esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
Elemental Al, Fe, Ce, and Mn powders (purity \(\geq\) 99.9%) were mixed to the nominal compositions of Al\({}_{82}\)Fe\({}_{16}\)Ce\({}_{2}\) and Al\({}_{82}\)Fe\({}_{14}\)Mn\({}_{2}\)Ce\({}_{2}\) in atomic percent. The metallic powders were then encapsulated in hardened steel vials. For the mechanical alloying process, a tungsten carbide (WC) ball with a powder-to-ball weight ratio of 10 g to 200 g was utilized. This ratio was selected to optimize the milling efficiency and ensure uniformity in the powder mixture. To prevent the sticking of the powders to the milling tools, 50 mL of n-hexane was employed as a process control agent. Prior to milling, the vials were evacuated and then filled with protective argon (Ar) gas at a pressure of 3 \(\times\) 10\({}^{5}\) Pa to create an inert atmosphere, thus preventing oxidation and contamination during the milling process. The mechanical alloying was conducted using a high-energy planetary ball mill (AGO-II type), which operated at a rotational speed of 350 rpm. The milling vials were equipped with a water-cooling system to effectively manage any temperature increases that could potentially affect the material properties. The duration of milling was varied, ranging from 5 to 40 h, to investigate the evolution of the alloys' microstructure over time. Tomaintain purity and minimize gaseous contamination, each milling run was executed continuously for its predetermined duration without any interruptions. The principle and function of a high-energy planetary ball mill in the preparation of amorphous alloy powders from elemental powders is illustrated in Figure 1. Post milling, the morphology of the milled powders was extensively characterized using scanning electron microscopy (SEM) employing a HITACHI TM4000 PLUS instrument (Hitachi High-Tech Corporation, Tokyo, Japan). Phase analysis of the as-milled powders was conducted using X-ray diffraction (XRD) with a SIEMENS D5000 diffractometer (Siemens, Berlin, Germany). This analysis utilized Cu K\(\alpha\) radiation (\(\lambda\) = 1.5405 A), covering a 26 range from 20 to 80 degrees, with a step size of 0.02\({}^{\circ}\) and a scanning speed of 1\({}^{\circ}\) per minute. The XRD data were analyzed and normalized using the MDI Jade 6.5 software, which accesses the ICDD's PDF-2 database, allowing for accurate phase identification. Lastly, the magnetic properties of the as-milled powders were assessed using a vibrating sample magnetometer (VSM, MicroSense EV9, Lowell, MA, USA) capable of applying a maximum field strength of 1200 kA/m. This measurement provided crucial insights into the magnetic behavior of the alloys, including their saturation magnetization and coercivity, which are essential for understanding their potential applications in various technological fields.  
Raw Metallic Powders
## 3 Results and Discussion
### Ultrasonic Tomography
To facilitate the diagnostic process and check the development of the zones characterized by low elastic characteristics while also detecting their geometry and size inside the stem, a few tomographic slices corresponding to horizontal sections of the investigated stem are shown in Figure 13.  
Figure 11: Different views of the 3D tomographic model. The location and orientation of the horizontal slices extracted from the 3D ultrasonic tomography data volume were decided interactively for a clearer visualization of the internal distribution of the elastic characteristics of the investigated stem, while also taking into account the visual inspection results (Figure 7) and the 3D CRP models (Figures 8-10). From the visualization of the tomographic slices, we could better understand the three dimensional development of the low velocity zones (blue colored in Figure 13) associated with the wood decay distribution. It can easily be observed that the decay begins approximately at an elevation of 80 cm above ground level and increases quite homogeneously towards the apical part of the stem.
## 2 Crystal Orientation and Practical Determination of Principal Indices of Refraction  
The following provides a summary and practical guidance to determine crystal orientation, as well as how the measurement and determination of the directions and values for the principal indices of refraction can be carried out. This is necessary since the methodsfor calculation of population transfer in Sections 4 and 6 will be presented in this coordinate frame. In order to add the molecular dynamics modelling, the crystallographic axes must be rotated onto the optical frame. Most of the following information is available in standard texts of crystallography and mineralogy such as [PERSON] (1959) [8]. However, methods commonly used in mineralogy are rarely used in protein crystallography or chemical crystallography and are worth summarising for these specific purposes.  
Firstly, we summarise the crystal classes with respect to the directions of principal indices of refraction. These can be represented by the optical indicatrix. This three-dimensional ellipsoid is given by the following:  
\[\frac{x^{2}}{n_{1}^{2}}+\frac{y^{2}}{n_{2}^{2}}+\frac{z^{2}}{n_{3}^{2}}=1 \tag{1}\]  
where \(n_{1}\), \(n_{2}\), and \(n_{3}\) are the principal refractive indices of the crystal.
## 3 Numerical Validations of Stiffened Ship Panel Strain Reconstruction  
### Ship Stiffened Panel Model  
The stiffened ship panel comprised a panel, two transverse T-sections, and six longitudinal angle steels, all constructed from Q235 structural steel, as depicted in Figure 4a.  
Seventeen points were designated on the outer side of the ship stiffened panel, with points 1# to 15# serving as verification points for strain field reconstruction, and points Load A and Load B acting as the loading points. Points 1#, 2#, 3#, 4#, and 5# were situated along Path 1, positioned 495 mm from the sideline. Points 11#, 12#, 13#, 14#, and 15# were positioned along Path 3, 1485 mm from the sideline. Points 6#, 7#, 8#, 9#, and 10# were all situated along Path 5, positioned on the outer side midline, as illustrated in Figure 4b. The dimensions of each component of the ship stiffened panel are shown in Table 1:  
In the finite element simulation analysis, the ship's stiffened hull panel was configured in a single-side fixed support form. By applying forces of 10 KN along the Z-direction (perpendicular to the panel surface) at the Load A and Load B points, the structure underwent bending deformation, which was defined as \"Case 1\". When the Load A point  
\begin{table}
\begin{tabular}{c c c c} \hline
**Component Name** & **Length [mm]** & **Width [mm]** & **Thickness [mm]** \\ \hline plate & 2600 & 1980 & 5 \\
1 \#, 2 \# transverse T-sections & Top surface & 2600 & 100 & 8 \\  & Sidewalls & 2600 & 150 & 6 \\  & longitudinal angle steel & 1980 & 90 & 4 \\ \hline \end{tabular}
\end{table}
Table 1: Dimensions of each component of the stiffened ship panel.  
Figure 4: (**a**) Ship stiffened panel; (**b**) reconstructed strain verification point and loading point.  
experienced a load of 13 KN, and the Load B point bore a load of 9 KN, the structure underwent bending-torsion deformation, defined as \"Case 2\". This paper focused on the simulated verification of the strain field reconstruction method for the stiffened ship panel in the aforementioned two cases.  
In the simulated verification process of the proposed strain field reconstruction method in this study, strain data from several measurement points on the inner side of the ship stiffened panels were extracted from numerical simulation results. These extracted data served as the measured strain input required for the strain field reconstruction method. Based on this, the computed reconstructed strain data for verification points on the outer side of the ship stiffened panels, as depicted in Figure 4b, were obtained. Subsequently, a comparative analysis was conducted between this reconstructed data and the simulated strain data obtained for each verification point. This comparative study aimed to assess the reconstruction accuracy of the method proposed in this paper.
# Factory Logistics Improvement: A Case Study Analysis of Companies in Northern Thailand, 2022-2024
## 2 Background  
### Industries in Thailand  
Thailand's economic landscape, as reported by the Office of the National Economic and Social Development Council in 2022, shows a gross domestic product (GDP) of THB 17,378,017 million, positioning the country as the world's 23 rd-largest economy. The GDP composition reveals agriculture contributing 8.7%, industry 32.6%, and services 58.7% [25]. Key industrial sectors include the electronics, automotive, and food industries [26].  
Thailand faces the middle-income trap due to the relatively low technological learning of firms, perpetuated ineffective science, technology, and innovation policies, and a lack of critical mass of innovative firms [27]. Challenges in Thai industry include managing cultural diversity, mitigating the impact of natural disasters on the global supply chain, and addressing the need for technological upgrading and input growth [28]. The lack of systematic planning and control of the production process in SMEs poses barriers to lean automation transformation and industrial technological advancement [29].
# Phishing Detection in Blockchain Transaction Networks Using Ensemble Learning
## 4 Experimental Results
### Comparison with Existing Methodologies
The outputs of individual models are combined, either through majority voting or weighted averaging, to make the final prediction. Ensemble learning can improve detection accuracy by leveraging the strengths of different models andreducing the impact of individual model weaknesses. It can also help mitigate false positives and false negatives, leading to more reliable phishing detection in blockchain transaction networks. * Ensemble learning can also be applied by combining different methodologies discussed earlier, such as combining blacklisting, heuristics, and machine learning-based approaches. Each methodology can contribute unique strengths to the ensemble, leading to a more comprehensive and robust phishing detection system. For example, the outputs of blacklisting, heuristics, and machine learning models can be combined using ensemble techniques to make the final decision. This approach helps leverage the complementary nature of different methodologies and enhance the overall accuracy and effectiveness of phishing detection. * Ensemble learning can be made adaptive by continuously monitoring the performance of individual models or methodologies and dynamically adjusting their contributions to the ensemble. This adaptability allows the system to respond to changes in the phishing landscape and quickly incorporate new detection techniques or update existing models. By combining ensemble learning with adaptive mechanisms, the phishing detection system can stay updated with evolving phishing techniques and improve its resilience against emerging threats in blockchain transaction networks. Ensemble learning-based methodologies offer several advantages for phishing detection in blockchain transaction networks. They can improve detection accuracy, handle a wide range of phishing techniques, and adapt to changing attack patterns. However, it's important to consider the challenges associated with ensemble learning, such as the need for diverse and high-quality models, potential overfitting, computational complexity, and the requirement for continuous model maintenance and updates. In summary, ensemble learning-based methodologies for phishing detection in blockchain transaction networks provide an effective approach to enhance the accuracy and robustness of the detection system by combining multiple models or methodologies. The choice of ensemble technique and the composition of the ensemble depend on the specific requirements and characteristics of the blockchain network, the available data, and the resources at hand.
# Design of an Autonomous Cooperative Drone Swarm for Inspections of Safety Critical Infrastructure
## 7 Algorithms and Protocols
### Cable Detection and Identification  
The cable detection algorithm relies on sensor data obtained from the onboard mmWave radar and camera sensors as well as vehicle odometry information obtained from the flight controller. First, images from the camera are passed to a Hough Lines hardware accelerator to extract the directions of the powerlines relative to the drone. Then, a data fusion scheme is used to fuse powerline direction data from the camera, 3D measurementsfrom the mmWave radar, and vehicle odometry from the flight controller into a coherent pose-estimate of the overhead powerlines.  
Assuming that all powerline cables are parallel, a projection plane \(\mathcal{A}\) is created that is perpendicular to the obtained powerline direction \(\mathbf{d}\), parallel to the gravity vector \(\mathbf{g}\), which contains the drone position \(\mathbf{p}\). The way the plane is defined, the closest point, relative to the drone, of any powerline with direction \(\mathbf{d}\) will lie in the projection plane \(\mathcal{A}\). The distance \(\mathbf{d}\) is obtained from the analyzed camera images, and \(\mathbf{g}\) and \(\mathbf{p}\) are fetched from the flight controller. Data from the mmWave radar sensor exhibit the highest variance in the direction \(\mathbf{d}\) along the powerline cables. Therefore, to reduce the largest source of variability in the mmWave data, new radar 3D measurements are projected along \(\mathbf{d}\) onto plane \(\mathcal{A}\). The clustering of projected points within the plane assures that multiple measurements of the same cable are combined into a single estimate.  
Now that radar, camera, and odometry data are fused in the projection plane, and a Kalman filter for each estimated powerline is used to track their poses over time. The final output of the perception system is a pose-estimate representation of each detected powerline cable that includes the 3D position of the closest point and the direction of the powerline relative to the drone. Figure 6 shows the test environment and the data generated by the perception system. A more in-depth explanation of this work can be found in [44].
## 3 Numerical Simulations and Experiment Validation
### Finite Element Simulation Model  
Building upon the theoretical framework described in Section 3.1, a two-dimensional finite element model of the AM component was developed using a self-written simulation program, as illustrated in Figure 2. The model measures 20 mm in length and 1 mm in height, with aluminum alloy specified as the base material; its corresponding thermal and physical properties are provided in Table 1. To ensure a sufficient resolution of the wave characteristics, the spatial mesh size was set to 5 \(\upmu\)m, and the time step was fixed at 0.1 ns. The total simulation duration was 5 \(\upmu\)s to capture the complete wave evolution and interaction with defects.  
To realistically emulate the internal void defects that are frequently observed in metal AM, multiple circular pores were introduced into the model domain. These voids were randomly distributed using a stochastic function, ensuring variability in both spatial ar  
Figure 2: Schematic diagram of simulation model.  
rangement and defect quantity. The diameters of the individual pores ranged from 20 um to 100 um, based on the defect characterizations reported in the literature [24; 36; 37], ensuring consistency with realistic porosity scales encountered in metal AM process. This allowed for the investigation of different porosity levels and their effects on wave propagation. In the numerical model, a grating-patterned laser source, consisting of 12 lines with a 0.5 mm period (totaling 6 mm in length), was applied to the top surface to induce localized thermoelastic expansion and generate SAWs with a fixed wavelength \(\lambda_{s}\) of 0.5 mm. The resulting transient thermal response was confined to a narrow region (6 mm \(\times\) 0.1 mm) adjacent to the laser source. To minimize boundary reflections, low-reflecting conditions were imposed on the bottom and lateral boundaries. A detection probe was placed 2 mm to the right of the laser source to capture the generated SAW signal.
## 4 Model and Methods
### Model Preparation and Hyperparameters Testing
These preprocessing steps ensure that the model is trained on data that accurately reflects real patient conditions, minimizing the risk of misleading patterns due to artifact-laden signals. The architectural configurations spanned 1-2 LSTM layers and 2-5 dense layers with dropout rates between 0.2 and 0.5. For normalization, three options were tested: batch normalization, layer normalization, and none. Bidirectional layers were included to assess their impact on feature extraction. The learning rate was varied between 0.0001 and 0.001 to find the optimal convergence speed, which is visualized in Table 1.  
This refined exploration led to the final model configuration, which included a hidden size multiplier of 2, one LSTM layer, three dense layers, a dropout rate of 0.3, and LayerNorm for normalization. The final model, consisting of 10,766 parameters, demonstrated stability and efficiency over 200 epochs of training, showing optimal recall and loss balance. Table 2 summarizes these hyperparameters, presenting a model specifically optimized for the accurate and efficient detection of apneic events with minimized risk of overfitting. To mitigate overfitting and enhance model generalization, we applied multiple strategies. Dropout layers were included after each dense layer, with dropout rates tuned to 0.3, as this value achieved the optimal trade-off between performance and regularization. Additionally, we monitored the validation set performance across each hyperparameter sweep, ensuring consistency between training and validation results. The final model's validation balanced accuracy was closely aligned with the test set results (validation recall \begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameters** & **Values** \\ \hline Sequence size & 30, 60, 90 \\ Scale & all \\ Balancing & True \\ PulseRate & True, False \\ Feature ing & True \\ Include time & True, False \\ Prepro. scaler & StandardScaler, RobustScaler \\ Hidden size & 1–3 \\ Number LSTM layers & 1–2 \\ Number dense layers & 2–5 \\ Dropout & 0.2–0.5 \\ Norm. type & batchnorm, layernorm, None \\ Bidirectional & True \\ Learning rate & 0.0001–0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Hyperparameters for LSTM. of 81% vs. test recall of 76%), indicating effective generalization and minimal overfitting despite the model's complexity.
# Evaluation of Three Auditory-Sculptural Qualities Created by an Icosahedral Loudspeaker
## 3 Experiment 1: Initial
### Discussion  
_Directionality:_ The results in Figure 3a show that the magnitude in directionality significantly increases from d\({}_{1}\) to d\({}_{3}\). The directionality of d\({}_{2}\) is more pronounced than the one of d\({}_{1}\), assuming that its sound FM(5) would already yield auditory objects projected further away from the IKO's surface than FG(3). Moreover, the rotating beam of d\({}_{2}\) is assumed to yield stronger lateralization cues in comparison to the fading from beam 1 (below) to beam 2 (above) in d\({}_{1}\). Condition d\({}_{3}\) that has been rated as most directional uses PN(6) as one of the rotating beams and FG(3) for the other, of which we assume that it avoids auditory grouping of the two time-variant beams. Also the static IB(1) might fall into background in relation to FG(3). PN(6), with its strong projection scope, appears to guide attention more than the spatially less-defined rotating FM(5) of d\({}_{2}\).  
_Contour:_ The experiment revealed that the magnitude of contour significantly increases in the conditions c\({}_{1}\) to c\({}_{3}\), cf. Figure 3b. In condition c\({}_{1}\) it appears that the low-frequency DR(4) cannot be localized very well other than at the IKO. Low frequencies are typically only weakly localized in rooms, which might cause a more uniform contour of the auditory object. Even though in c\({}_{2}\), FG(3) is directed upwards and PN(6) downwards, which can only produce weak lateralization cues, the contouring is assumed to be stronger than in c\({}_{1}\) as PN(6) still produces reflections at the closest walls behind the IKO. Condition c\({}_{3}\) presents the most pronounced contour. During one rotation of the slower beam, the two beams meet three times (every 120 degrees) resulting in three dominant directions. In combination with the temporal development of the directions from the two individual beams, they appear to yield a strong contour.  
_Plasticity:_ While the results indicate a significant increase in the magnitude of plasticity from p\({}_{1}\) to p\({}_{2}\), the further increase to p\({}_{3}\) is only weak, cf. Figure 3c. We assume that the movement in p\({}_{1}\) draws attention gradually to the depth, but with no simultaneous depth grading experienced. Therefore, we assume that almost no plasticity was perceived. Condition p\({}_{2}\) uses layering of static FG(3) and rotating PN(6) with large projection scope that is most likely producing a time-variant but sonically distinguishable concurrent depth grading. Condition p\({}_{3}\) was rated with higher plasticity, which can be interpreted as adding a further layering that seems to have been experienced as a weak increase of plasticity, only. The expected rise in depth grading may have been obstructed by a stronger auditory grouping of sounds. The downwards directed FG(3) beam seems to have been masked strongly by the counter-clockwise rotating IB(1) and PN(6) so that the additional auditory object in the depth might not have added a perceivable layer.
# Dynamic Cooperative MAC Protocol for Navigation Carrier Ad Hoc Networks: A DiffServ-Based Approach
## 4 DiffServ-Based Dynamic Cooperative MAC Protocol
### MAC Sub-Protocol with Optimal Relay  
This subsection aims to handle the MAC for the pairs in Scenario 3. It also aims to tackle the link failure problem caused by long-range transmission and degrading link quality.