esa-sceva/satcom-chunk-collection huggingface dataset:
# Transmission Line Fault Classification Using Conformer Convolution-Augmented Transformer Model
## 3 Simulation Results and Discussion
### Issue of Multiple Input Sampling Rate  
As indicated in Table 8, the accuracy of Model III decreases when it is tested with data sampled at different rates, particularly for data with sampling rates that differ significantly from the training sampling rate. The lowest validation accuracy, observed at a data sampling rate of 188.5 samples per cycle, is 47.37%. This indicates that the model's performance is adversely affected when dealing with data at extreme sampling rates not encountered during its training phase. The impact of the sampling rate should be carefully considered when deploying the model in real-world scenarios. Matching or closely aligning the input data with the training sampling rate is crucial to preserve the accuracy and reliability of the model's fault classification capabilities.
## 2 Materials and Methods
### Data
#### 2.2.2 Discharge and Sediment Data
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dam**} & \multirow{2}{*}{**Name**} & \multirow{2}{*}{**Station**} & \multicolumn{2}{c}{**Coordinates**} & \multicolumn{2}{c}{**Elevation**} & \multicolumn{2}{c}{**Period (years)**} & \multicolumn{1}{c}{**Downstream**} \\  & & & **ID** & **Long** & **Lat** & **(msl)** & **Pre-Dam** & **Post-Dam** & **Distance (km)** \\ \hline \multirow{8}{*}{**Courage**} & E1 Toro & b1 & \(-\)76.13 & 8.11 & 100 & 1991–1998 & 2002–2010 & 26 \\  & Carrizola & b2 & \(-\)76.10 & 8.16 & 55 & 1993–1998 & 2002–2020 & 40 \\  & Nueva Colombia & b3 & \(-\)75.98 & 8.50 & 20 & 1991–1998 & 2002–2020 & 112 \\  & Monteria & b4 & \(-\)75.89 & 8.75 & 15 & 1963–1998 & 2002–2020 & 162 \\  & Sabana Nueva & b5 & \(-\)75.85 & 9.03 & 9 & 1979–1998 & 2002–2020 & 205 \\  & Palma Central & b6 & \(-\)75.82 & 9.20 & 6 & 191–1998 & 2002–2010 & 230 \\ \hline \multirow{8}{*}{**Courage**} & La Balsa & c1 & \(-\)76.60 & 3.09 & 987 & 1976–1983 & 1987–2020 & 27 \\  & Tablanca & c2 & \(-\)76.57 & 3.12 & 980 & 1978–1983 & 1987–2010 & 36 \\  & La Bolsa & c3 & \(-\)76.50 & 3.20 & 964 & 1967–1983 & 1987–2016 & 75 \\ \hline \multirow{8}{*}{**Courage**} & Puente Santander & d1 & \(-\)75.31 & 2.94 & 431 & 1960–1984 & 1988–2019 & 42 \\  & Angostura & d2 & \(-\)75.12 & 3.44 & 345 & 1975–1984 & 1988–2020 & 119 \\ \cline{1-1}  & Purificación & d3 & \(-\)74.94 & 3.85 & 306 & 1960–1984 & 1988–2019 & 182 \\ \cline{1-1}  & Narino & d4 & \(-\)74.84 & 4.39 & 277 & 1978–1984 & 1988–2019 & 273 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Gauging stations selected for the study. distance downstream of the dams at which the suspended sediment load was almost fully recovered.
# A Discrete Particle Swarm Optimization Algorithm for Dynamic Scheduling of Transmission Tasks
## 1 Introduction
### Contributions  
The following problems need to be solved when applying a DPSO to the DSTT:  
1. It is necessary to establish a mathematical model suitable for the DPSO of the DSTT;
2. It is necessary to solve the DPSO's mathematical-description problems of particle position, direction, and velocity.
3. It is necessary to design specific particle-update methods.  
Based on the above problems, this paper conducts research on the DSTT, and improves the DPSO. The contributions of this paper are mainly reflected in the following aspects:  
1. We build a mathematical model of the DSTT, including a task model, an evaluation model, an evaluation function, and an output of the Schedule--the mathematical model of the transmission plan--and propose a one-dimensional code to describe the Schedule, making it suitable for the calculation of the DPSO;
2. We propose a DPSO to define the particle position, particle-update direction and target definition, and solve the mathematical description of the DPSO for specific problems;
3. Based on the basic idea of the DPSO, and taking into account inertia retention, particle best, and global best, we use the probability-selection model to realize the particle update, and we propose random perturbation to improve the diversity of the particle population.  
In Section 2, we build a mathematical model of the DSTT suitable for intelligent-algorithm processing. In Section 3, we outline the specific operation and implementation method of the DPSO used in this paper and test the DPSO's optimal parameter combination. In Section 4, the experimental results are presented, and the results are analyzed and discussed. In Section 5, we make a conclusion and point out future avenues of work.
## 7 Proposed Framework
### Framework for Application Area 3: Farmers Should Be Paid More Fairly
#### 7.3.3 Advantages over Traditional Payment Systems  
There are many benefits that blockchain technology offers over conventional payment methods.  
* _Transparency:_ The blockchain is a public ledger that contains records of every transaction. This facilitates the tracking of money and product transfers across the supply chain.
* _Security:_ The blockchain technology is quite safe. Since all of the data are encrypted and kept in several places, they are incredibly impossible to hack or alter.
* _Efficiency:_ Many of the laborious processes required in conventional payment methods may be automated by the blockchain technology. All parties concerned may save money and time by doing this.
* _Fairness:_ All participants to a transaction are guaranteed to be treated equally by the smart contracts. The contracts are automatically executed as soon as the predetermined criteria are satisfied and cannot be altered or interfered with.  
Agricultural cooperatives may help guarantee that farmers get a fair price for their produce by using blockchain technology. The agricultural supply chain's efficiency and transparency may both be enhanced by blockchain technology.
# Simulation Framework for Detection and Localization in Integrated Sensing and Communication Systems
## 5 Evaluation and Abstraction
### Target Detection Abstraction  
Target detection abstraction is evaluated using false alarm probability \((P_{FA})\) and the detection probability \((P_{D})\), as detailed in Section 2.  
A set of thresholds has been defined by selecting threshold values \(\Delta_{\xi}\) between 0 dB and 3 dB. The Boolean variable \"withTarget\" is stored to analyze \(P_{FA}\) and \(P_{D}\) later for each simulation seed. Figure 5 shows the curves of the false alarm probability versus the selected threshold factor for two examples corresponding to extreme cases. First, the case where the SNR is \(-20\) dB and the algorithm uses only 2 OFDM symbols and, second, the case where the SNR is 20 dB, and the algorithm uses 1024 OFDM symbols. These curves show a notable decay in the false alarm probability as the threshold factor \(\Delta_{\xi}\) increases.  
Figure 4: Approximated CDFs of the distance and positioning errors under target-dominated conditions for 2–1024 OFDM symbols applied for all SNR levels defined in Table 1. Here, the target is positioned at \(d=64\) m.  
To examine the effect of \(\Delta_{\xi}\) on the target detection probability, Figure 6 depicts this probability against various selected factors \(\Delta_{\xi}\) ranging from 0 to 9 dB. A correlation between the false alarm and target detection probabilities can be identified. For example, when \(\Delta_{\xi}=0\) dB, the probability of detection and false alarm reach 100%, meaning that the algorithm continuously detects targets even when the target is not present in the scenario. Nevertheless, If the threshold exceeds 3 dB, as shown by the light blue (\(\Delta_{\xi}=6\) dB) and red lines (\(\Delta_{\xi}=9\) dB), we are over-dimensioning the detection. In other words, we are being too strict, which likely increases the likelihood of incorrect detections.  
Conversely, we observe that with \(\Delta_{\xi}=3\) dB, the false alarm probability is close to 0%, according to Figure 5. This behavior yields a sufficiently reasonable detection probability concerning the SNR. In the case of over-dimensioning \(\Delta_{\xi}\), the detection capability of the algorithm decreases since the curve is shifted towards increasing SNR values.  
For the abstraction of the target detection algorithm, \(\Delta_{\xi}\) has been fixed at 3 dB, ensuring a null probability of false alarm. Figure 7 shows a clear relationship between the detection probability and the number of OFDM symbols for a given SNR. Doubling the number of OFDM symbols effectively achieves 3 dB improvement in SNR. Lookup tables illustrating  
Figure 5: False alarm vs. threshold factor, with the target at \(d=64\) m.  
Figure 6: Detection probability vs. SNR for 8 OFDM symbols and different values of the selected factor, with the target positioned at \(d=64\) m.  
the detection probability across various SNR values and OFDM symbol counts for these thresholds are available in [16].
## 5 Transmission Spectrum and Radial Displacement Field Study Analysis
### Analysis of Transmission Spectrum Results  
In order to verify the correctness of the calculated dispersion relation curves, the flexural wave and longitudinal transmission spectra of the \(1/2/3/8\)-period finite-cycle AREM and WREM were calculated for the r-direction excitation state, respectively, as shown in Figures 12 and 13.  
Figure 11: Schematic diagram of transmission spectrum calculation for finite-period AREM structures.  
Figure 12: (**a**) Flexural wave dispersion relation curve of AREM; (**b**) flexural wave transmission spectrum of AREM; (**c**) longitudinal wave dispersion relation curve of AREM; (**d**) longitudinal wave transmission spectrum of AREM.  
Figures 12 and 13a-d show the flexural wave dispersion relation curve, flexural wave transmission spectrum, longitudinal wave dispersion relation curve, and longitudinal wave transmission spectrum from the AREM structure and WREM structure, respectively. The green shaded part indicates the flexural wave attenuation region, and the blue shaded part indicates the longitudinal wave attenuation region. It can be observed that the position of the forbidden band in the dispersion curve corresponds to the attenuation region of the wave in the transmission spectrum. It can be observed from Figures 12 and 13 that the flexural wave attenuation of the eight-period AREM structure can reach a maximum of \(-280\) dB, and the longitudinal wave attenuation can reach a maximum of \(-180\) dB. The flexural wave attenuation of the eight-period WREM structure can reach a maximum of \(-130\) dB, and the longitudinal wave attenuation can reach a maximum of \(-110\) dB. It is worth noting that the attenuation of the second longitudinal BG under the one-cycle AREM shows Fano-like interference. With the gradual increase in the number of cycles, the BS mechanism in the BG is gradually presented, the attenuation band of the second longitudinal BG gradually shows a centrosymmetric tendency, and the attenuation performance varies smoothly with frequency, with the maximum attenuation appearing near the center frequency of the BG. Therefore, the flexural BG in the AREM has a stronger attenuation effect on the transmission of vibration energy. Compared with the WREM, the wave attenuation capacity is increased by more than \(110\%\). For the longitudinal BG, the total bandwidth of the AREM is increased by \(34\%\) compared to the WREM. The longitudinal BG in the AREM also has stronger attenuation of vibration energy transmission, and its wave attenuation capacity is improved by more than \(56\%\) compared to the WREM. And in the range of the longitudinal BG and flexural BG, the AREM only needs four to five cycles of finite-period structure to achieve the attenuation equivalent to the WREM eight cycles of finite structure. In summary, the AREM not only has wider bandwidth in both the flexural BG and longitudinal BG compared to the WREM, but also exhibits stronger wave attenuation characteristics than the WREM.
# Enhancing 3D Lung Infection Segmentation with 2D U-Shaped Deep Learning Variants
## 3 Material and Methods
### Segmentation with 2D Architectures
#### 3.3.5 TransUNet  
TransUNet [13] emerges as a transformative model within the domain of image segmentation, combining the strengths of global self-attention from Transformers and the local spatial awareness of Convolutional Neural Networks. Its primary aim is to tackle the intricate challenges presented by images, characterized by complex structures and significant variations.  
The overarching goal of TransUNet is to leverage the inherent capabilities of both Transformers and CNNs to achieve precise segmentation results on medical images. With medical images often exhibiting intricate anatomical details and varying shapes, TransUNet seeks to exploit the global self-attention mechanisms of Transformers for understanding broader contextual relationships, while simultaneously harnessing the local spatial information extraction prowess of CNNs. TransUNet employs a hybrid CNN-Transformer encoder, effectively extracting high-level semantic features from image patches. Complementing this, TransUNet features a cascaded upsampler (CUP) decoder, responsible for upscaling the features and amalgamating them with high-resolution CNN features through the integration of skip-connections.  
TransUNet also introduces an innovative enhancement through the inclusion of additive Transformers within the skip-connections. This strategic integration further augments the model's capability to capture salient features and intricate structures, ultimately enhancing the quality of the segmentation results. Figure 5, adapted from [13], illustrates the general structure of TransUNet.  
In our implementation of the TransUNet architecture, the model is constructed with a substantial total of 406.5 million parameters and a total of 176 layers, with 98 of these layers containing parameters. For a more detailed scheme of TransUNet, please refer to [13].  
Figure 5: TransUNet architecture [13].
# Lightning Protection Methods for Wind Turbine Blades: An Alternative Approach
## 1 Introduction  
Windmills have been around for centuries, operating as grain grinders and water pumps. The concept and technology behind windmills have been adapted to generate electricity, which in its new form is now called wind turbines (i.e., wind energy). Wind energy generation is now becoming one of the largest contributors to renewable energy generation, where the recent demand for renewable energy has seen its increasing growth in use as well as in physical size. In other words, wind turbines are getting taller, in order to accommodate the demand, by capturing wind through a larger blade swept area and converting it into electricity. Owing to this, wind turbines are now more prone to lightning strikes due to the fact of their increased structural height.  
There are approximately 2000 thunderstorms at any given minute and about 100 lightning strikes per second worldwide [1]. This creates great risk for tall structures, such as wind turbines, to be struck by lightning, where the average electric current from a lightning return stroke is 30 kA [1]. This massive flow of current can heat up the leader channel air to between 25,000 \({}^{\circ}\)C and 30,000 \({}^{\circ}\)C (around fivetimes the effective temperature of the sun) [1; 2; 3]. Lightning protection system (LPS) is composed of lightning receptor, down conductor, and grounding, and all elements must be well connected to pass the lightning current to Earth safely. Although wind turbines are installed with LPS, there are still cases where blades and whole turbines are destroyed due to the fact of lightning strikes. Considering the 20-25 year design life for wind turbine [3], it is worth safeguarding the turbines from lightning strikes, because the damage associated with it will cause the down time of the turbine operation, causing extra costs for maintenance and an shortage of electricity. This, therefore, may suggest a need for improving the existing lightning protection systems for wind turbine blades.
# Machine Learning Technologies in the Supply Chain Management Research of Biodiesel: A Review
## 2 Biodiesel Supply Chain Management  
### Biodiesel Production  
The first step in biodiesel production involves selecting the appropriate feedstock, which relies on factors such as the chemical process, economic consideration, and the oil content in the chosen feedstock [14]. Biodiesel production can utilize various types of vegetable oil, animal fat, microbial oil, algal oil, and discarded oil [15]. Vegetable oils include soybean oil in the U.S., linseed and olive oil in Spain, sunflower oil in France, palm oil in Indonesia, guang pi in China, rapeseed oil in Germany, and canola oil in South Korea [16; 17]. Four popular technologies can convert vegetable oil and/or animal fat to biodiesel [18]. These technologies are the direct use of mixed oil, the microemulsion of oil, the pyrolysis of oil, and the transesterification of oil. Among these technologies, transesterification is generally preferred for producing biodiesel, because it effectively reduces the viscosity [19].  
Triglycerides are esters of saturated and unsaturated monocarboxylic acids and trihydric alcohol glycerides that are found in vegetable oils and animal oils, which in the presence of a catalyst can undergo a reaction with alcohol [20; 21]. Due to the chemical and physical advantages, cost-effectiveness, and rapid reaction with triglycerides, methanol is normally used for the alcohol [2]. From this process, biodiesel is produced as the main product, while glycerin is a by-product [1]. Although transesterification is commonly used for biodiesel production, it is no longer efficient due to the high consumption of alcohol, which is associated with higher production cost [22]. Therefore, more environment-friendly methods with lower energy consumption have been explored, such as ultrasonic, microwave, hydrodynamic cavitation, membrane, plasma, reactive distillation, supercritical, co-solvent, rotatory, and plug flow techniques [23]. These methods can minimize the reaction time, energy consumption, and catalyst requirements.
## 3 Finite Element Modeling  
The finite element simulations were performed through the software ABAQUS [21] and the analyses were carried out through a dynamic implicit quasi-static solver. All components were meshed by means of solid C3D8R elements, an 8-node linear brick with reduced integration. Mesh sizes were determined through sensitivity analyses, resulting in maximum dimensions of 5 mm, 10 mm, and 20 mm for bolts, plates, and steel profiles, respectively. A proper partitioning allowed the use of a structured hex pattern (Figure 6a). A stress-based mesh refinement algorithm has been implemented in the software.  
Figure 5: Geometric scheme for the definition of stroke.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Assembly**} & \multicolumn{3}{c}{**Half-Lengths of the Slots \(d_{sh.min}\)**} \\ \cline{2-5}  & **Upper** & **Lower** & \(d_{1,b}\) & \(d_{2,b}\) \\  & **[mm]** & **[mm]** & **[mm]** \\ \hline D1A & 31.3 & 38.4 & 405 & 495 \\ D2B & 26.6 & 31.0 & 658 & 768 \\ D2C & 33.5 & 38.8 & 960 & 1090 \\ D3B & 32.9 & 40.2 & 828 & 960 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Dimensions of the parts constituting the damper.  
The plastic behavior of steel parts was simulated based on the von [PERSON] yield criterion with combined hardening. The elastic properties and the true stress-true strain curves for S355 steel grade were established through coupon tests conducted in [22]. The bolts were modeled by meshing a solid cylinder with the nominal circular gross area of the bolt. Equivalent true stress-true strain curves for the bolts have been derived from data presented in [23; 24].  
The interactions between contacting surfaces (e.g., bolt-to-plates, plate-to-plate) have been simulated, considering both \"Normal\" and \"Tangential\" behaviors. The former was implemented using the \"Hard Contact,\" while the latter was modeled differently for steel-to-steel interfaces and friction pad-to-steel interfaces (Figure 6b). A friction coefficient of 0.3 was employed for steel-to-steel surfaces, while a coefficient of 0.53 has been defined for the pad. Bolt pre-tensioning was modeled using the \"Bolt load\" option, applied in the initial analysis step with no other external actions. The preload in the bolts of the friction damper was assumed equal to the corresponding design value, while the preload recommended by EN1993 1-8 [16] was applied to all other bolts. Boundary conditions were consistently modeled as in [7], namely as follows: (i) the column was pinned at the lower end, releasing all flexural rotations and vertical displacements at the upper end, allowing the column to deform freely along its axis; (ii) the beam tip was simply pinned in the initial step, and the beam was additionally restrained against lateral-torsional buckling by introducing torsional restraints spaced according to [20]. Monotonic displacement histories were applied at the beam tip in a vertical direction.
## 2 Materials and Methods
### Organic Compounds Degradation by Hybrid Anaerobic Baffled Reactor (HABR)  
The degradability of wastewater is remarkably affected by the type of organic compounds in it [36]. In order to further investigate the removal of different organic compounds and their biodegradability in different process units, GC-MS analysis was performed. The organic compound type numbers in the HABR influent and effluent are shown in Figure 5. GC-RMS analysis was performed to qualitatively characterize the pollutant types in the influent and effluent of the HABR. The results demonstrated that the quality of the influent was extremely complex, in terms of a large number of alkanes, amides, organic acids, ketones, phenols, and esters. Although COD was not decreased heavily through the HABR system, the number of organic compound types in the effluent was decreased to 44, while that in the influent was 84. The chemical structure of several compounds generally displayed substantial variation through the HABR system. Especially, the decrease of alkane types and increase of alkene types indicated that a number of long-chain alkanes were divided to alkenes under anaerobic condition. The HABR performed better in reducing the categories of alkanes than an EGSB reactor that decreased alkanes to 17 types [12]. Following the treatment, many high-molecular-weight compounds, such as cyclanes, quinolines, and phenols, were also converted to low-molecular-weight ones, which became much easier to be degraded further through subsequent aerobic techniques. The results indicated that HABR showed good performance on degrading some structurally complex and biologically refractory substances, which indicated the improvement of biodegradability of the wastewater [16].  
Figure 4: Suspended solid (SS) removal of the hybrid anaerobic baffled reactor (HABR) during operational period of 56-131 days.