k is the number of bit slices processed in parallel. This method significantly enhances parallel processing efficiency. Additionally, parallel implementations such as Galois/Counter Mode (GCM) on GPUs demonstrate significant throughput enhancements, further validating the efficacy of bit-slicing parallelization techniques for accelerating AES encryption on modern GPUs [24].  
```
This algorithm divides data into bit-level slices, enabling GPU threads to encrypt multiple data slices simultaneously, significantly increasing parallel processing performance.
1. CPU prepares \(k\)-bit slices of data \(D\)
2. GPU runs parallel AES operations on each bit slice
3. Combine all encrypted slices into final output
```  
**Algorithm 5**Bit-slicing parallelization in GPU
## 5 Conclusions  
To solve the ship dynamic positioning control problem that involves external disturbances, model uncertainties, and thruster failures, a fixed-time adaptive fault-tolerant control technique is investigated in this study. Based on the fixed-time control theory and sliding mode control, a fixed-time sliding mode adaptive fault-tolerant controller is proposed, which can make the ship's dynamic positioning error converge within a fixed time even if there is a thruster fault. By introducing the IAE and ITAE performance indicators, it becomes evident that the control system suggested in this article allows for faster convergence of the dynamic positioning error to the equilibrium point. Additionally, it enables an instantaneous reaction and reduces the magnitude of error overestimation. Then, a comprehensive stability analysis is performed to ensure that all the signals in the closed-loop system are fixed-time stable. Finally, simulation experiments and comparative analysis prove the effectiveness and superiority of the proposed control scheme. In further study, we will try to increase the fault-tolerant performance and robustness of multi-USV navigation by investigating fixed-time formation tracking control with actuator faults.  
Conceptualization, Y.Z.; Methodology, Y.Z.; Software, Y.Z.; Validation, Y.Z.; Formal analysis, Y.Z.; Investigation, Y.Z.; Resources, Y.Z.; Data curation, B.S.; Writing--original draft, Y.Z.; Writing--review & editing, B.S.; Visualization, B.S.; Supervision, J.Z.; Project administration, J.Z.; Funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Figure 8: Curves of thruster control input in Case 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \(\mathbf{ITAE_{x}}\) & \(\mathbf{ITAE_{y}}\) & \(\mathbf{ITAE_{\psi}}\) & \(\mathbf{ITAE_{u}}\) & \(\mathbf{ITAE_{p}}\) & \(\mathbf{ITAE_{r}}\) \\ \hline FT-AFTC & 3.451 & 4.995 & 0.108 & 1.367 & 8.786 & 0.232 \\ ABSMC & 13.722 & 13.839 & 10.846 & 15.490 & 18.413 & 16.242 \\ NFTC & 77.11 & 96.41 & 27.273 & 20.823 & 42.547 & 12.797 \\ \hline \hline \end{tabular}
\end{table}
Table 5: ITAE of the three controllers in Case 2.
## 2 Data Model
### Dictionary Representation  
In order to differentiate between instruments in a music recording, we make the simplifying assumption that the tones for each instrument \(\eta=1,\dots,N_{\mathrm{ins}}\) (where \(N_{\mathrm{ins}}\) is the total number of instruments) follow a characteristic pattern, namely that we can express the amplitudes of the harmonics as:  
\[a_{h}=a\,D[h,\eta]\,e^{i\phi_{h}},\qquad h=1,\dots,N_{\mathrm{har}}, \tag{10}\]  
where \(a\geq 0\) is the _global amplitude_ of the tone, \(D\in[0,1]^{N_{\mathrm{har}}\times N_{\mathrm{ins}}}\) is the _dictionary_ containing the _relative amplitudes_ for the harmonics, and \(\bar{\phi}_{h}\in[-\pi,\pi)\) is the phase angle for the respective harmonic.  
In a realistic music recording, the tones of the different instruments overlap and their parameters change over time. Thus, to construct an appropriate time-frequency model, we must equip the parameters with indices relating to the tones \(j=1,\dots,m\) (where \(m\in\mathbb{N}\) is the total number of simultaneously played tones) and the time frames \(k=1,\dots,n_{\mathrm{len}}\). We define the tone-wise and global model spectrograms, respectively, as:  
\[z_{j}[k,l] =\sum_{h}a_{j,h,k}\exp\left(-\frac{(\beta l-f_{j,h,k})^{2}}{2 \sigma_{j,k}^{2}}+i2\pi f_{j,h,k}\alpha k\right) \tag{11a}\] \[=\sum_{h}a_{j,k}\,D[h,\eta_{j,k}]\exp\left(-\frac{(\beta l-f_{j,h,k})^{2}}{2\sigma_{j,k}^{2}}+i\varphi_{j,h,k}\right)\] incorporating the phase shift via \[\varphi_{j,h,k}:=\bar{\varphi}_{j,h,k}+2\pi f_{j,h,k}\alpha k,\] and: \[z[k,l]=\sum_{j}z_{j}[k,l]. \tag{11b}\]  
For signals consisting only of sinusoids, the spectrum is modeled precisely by (8), where the standard deviation \(\sigma_{j,k}\) of the Gaussians is given as \(\sigma_{j,k}=1/(2\pi\zeta)\). However, especially at the beginning and end of a tone, boundary effects can occur, leading to the spectrum being better approximated by different values for \(\sigma_{j,k}\). Therefore we include those as free parameters.
# Biosurfactants' Potential Role in Combating COVID-19 and Similar Future Microbial Threats
## 2 Approaches That Can Use Biosurfactants in Outbreak Prevention and Management
### Environmental Approaches  
#### 2.3.1 Environmental Control and Management against Potential Outbreaks  
Wastewater remains a significant transmission route of pathogens that have an outbreak or pandemic potential. This poses a significant challenge, especially for health authorities when it comes to testing and understanding the transmission dynamics of pathogens. In the case of the current COVID-19 pandemic, the role of wastewater treatment in the transmission of the virus remains unknown [69]. Detecting pathogen DNA or RNA in wastewater samples can provide an early warning system in the transmission of the pathogen in the community. Therefore, early warning systems in wastewater treatment facilities using optimized protocols for sampling, sample storage and recommended concentrations of genetic material can be an important tool for the early detection, response, and management of an outbreak [70]. Biosurfactants have been shown to have broad-spectrum antimicrobial activity, therefore their applications in the treatment of wastewater can serve as an early and safe method for managing sewage in sewage treatment facilities [71].  
Wastewater pollution can be a cause of thalassogenic infectious diseases when poorly treated wastewater is disposed into the sea. The WHO estimates that, globally, 120 million cases of gastrointestinal diseases and about \(5\times 10^{7}\) cases of severe respiratory diseases are caused by swimming in wastewater polluted waters [72]. _Bacillus amyloidosisefaciens_ ST34 and _Pseudomonas aeruginosa_ ST5 are biosurfactant-producing bacteria that have been isolated from wastewaters, producing surfactin and rhamnids with antimicrobial activity and a broad spectrum of other pathogens including drug-resistant _Staphylococcus aureus_, _Escherichia coli_ strains and _Candida albicans_[73]. This indicates that they could have an important role in the initial biological treatment stages of wastewater. Poorly treated sewage from hospitals and quarantine facilities can be a potential source of the spread of pathogens. Wastewater treatment was shown to be enhanced when a lipopeptide was applied to a lignocellulosic biocomposite. This resulted in a boost of the adsorption characteristics of the biocomposite through an increase in its stability, roughness, sharpness, and roundness [71]. By trapping compounds such as heavy metals and xenobiotics, and reducing the BOD in water treatment process, this contributes to a decrease in microbial pathogen populations. Various combinations of biosurfactant-producing bacteria have been isolated from wastewater. A combination of biosurfactant-producing bacteria with very broad spectrum activity could be considered as additional treatment options in wastewater facilities.
# Link Load Correlation-Based Blocking Performance Analysis for Tree-Type Data Center Networks
###### Abstract
The authors in [10] gave the theoretical blocking performance of switches at different layers in optical data center networks, according to the Markov chain model. However, their analysis focused on the blocking at each switch's ports rather than the blocking of a flow along the whole path. In summary, there have been few works focused on the theoretical blocking performance of DCNs, and the conventional EFPA analysis method results in a relatively large approximation error, as the characteristics of DCNs are not taken into consideration. In this paper, a modified EFPA method is proposed for blocking performance analysis in tree-type DCNs, based on the traffic load correlation between edge links and aggregation links. The theoretical accuracy is verified by simulation. The proposed theoretical analysis method may act as the guideline for DCN configuration and traffic engineering. The remainder of this paper is organized as follows. In Section 2, we introduce the system model. In Section 3, the proposed analysis method is introduced. The simulation results are presented in Section 4, and Section 5 concludes the paper.
# Point-Sim: A Lightweight Network for 3D Point Cloud Classification
## 3 Methods
### Trigonometric Functions Embedding  
Referring to positional encoding in the transformer [22], for a point in the input point cloud, we use trigonometric functions to embed it into a C-dimensional vector:  
\[\text{Trigonometric}(p_{i})=\text{Concat}(f_{i}^{x},f_{i}^{y},f_{i}^{z})\in \mathbb{R}^{1\times C_{i}} \tag{10}\]  
where \(f_{i}^{x},f_{i}^{y},f_{i}^{z}\in\mathbb{R}^{1\times\frac{C_{i}}{3}}\) denote the embeddings of three axes, and \(C_{i}\) represents the initialized feature dimension. Taking \(f_{i}^{x}\) as an example, for channel index \(m\in[0,\frac{C_{i}}{6}]\), we have the following:  
\[\begin{split} f_{i}^{x}[2m]&=\sin(\alpha x_{i}/ \beta^{\frac{\text{det}}{\alpha_{i}}}),\\ f_{i}^{x}[2m+1]&=\cos(\alpha x_{i}/\beta^{\frac{ \text{det}}{\alpha_{i}}})\end{split} \tag{11}\]  
where \(\alpha\) and \(\beta\) respectively control the magnitude and wavelength. Due to the inherent properties of trigonometric functions, the transformed vectors can effectively encode the relative positional information between different points and capture fine-grained structural changes in the three-dimensional shape.
## 3 Theory of Proposed Method
### Location Process of Proposed Method  
The whole procedures of PCFWE are shown in Figure 3 as below:  
1. Establish linear equations for the source location.
2. Set index \(I\) = 0 and \(w_{i}^{(0)}=1\) for the primary iteration and obtain the initial location result \(\theta^{(0)}\).
3. Calculate the residuals \(\Delta^{(0)}=A\theta^{(0)}-b\), which are used to generate the preliminary weights \(w_{i}^{(1)}\) according to Equation (7).
4. Conduct the next iteration, \(I\) = 1, and solve the weighted least squares solution \(\theta^{(1)}\) from Equation (14).
5. Update the weights \(w_{i}^{(2)}\) according to the residuals \(\Delta^{(1)}\).
6. Use the new weights in the next location, where \(I\) = 2 and \(\theta^{(2)}\) is calculated.
7. Repeat the above calculations (steps 4-6) to optimize the weights and filter the outliers, until \(\sum\Bigl{|}\Delta^{(I)}-\Delta^{(I-1)}\Bigr{|}_{2}\leq\varepsilon\).
8. Reconstruct linear equations with the remaining measurements.
9. Precondition linear equations to lower the condition number and eliminate the ill condition.
10. Obtain the optimal closed-form solution by solving the preconditioned linear equations with Equation (18).
## 3 Emission Reduction Measures at Ports  
A seaport may consist of several terminals, which specialize in handling different types of freight commodities, e.g., containers, dry bulks, tanks, and Ro-Ro. Ports and terminals are equipped with various material handling equipment and facilities for loading/unloading, storing and maintaining the freights, and serving vessels, trucks, and trains. Port emission reduction solutions may be classified into the following six structured categories.  
1. Operational measures, which focus on improving operational efficiency in the port system to reduce fuel/energy consumption and better utilise energies;
2. Technical measures, which focus on improving energy efficiency of the vehicles and equipment through upgrading, retrofitting, or replacing existing machinery, vehicles, equipment, and engines, and adopting carbon capture systems (CCS);
3. Fuel and energy measures, which focus on adopting cleaner alternative fuels and energies such as biofuels, hydrogen, electricity, solar power, and wind energy;
4. Infrastructural measures, which focus on the changes of port infrastructure and facilities to support emission reductions, e.g., port/terminal redesign and expansion for modal shift by increasing the use of rail services and barge services, cold ironing to provide shore power to vessels, and installing new electrical infrastructure to support equipment electrification;
5. Digitalisation measures, which focus on the applications of digital technologies to automate processes, collate reliable data, and implement artificial intelligence to support operational and technical measures;
6. Policy and collaboration measures, which focus on relevant legal regulations, incentive programs, financial investment, insurance policies, and cross-function, cross-company, and cross-sector collaborations that would impose, accelerate, and facilitate the adoptions of the solutions in other categories.  
The solution measures in the first three categories are more fundamental and have a direct impact on emission reduction at ports, whereas the solution measures in the last three categories are more like enablers and facilitators that support the development and implementation of the solution measures in the first three categories. Nevertheless, some solution measures cut across different categories or may be classified into multiple categories. In the following, we provide a more detailed discussion on how these solution measures could be used to decarbonise the emission sources in each emission source category according to the geographical location: seaside, quayside and yardside, landslide interface, landslide industry, and support facility. It should be pointed out that there are some correlations among these geographical locations regarding emission reduction measures, e.g., some measures cut across two or more locations, so the effectiveness of the measures in one location may be affected by the measures in other locations.
# Gear Integrated Error Determination Using the Gaussian Template Convolution-Facet Method
## 2 Theory of Gear Integrated Error  
### Gear Integrated Error Curve  
The integrated error curve for gears serves as a comprehensive compilation of both individual and composite error data, providing a more extensive set of information compared to individual gear errors alone. As illustrated in Figure 1, the blue curve depicts the model of the integrated error curve within the gear section. The abscissa for each point on the curve corresponds to the angular coordinate \(\varphi\) (unit: \({}^{\circ}\)) associated with the respective point on the tooth face, while the ordinate represents the integrated error value denoted as GIE (unit: um). Each segment of the curve signifies the integrated error of the left or right flank of each tooth.  
The determination of pitch deviation, profile deviation, and other error parameters outlined in the international standard for gear accuracy is executed within a specific section of the tooth trace. Simultaneously, the calculation of the gear integrated error curve is conducted within the same tooth trace section. Consequently, the gear integrated error  
Figure 1: Model of integrated error curve in gear section.  
curve discussed in this article pertains specifically to the integrated error curve within the gear section, aligning with established gear accuracy standards.  
The symbol meanings of the error term in Figure 1 are as follows: \(f_{pt}\) represents the single-pitch deviation; \(F_{pk}\) represents the sector pitch deviation; \(F_{p}\) represents the cumulative pitch deviation; \(f_{i}^{\prime}\) represents the tooth-to-tooth tangential composite deviation; \(F_{i}^{\prime}\) represents the total tangential composite deviation; and \(F_{\kappa}\) represents the total profile deviation. According to the integrated error curve, the profile form deviation, \(f_{fa}\), and profile slope deviation, \(f_{Ha}\), can be further obtained; \(f_{pht}\) represents the base pitch deviation. The detailed definitions of these error items are given in the ISO-1328.1:2013 [14] and ISO/TR 10064-2:1996 [15] standards and will not be elaborated here.  
Superimposing the gear integrated error curves of the left and right flanks on the same coordinate system can obtain a bidirectional integrated error curve in gear section (SJZ curve). Figure 2 is a segment on the SJZ curve, the pair of deviation values: \(C_{L}\) and \(C_{R}\) (or \(C_{L}^{\prime}\) and \(C_{R}^{\prime}\)) representing the fixed chord position of the tooth, which should be located on the same angular coordinate \(\varphi\). The vertical coordinate distance between \(C_{L}/C_{R}\) and \(C_{L}^{\prime}/C_{R}^{\prime}\) is \(L_{C}\) or \(L_{C}^{\prime}\), and its maximum variation is radial runout, defined as following:  
\[F_{r}=\max(L_{C},L_{C}^{\prime}) \tag{1}\]
# Switchable Broadband-to-Tunable Narrowband Magnetic Probe for Near-Field Measurements
###### Abstract
This probe used a varactor diode to switch the frequency bands among the GSM900, UMTS, and GPS applications to improve the sensitivity by 7-9 dB compared to the sensitivity results of a broadband magnetic probe of equivalent size. The authors used Advance Design System (ADS) software to model the equivalent circuit of this probe. Furthermore, the measurement results showed good agreement with the simulation results from the ADS software. This work presents further advantages than what has been reported in the previous literature reviewed by designing, modeling, and fabricating a switchable broadband-to-multi-narrowband magnetic probe. Switching is achieved using a PIN diode for broadband-to-single-band transition. Simultaneously, the varactor diode is utilized to tune the obtained single band to the most influential bands like WiMAX (3.5 GHz), mid-bandwidth (3.6-3.8) GHz for 5G applications, PAN (4.8 GHz), and WLAN (5.2 GHz). This article is organized as follows. Section 2: Describes the design, modeling, and analysis of a switchable magnetic probe. The basing circuit for this probe is illustrated in Section 3. Section 4: Briefly discusses the design and modeling of the equivalent circuit in AWR Software. Section 5:Demonstrates the isolation and sensitivity results compared with the literature reviewed. Finally, Section 6: Presents a brief conclusion.
## 3 Results and Discussion
### Internal Tunnel Overpressure  
Pressure sensors were set at 2, 30, 60 and 90 cm in the tunnel to measure internal overpressure. The distance between the portal and the pressure sensor location L, divided by the diameter of the tunnel D (30 cm) to be dimensionless, resulted in L/D = 0.067, 1, 2 and 3, respectively. Figure 7 shows a comparison of the blast pressures measured at the portal and the internal sections of the tunnel with different amounts of C4 charges.When the shock wave propagated between the portal section and the L/D = 1 section, the blast pressure was attenuated, but it increased as the shock wave moved towards the L/D = 2 section, which might be due to reflection waves and superposition by blast waves generated on the inner walls causing the blast pressure measured at that section to be greater than the others. Finally, the blast pressure was attenuated as the shock wave moved towards the L/D = 3 section.  
For comparing the relation between peak pressures measured in the tunnel and portal section, the peak pressures measured by sensors were normalized, and the portal section was set at 1 (\(\mathrm{P^{\prime}_{\mathrm{so}}=P_{internal}/P_{\mathrm{protal}}}\)). The peak pressures of sections L/D = 1, 2 and 3 were compared with the portal section, as shown in Figure 8. The cubic polynomial regression equation ofmaximum coefficient of determination (R-squared value of 0.91) was used as the best regression equation for internal overpressure ratio, as shown in Equation (9).  
\[\mathrm{P^{\prime}_{\mathrm{so}}=-0.395\bigg{(}\frac{L}{D}\bigg{)}^{3}+1.67 \bigg{(}\frac{L}{D}\bigg{)}^{2}-1.63\bigg{(}\frac{L}{D}\bigg{)}+1.1 \tag{9}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\mathbf{Z}\) & \(\mathbf{P_{\mathrm{so}}}\) & \(\mathbf{P_{\mathrm{portal}}}\) & \multicolumn{2}{c}{**Difference *1**} & \multicolumn{1}{c}{**Ratio *2**} \\ \hline \(\mathbf{(m/kg^{1/3})}\) & \(\mathbf{(kPa)}\) & \(\mathbf{(kPa)}\) & & & \\ \hline
3.26 & 57.97 & 71.66 & \(-\)19.10 & 1.24 \\
2.59 & 92.81 & 107.84 & \(-\)13.94 & 1.16 \\
2.40 & 106.97 & 117.64 & \(-\)9.07 & 1.10 \\
2.26 & 113.37 & 135.97 & \(-\)18.09 & 1.22 \\
2.15 & 143.61 & 162.04 & \(-\)11.37 & 1.13 \\ \hline \hline \end{tabular}  
*1: (\(\mathrm{P_{\mathrm{so}}-P_{\mathrm{portal}}}\))/\(\mathrm{P_{\mathrm{portal}}}\times 100\). *2: \(\mathrm{P_{\mathrm{portal}}/P_{\mathrm{so}}}\).  
\end{table}
Table 4: Overpressure comparison between \(\mathrm{P_{\mathrm{so}}}\) and \(\mathrm{P_{\mathrm{portal}}}\).  
Figure 7: Internal overpressure vs. L/D.