esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Transfer Learning Based Snapshot Ensemble Method (TLSE)
#### 2.2.1 Transfer Learning Phase for TLSE  
In our study, a CNN model is adapted as the base model, which is pre-trained on the ImageNet dataset (ILSVRC2012) [43]. The last fully connected layers of the downloaded model are excluded. Instead of using several data augmentation techniques to reduce the overfitting problem, the modification of model architecture is designed by adding regularization to the model. On top of the base model, three fully connected layers with 512, 128, and 7 nodes are conducted. The dropout [44] is adopted in the first two fully connected layers, which consists of setting the output of each hidden neuron to zero with a probability of 0.5. This technique forces CNN to learn robust features. The ReLu [45] activation is applied in the first two fully connected layers. The weights are initialized with he-style [46], which allows extremely deep CNN models to converge, especially when taking ReLu into account. Batch Normalization [47] is set before the activation layer to provide activation with the stable distribution, which can accelerate deep network training by reducing internal covariate shift. These modifications of model architecture allow the network to generalize to the new data distribution easily.  
The training technique for the transfer learning phase in this TLSE method is that: firstly, freeze the pre-trained ImageNet's weight and remove the fully connected layers; secondly, add regularization to the model by making some specific refinements; at last, fine-tuning the whole CNN model using a small learning rate on the target dataset.
# Time Domain Room Acoustic Solver with Fourth-Order Explicit FEM Using Modified Time Integration
## 3 New Room Acoustic Solver Using Explicit TD-FEM with Modified Adams Method  
This section presents a description of the novel time domain room acoustic solver using explicit FEM, which is suitable for acoustic simulations in complex shaped rooms. The proposed method uses four-node quadrilateral FEs with modified integration points for spatial discretization and a specially designed higher-order time integration method for time discretization. First, frequency domain dispersion error analysis is applied to derive modified integration points that can maintain fourth-order accuracy in space. Then, a fourth-order accurate time integration method suitable for the present room acoustic solver is designed using a time domain dispersion error analysis. Additionally, we present the stability condition of this new method.
## 4 Analysis Model
### Analysis Setting
#### 5.1.4 Earthquake Records  
The seismic action of a near fault is short in duration. Thus, the maximum structural displacement occurs under a 1.5-2.5 cycle action of the main shock wave. The shock absorption effect of the TMD comes from the kinetic energy of the control mass block. The TMD does not provide enough energy to resist the structural movement in the early stage of an earthquake. Therefore, there is no noteworthy shock absorption effect of the TMD on the maximum structural displacement reaction under the action of a near-fault \begin{table}
\begin{tabular}{l l} \hline
**Analysis Parameter Category** & **Parameter Range** \\ \hline \multirow{6}{*}{Parameters of main structure} & Structure type: 10 DOF shear building \\  & Story mass: m\({}_{\mathrm{a}}\) = 500 m \\  & Inter-story stiffness: \(k_{\mathrm{a}}\) = 883,645 kN/m \\  & Inter-story damping: \(c_{\mathrm{a}}\) = 2813 kN/s/m \\  & First modal frequency: \(f_{0}\) = 1.0 Hz \\  & First modal damping ratio: \(\xi_{0}\) = 0.01 \\ \hline \multirow{6}{*}{Control Parameters of the ISAMD} & Mass ratio: \(\mu\) = 0.02 \\  & Freq. ratio: \(\gamma_{f}\) = 0.8145\(\sim\)6.0 \(\mathrm{\SIUnitSymbolMicro}\)rate = 0.95 \\ \cline{1-1}  & Damping ratio: \(\xi_{a}\) = 0.01–0.10 \\ \cline{1-1}  & Weight ratio: \\ \cline{1-1}  & \(\mathrm{w_{v}/w_{a}}=1/0,4\pi\mathrm{f_{0}/0.414,2\pi\mathrm{f_{0}/1},2\pi \mathrm{f_{0}/2.414,0/1}}\) \\ \hline \multirow{6}{*}{Control Parameters of the TMD} & Mass ratio: \(\mu\) = 0.02 \\ \cline{1-1}  & Freq. ratio: \(\gamma_{\mathrm{f}}\) = 0.8145–1.2277@rate = 0.95 \\ \cline{1-1}  & Detuning rate: \(\Gamma\) = 18.55\(\sim\)22.77\% \\ \cline{1-1}  & Damping ratio: \(\xi_{\mathrm{a}}\) = 0.071–0.10 \\ \hline \multirow{2}{*}{Seismic waves} & 26 records of ground acceleration of earthquakes with \\ \cline{1-1}  & epicentral distance = 1–339 km (see Table 2) \\ \hline \end{tabular}
\end{table} 
Table 1: Analysis setting value [47]. ground motion. Conversely, the structural displacement reaction is controlled by the component of the resonant frequency under the action of a far-field ground motion. The maximum displacement reaction occurs at 3-5 reciprocating cycles because the control mass block has enough time to accumulate sufficient kinetic energy to confer seismic resistance. Therefore, the TMD and ISAMD have significant control effects on the maximum structural displacement reaction for far-field ground motions because the shock absorption effect of the structural control method is closely related to the actual time history of the earthquake force. To compare the control efficiencies of different control methods, diachronic analysis of a building under control of the TMD and ISAMD is necessary to analyze and compare the average ratio and standard deviation of the shock absorption effect. Records of 26 earthquakes with epicentral distances of 1-339 km were used to analyze the seismic resistance. Each earthquake's name, occurrence time, recording station, epicentral distance, seismic direction and original peak ground acceleration are listed in Table 2.
# Arrays of Sub-Terahertz Cryogenic Metamaterial
## 3 Metamaterial Matrices of Microwave Filters: Design, Fabrication, and Experimental Studies
### Experimental Studies and Comparison with Modeling  
The spectral evaluation of the fabricated samples is based on backward-wave oscillator (BWO) radiation sources. Four such sources cover the frequency range 70-600 GHz. The output power spectrum of the BWO is rather non-uniform and requires a broadband detector for monitoring and calibration. Moreover, on the way from the BWO to the sample, the probe signal is attenuated and reflected from cryostat windows, filters, a sample holder, and elements of the beam guide. For the correct spectral measurements, an experimental setup was equipped with three calibration channels. First, with the pyroelectric detector close to the BWO, an estimation of the BWO and output horn spectrum is provided. Second, the channel with the RuOx bolometer provides a measurement of the power approaching the sample holder. The third one, comprising an array of NIS junctions, monitors the power approaching the chip with the bolometer under test. Such a complicated calibration technique is described in detail in [29]. A schematic view of the experimental setup is presented in Figure 23. Measured spectra of arrays of half-wave and electrically small antennas are presented in Figure 24.  
Figure 21: Schematic view of SINIS detector from [15]: (**a**) top view; (**b–d**) cross-section of three SINIS types: thick absorber deposited as last layer (**b**), thin absorber deposited first (**c**), and suspended absorber (**d**).  
Figure 22: SEM images of detectors fabricated by: (**a**) Dolan technique of shadow evaporation; (**b**) bridge-free shadow evaporation; and (**c**) suspended absorber (see also [31]).
# Internal Flow Prediction in Arbitrary Shaped Channel Using Stream-Wise Bidirectional LSTM
## 4 Results and Discussion
### Quantitative Evaluations  
#### 4.3.1 Mean Relative Error  
To quantify and compare the performance of the models, the mean relative error between the prediction and ground truth was calculated for two areas, the entire channel region and the corner vortex region generated near the wall, using the formula in Equation (6):  
\[MRE(\alpha,\delta)=\frac{1}{N}\sum_{l=1}^{N}\frac{\sum_{i=1}^{N_{x}}\sum_{j=1}^{ N_{y}}|\alpha_{ij}-\hat{\alpha}_{ij}|\delta_{ij}}{\sum_{i=1}^{N_{x}}\sum_{j=1}^{ N_{y}}|\alpha_{ij}|\delta_{ij}}. \tag{6}\]  
where \(\alpha\), \(\delta\), \(N\), \(i\), \(j\), and \(\hat{\cdot}\) denote a scalar field variable, binary field values of the RoI, the number of test datasets, the index values of the width and height axes, and the predicted value, respectively. Table 2 summarizes the mean relative error for each RoI of each model. In the table, the best performance results are shown in bold, and the second-best performance results are underlined for emphasis. U-Net with SB-LSTM showed the best performance for all metrics, followed by ED with SB-LSTM, which showed the second-best performance for all metrics. Note that there was a significant improvement in the case of \(P\) prediction compared to the decrease in the error rate of the \(u\) and \(v\) values. This demonstrates the effective performance improvement of internal flow learning with the SB-LSTM module. These findings align with previous studies that have shown that the U-Net structure outperforms the ED structure in original image processing and flow prediction.  
Figure 14: Qualitative results of four different methods. From top to bottom: \(u\), \(v\), \(P\). From left to right: (**a**) ED, (**b**) U-Net, (**c**) ED with SB-LSTM, (**d**) U-Net with SB-LSTM, (**e**) target.
## 3 Parameter Optimization and Performance Test
### Net Dropping Seedling Duration Measurement Test
#### 3.1.1 Test Conditions  
The automatic seedling feeding device shown in Figure 10 was developed to measure the net seedling dropping duration, which was powered by a 24 V DC power supply to drive the motor, air compressor, and pneumatic control system. The motor drove the operation of the batch seedling launching mechanism; the air compressor provided compressed air for the pneumatic seedling clamp and each expansion cylinder; and the pneumatic control system included photoelectric switches, solenoid valves, and other components, which were used to control the pneumatic components to complete the specified action according to the timing. The pot seedlings used in the test were \"Xingshu 215\" pepper pot seedlings with a seedling age of 35 days. The seedling growth was from 20 July to 25 August 2022, and 20 pot seedlings were randomly selected for the material characteristics test. The average height of the pot seedlings (including the pot body) was 141.5 mm, the average maximum leaf expansion was 93.1 mm, the average weight was 12.2 g, and the average water content of the pot body was 42.5%.
## 2 Study Sites and Methods  
### Study Area  
The sea-bottom-pollution study was carried out in a total of 14 marinas in North Africa and in the western Baltic Sea. In North Africa, sampling took place in two marinas in the city of Alexandria in Egypt (6.1 million inhabitants) and in three marinas in Tunisia, namely the city of Bizerte (about 143,000 inhabitants), Yasmine Hammamet (a tourism resort and part of the city of Hammamet, the latter has about 100,000 inhabitants) and Monastir (about 93,000 inhabitants). The criteria for selecting the harbors were their accessibility and their location in an urbanized area. All African marinas have touristic infrastructure and beaches in the vicinity (Figure 1).  
In the Baltic Sea, the studies focused on harbors surrounding the cities of Rostock and Kiel. Rostock (210,000 inhabitants) and Kiel (246,000 inhabitants) are located in the western Baltic Sea and are the most important Baltic Sea harbor cities in Germany. Both host cruise, cargo, dry and military ports as well as shipyards. In addition, each city has 10 marinas for leisure boats. Sampling took place in four of the marinas in Rostock (Alter Strom, Hohe Dune, Alter Stadthafen and Ludewigbecken) and in five marinas in and around Kiel (Dusternbrook, Seeburg, Strande, Wendtorf and Laboe) (Figure 2).  
With the Kiel Week and the Hanse Sail Rostock, the cities host major annual Baltic sailing and harbor events. The Kiel Week is the largest sailing event in the world and the largest summer festival in the Baltic Sea region. In 2023, it attracted 3.8 million visitors over nine days, the highest number ever recorded. More than 100 larger historic steam and sailing ships offered boat trips, over 4000 sport-sailors with about 1500 boats took part in the competitions and 22 cruise ships visited the harbor during the event [33]. The Hanse Sail was founded in 1991. In 2023, the Hanse Sail Rostock counted about 500,000 visitors during four days and about 150 ships offered boat trips for the visitors [34]. Before the COVID-19 pandemic, for example in 2009, the Hanse-Sails counted more than 1 million visitors and in some years more than 200 participating historic ships [35]. The potential role of these events on sea bottom pollution was one aspect of interest.
## 2 Materials and Methods
### Scale-Up Design  
The height of the large-lab-scale column was obtained according to the geometric, kinematic, and dynamic similarity criteria. Based on the geometric criteria, the ratio between the bed height and column diameter for both cases (lab-scale-column--h/d and large-lab-scale column--H/D) was kept constant. In addition, the superficial fluid velocity was maintained constant in both cases to ensure the same mass transfer and hydrodynamic conditions [26]. The large lab-scale column diameter was set to 4 cm. Considering the optimal conditions obtained in the design of experiments (\(h\) = 30 cm, \(Q\) = 5 mL/min), the bed height of the large-scale column was set at 60 cm, and the flow rate at 20 mL/min. The large-lab-scale column experiment was conducted at 20 \(\pm\) 1 \({}^{\circ}\)C with an initial P concentration of 10 mg/L.  
The length of the unused bed (_LUB_) method was employed to calculate the main parameters to operate in an industrial-scale column based on previous large-lab-scale results. According to this method, if the adsorption stops at the breakthrough point, part of the adsorbent capacity remains unused. This fraction is proportional to the distance between the location of the stoichiometric front (\(h_{st}\)) and the bed height at the lab-scale (\(h\)), as shown in Equation (8),  
\[LUB=h-h_{st} \tag{8}\]  
In summary, _LUB_ is related to the adsorption rate, and as the mass transfer processes become slower, _LUB_ becomes longer [15]. Considering that \(h_{st}\) moves at the same velocity as the real front, the travel velocity (\(u_{z}\)) can be defined using either \(t_{bp}\) or \(\mathrm{t_{st}}\), as shown in Equation (9).  
\[u_{z}=\frac{h_{st}}{t_{bp}}=\frac{h}{t_{st}} \tag{9}\]  
Combining Equations (8) and (9) the Equation (10), may be obtained,  
\[LUB=u_{z}\Big{(}t_{st}-t_{bp}\Big{)}=\frac{t_{st}-t_{bp}}{t_{st}}h \tag{10}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Run** & **Q (mL/min)** & **_h (cm)_ & **Run** & **Q (mL/min)** & **_h (cm)_ & **Run** & **Q (mL/min)** & **_h (cm)_ \\ \hline
1 & 5 & 20 & 4 & 7.5 & 20 & 7 & 10 & 20 \\
2 & 5 & 30 & 5 & 7.5 & 30 & 8 & 10 & 30 \\
3 & 5 & 40 & 6 & 7.5 & 40 & 9 & 10 & 40 \\ \hline \end{tabular} \(Q\)—flow rate; _h_—bed height.  
\end{table}
Table 2: Conditions of the experiments according to a full factorial design in a lab-scale column [10 mg P/L, 20 \(\pm\) 1 \({}^{\circ}\)C, pH 7.33].  
By rearranging Equation (10), it is possible to determine a relationship to calculate the breakthrough time for different bed heights.  
\[t_{bp}=\frac{1}{u_{z}}(h-LUB) \tag{11}\]
# Localized Necking Model for Punching Fracture Simulation in Unstiffened and Stiffened Panels
## 3 Fracture Model Formulation  
### Material and Experiments  
Quasi-static tests were conducted to characterize the plasticity and fracture model parameters of 1.9 mm thick JIS G3131 SPHC hot-rolled thin plates. The chemical composition of the mill test certificate is listed in Table 1. All specimens were fabricated along the rolling direction of the sheet material using the laser cutting technique. Standard uniaxial tension tests were conducted for five types of specimen geometries.  
* Standard dog-bone specimen with a 12.5 mm gauge width, denoted as DB [19].
* Central hole specimen with a 6 mm central hole and 20 mm gauge width, denoted as CH.
* A notched tension specimen with a 10 mm gauge width and a notch radius of 20 mm, denoted as NT.
* Plane strain tension specimen with a 16 mm gauge width and a notch radius of 5 mm, denoted as PST.
* Shear specimen, adopted from the design proposed by [PERSON] et al. [8], denoted as SH.  
Figure 2: Schematic curves of ductile fracture and necking limit.  
All specimen geometries are shown in Figure 3. The tension tests were carried out using a 50 mm extensometer and a servo-mechanical loading frame under a constant stroke velocity of 2.0 mm/min at room temperature. The DB specimen was used to obtain the characteristics of the elasto-plastic behavior before the onset of diffuse necking. The stress state of the CH specimen was close to the uniaxial tension (\(\eta=1/3,\overline{\theta}=1.0\)). NT and PST specimens show fracture mechanisms similar to plane strain tension (\(\eta=1/\sqrt{3},\overline{\theta}=0.0\)). The SH specimen was designed to obtain the shear fracture (\(\eta=0.0,\overline{\theta}=0.0\)) in the gauge part, but the stress state changed to a mixed tension/shear state by increasing the loading step.
# Spectral Line Reflectance and Fluorescence Imaging Device for Skin Diagnostics
###### Abstract
To extract \(n\) spectral line images in a snapshot, the number of spectral sensitivity bands of the image sensor system also has to be \(n\). For instance, standard RGB color cameras can record three spectral line images by a single snapshot if the illumination spectrum comprises three spectral lines, each of them positioned within one of the detection bands (R, G, or B) [19; 20; 21]. Triple spectral line imaging has been successfully applied for colored counterfeit detection [17; 22] and for remote distribution mapping of the main skin chromophores (melanin, oxy-, and deoxy-hemoglobin) [23; 24]. However, also mapping of other skin chromophores (e.g., bilirubin, lipids, or water) is of diagnostic interest; therefore, SMSLI technologies for higher \(n\) values have to be developed. Our previous study in this direction resulted with a battery-powered proof-of-concept device for the snapshot acquisition of four spectral line images to map four skin chromophores, also being able to capture skin autofluorescence (AF) images under the violet 405 nm laser excitation [25]. However, its double-camera design appeared too robust and inconvenient for clinical applications. As the next step, we developed a more compact and handy single-camera design, which is described in this paper.
# A Multi-Physics Modeling-Based Vibration Prediction Method for Switched Reluctance Motors
## 4 Experimental Results and Verification
### Simulation and Experimental Results  
#### 4.2.1 Analysis of Flux Linkage Characteristics  
Based on the established magnetic field module, the flux linkage characteristics of several rotor angles are analyzed. Considering the accuracy of the position sensor used in the experimental test, the rotor angle is set from 0\({}^{\circ}\) (aligned position) to 22.5\({}^{\circ}\) (unaligned position) with the step increment of 4.5\({}^{\circ}\). In the Ansys software (Canonsburg, PA, USA), the mesh can be divided intelligently. As can be seen from Figure 6b, the mesh around the air gap is finer. The exact current is applied to phase A windings, and the value is between 2A and 16A, with the increment of 2A. The current density J\({}_{\mathrm{z}}\) is obtained by dividing the excitation current and the cross-sectional area of the winding wire. The magnetic vector distribution is solved by the software solver. The flux linkage data can be obtained after post-processing. To verify the simulation results, relevant experiments are carried out. The test procedure is as follows: fix the rotor at the required position, and apply a certain width pulse voltage to the A phase to make the current rise to at least 16A, then record the current and voltage data; tune the rotor to 4.5 degrees, repeat the previous operation until the rotor angle is 22.5 degrees, and then finish the measurement.  
The comparison between the simulation results and experimental results is shown in Figure 9. It is seen that the flux linkage curve calculated by the simulated magnetic field is consistent with the experiment results, which proves that the magnetic field simulation model of the SRM built in this paper conforms to the characteristics of the actual motor. Meanwhile, Figure 9 shows that the flux linkage curve of the SRM is nonlinear, which is related to the rotor angle and current magnitude. When the rotor is at the aligned position, the flux linkage is the largest, while the flux linkage is smallest at the unaligned position. Due to the saturation characteristics of ferromagnetic material, the flux linkage begins to saturate when the current reaches to 6A. In addition, the simulated flux linkage curve agrees  
Figure 8: (**a**) The experimental platform of SRM; (**b**) the measurement points.  
well with the experimental curve in the rotor position region with smaller flux linkage, but there are some differences at the regions with larger flux linkage. The difference is manifested in the fact that the saturation effect of the flux linkage is greater in the experimental measured curve, that is, the slope of the relationship curve decreases faster.
# UAV Dynamic Object Tracking with Lightweight Deep Vision Reinforcement Learning
## 3 Preliminaries
### Q-Learning  
Q-learning is a model-free, off-policy, and value-based RL method that uses the Bellman Equation (1) to update its value function.  
\[V(s)=max_{a}\big{[}R(s,a)+\gamma V(s^{\prime})\big{]} \tag{1}\]  
where \(V(s)\) is the value of state \(s\), \(R(s,a)\) is the reward of taking action \(a\) in state \(s\), \(\gamma\) is the discount factor, and \(V(s^{\prime})\) is the value of the next state.  
The main idea behind Q-learning is to store a table of Q-values for all state-action pairs that reflect how beneficial to take an action at a particular state. A Q-value is expressed as \(Q(s,a)=\mathbb{E}_{\tau}[R_{t}\mid s_{t}=s,a_{t}=a]\). Using the TD learning approach, Q-values are updated using the following equation:  
\[Q(s,a)=Q(s,a)+\alpha\big{[}R(s,a)+\gamma max_{a^{\prime}}Q(s^{\prime},a^{ \prime})-Q(s,a)\big{]} \tag{2}\]  
where \(Q(s,a)\) is the Q-value of taking action \(a\) at state \(s\), \(\alpha\) is the learning rate, \(R(s,a)\) is the reward of taking that action, \(\gamma\) is the discount factor, and \(Q(s^{\prime},a^{\prime})\) is the Q-value of taking action \(a^{\prime}\) at state \(s^{\prime}\).  
In the beginning, the Q-table is initialized with all zeros by default. We can achieve better optimization if we initialize the Q-table with values higher than the upper bound of all state-action values because it encourages the agent to explore state-action pairs that are not yet visited. During the learning process, the agent explores different state-action pairs and updates the Q-value table using Equation (2). In the end, an optimal policy is obtained by choosing the most beneficial action in every state.