esa-sceva/satcom-chunk-collection huggingface dataset:
# Logistics Transportation Vehicle Supply Forecasting Based on Improved Informer Modeling
## 4 Experiment and Analysis
### Experiments and Analysis of Results
#### 4.3.3 Comparative Experiments on Integrated Predictive Models  
To verify the performance of the proposed supply forecasting method in comparison with recently popular ensemble models composed of linear and nonlinear approaches, comparative experiments involving ensemble models were conducted. These included the ARIMA + BPNN model, ARIMA + GRU model, and ARIMA + LSTM model. The aim was to analyze the fitting effectiveness of the proposed supply forecasting model. The results of the comparative experiments between the proposed method and the ensemble prediction models are presented in Table 6.  
Figure 9: Results of single model comparison experiment. (**a**) support vector regression (SVR) model; (**b**) long short-term memory (LSTM) model; (**c**) gated recurrent units (GRUs) model; (**d**) back propagation neural network (BPNN) model; (**e**) Informer model; and (**f**) InformerStack model.  
To further validate and visually demonstrate the predictive performance of the proposed method for supply volume, a visualization of the fitting results from the comparative experiments on the test set is presented. The results of the ensemble model comparisons are shown in Figure 10. The supply volume predicted by the Enhanced_Informer model exhibits a smaller discrepancy from the true values compared to the ensemble prediction models, indicating a greater ability to capture the local signal fluctuations of the true values and demonstrating improved model fit quality.  
In the comparative experiments involving ensemble models formed by combining linear and nonlinear approaches, the proposed method demonstrates significant improvements in predictive performance. Specifically, compared to the ARIMA + BPNN ensemble model, the proposed method reduces MSE by 74.88% and MAE by 51.31%. When compared to the ARIMA + GRU ensemble model, the proposed method achieves a reduction of 71.56% in MSE and 50% in MAE, effectively halving the error metrics. In comparison with the ARIMA + LSTM ensemble model, the proposed method results in a 74.07% decrease in MSE and a 52.02% decrease in MAE. These findings indicate that the enhanced Informer model proposed in this paper exhibits superior fitting performance for supply volume predictions within the context of ensemble prediction models.
## 4 Experimental Results
### Data Collection Environment and Experimental Setup for Leak Detection
Binary cross-entropy was used as the loss function, and the learning rate was configured to \(1\times 10^{-3}\). Figure 5: Source domain data collection testbed environment using plant piping specimens. Figure 6: Target domain data collection testbed environment using plant piping specimens.
# Threat Detection Model for WLAN of Simulated Data Using Deep Convolutional Neural Network
## 2 Materials and Methods
### RF Impairments and Channel Configuration
#### 2.4.1 Rayleigh Multipath and AWGN  
We assume an average pathway gain of [0, \(-\)2, \(-\)10] dB with corresponding delay pattern samples of [0 1.81 3.41]. The signals are sent through a Rayleigh multipath fading and static channel. Therefore, to ensure that there is no change in channel for the same RF, we set the upper limit of Doppler shift to zero. Then we apply these settings with the multipath channel and add _awgn_ noise such that: Path Delays = [0 1.81 3.41]/fs, Average Path Gains = [0, \(-\)2, \(-\)10], and the Maximum Doppler Shift = 0.  
Simulating a static Rayleigh fading channel with a specific delay profile and average path gains effectively applied a known transformation to the data. This introduced variations in the received signal characteristics while keeping the channel static. The fading channel effectively acted as a data augmentation technique to create diversity within the dataset. By introducing variations, we've generated additional samples for the minority class and improved the performance of the deep-learning model when dealing with class imbalance.
## 5 Conclusions  
A novel 3 DOF piezoelectric USM with a spherical rotor was developed and investigated in this paper. A proposed piezoelectric ultrasonic motor with spherical rotor and two unimorph-type actuators are mainly focused on miniaturization and high-power density. An unimorph actuator combines piezoelectric ring-shaped transducer bonded to a thin bronze flange. Two shapes of the bronze flange were investigated: disc- and triangle-shaped. Numerical simulations and experiments were accomplished, and the working principle of the actuator was validated. Particular modal shapes were determined that can be used for the generation of the 3 DOF rotor motion. Results of harmonic response analysis showed that the largest amplitudes of oscillation were achieved when the flange has the disc-shape. Analysis of contact point oscillations trajectories was performed, and it was shown that elliptical trajectories of the contact point motion are generated at operating frequencies and can generate rotary motion of the spherical rotor. Three prototypes of USM were made when a ring-shaped piezoelectric transducer and two unimorph-type piezoelectric actuators with disc-shaped and triangle-shaped flanges were used. Measurement of actuator vibrations confirmed the results of numerical simulation. The difference between the results of measurement and numerical simulations at resonant frequencies are below \(\sim\)1% and \(\sim\)3% for disc-shaped and triangle shaped flanges, accordingly. Some decrease of an amplitude and shift of the resonance frequency of the unimorph actuators was determined using a preload (weight of the spherical rotor). The resonance frequencies (operating frequencies) of the actuators without preload/with preload, accordingly, are: for disk-shaped  
Figure 14: Measured vibrations of the actuator with disc-shaped flange: (**a**) deformations of the actuator with disc-shaped flange at 94.4 kHz; (**b**) amplitude vs. frequency graph, peaks of oscillations at 61 and 94.4 kHz. Red-X, green-Y, blue-Z coordinates.  
configuration 94.4 kHz/94.5 kHz and for triangle shaped configuration 93.7 kHz/93.9 kHz. The rotary speed of the rotor at an operating frequency and amplitude of driving electric signal of 70 V for a disk-shaped actuator was approximately 30 rpm, for a triangle-shaped actuator 27 rpm.  
[PERSON] was responsible for conceptualization, creation of experimental investigations and methodology, reviewing and editing the manuscript. [PERSON] contributed to numerical and experimental investigations, writing and preparation of the manuscript. [PERSON] contributed to formal analysis, writing and editing of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the European Regional Development Fund under a grant agreement with the Research Council of Lithuania, grant number 01.2.2-LMT-K-718-01-0010.  
This project has received funding from the European Regional Development Fund (project according to the supported activity No. 01.2.2-LMT-K-718-01-0010) under a grant agreement with the Research Council of Lithuania.  
The authors declare no conflict of interest.
# Enhancing an Imbalanced Lung Disease X-ray Image Classification with the CNN-LSTM Model
## 3 Materials and Methods
### Experimental Setup  
As shown in Figure 6, this study consists of several steps, including pre-processing, dataset splitting, selecting an appropriate architecture, and evaluating the best architecture. To ensure accurate and reliable predictions from a machine learning model, it is crucial to perform data pre-processing before training the model. This process involves transforming the data into a format suitable for the machine learning model. In this specific research, the images are resized to \(224\times 224\) pixels, and the pixel values are normalized by dividing them by 255. This transformation converts the range from 0-255 to 0-1, preparing the data for the model.  
This research aims to explain the process of training a model to achieve accurate classification of X-ray images. The training will be conducted in two phases:  
1. Determining the optimal number of deep-learning layers;
2. Applying image augmentation techniques.  
To select the optimal method for achieving high classification accuracy, both stages will be evaluated using the F1-score metric. The experiment will maintain the same setup as presented in Table 3, with 65 epochs and a batch size of 32, ensuring consistency. Additionally, the Adam optimizer will be used with an initial learning rate of \(5\times 10^{-6}\). These hyperparameters were chosen based on previous experimentation and have been found to be effective for the task at hand [32]. By maintaining consistent hyperparameters across experiments, the obtained results can be easily compared and evaluated, leading to more reliable and accurate conclusions.  
Figure 5: Illustration of K-fold cross-validation.  
In the first stage, experiments with various layer configurations will be conducted to establish the ideal number of deep-learning layers. This will involve building models with different numbers of layers. To increase the diversity of the training data, image augmentation techniques will be applied in the second step to the X-ray images. This will include transformations, such as rotation, flipping, and cropping, to generate new versions of the images for the model to learn from. The best augmentation techniques will be selected for use in the final model. Our aim is to develop a highly accurate model for X-ray image classification that can support medical diagnosis by incorporating the outcomes of both training phases.
## 2 Materials and Methods
### Spectroradiometric Data and Processing  
The spectral reflectance of the canopy for each genotype was captured at the BT, AN, and early milk grain (EMG, ZS 73) growth stages using a FieldSpec 4.0 spectroradiometer (Analytical Spectral Devices (ASD), Malvern Panalytical, Malvern, Worcs, UK), which can detect reflected light from the canopy in the range between 350 and 2500 nm with a final spectral interval of 1 nm and spectral resolution ranging between 3 nm below 1000 nm and 10 nm between 1000 and 2500 nm. The spectral reflectance of the canopy was captured between 11:00 and 15:00 h under clear-sky conditions from two different places within each plot. The optical sensor of the device, which had a field of view of 25\({}^{\circ}\), was placed vertically approximately 1.0 m above the canopy to cover an area of approximately 0.15 m\({}^{2}\) (approximately 44 cm in diameter). The final reflectance curve for each plot was the average of the spectral reflectance of the two different places with 10 scans for each. A white polytetrafluoroethylene (PTFE) barium sulfate panel (BaSO\({}_{4}\), Spectralon, Labsphere, ASD), which provides a reflectance value of 95-99% over a full spectrum range (350-2500 nm), was used to calibrate the spectral reflectance measurements and to account for any changes in sun irradiance and atmospheric conditions. These calibrations were made on each of the two plots. The spectral reflectance regions between 1830-1940 and 2445-2500 nm were removed from the final reflectance curve and analysis because of high noise levels. The spectral reflectance data in the full-spectrum range (350-2500 nm) were used to test the relationship between spectral reflectance at a single wavelength in the full-spectrum range and target traits. These spectral reflectance data were also used to calculate 13 published and modified SRIs. According to [PERSON] et al. [19], the modified SRIs were constructed in this study by replacing one or two wavelengths from published SRIs with new wavelengths, but these new wavelengths were still close to those of the published SRIs. The names of these SRIs and their abbreviations and equations are listed in Table 1. To make the data processing easier to understand, Figure 2 shows a general flowchart of the methodology proposed for indirectly estimating the plant biomass at different phenological stages.
## 2 Related Works
### Semantic-Feature-Based Global Localization  
Due to the high ability of convolutional neural networks to discover complex patterns, NetVLAD uses the network to generate global descriptors directly end-to-end [51]. LoST uses the network to learn local key points, global descriptors, and semantic information to complete VPR tasks with extreme changes in view and appearance [52]. In addition, some methods even use the network directly to give the results of pose estimation end-to-end [53; 54; 55]. Although their results show extremely high accuracy and robustness to noise in the dataset, they are invariably dependent and data-dependent, so the generalization performance of these methods is not satisfactory.  
As a high-level feature, semantic information is compact and has good stability, so it is suitable as a reference for visual localization. Some research work used specific landmarks as masks to avoid extracting feature information in some dynamic regions [56]. VLASE uses the semantic edges extracted by the network to characterize the image and, then, achieve localization. Some methods use columnar landmarks as a special location reference to improve the positioning accuracy of robots [57]. The aforementioned methods only consider the information of the episemantic class. However, the spatial relationship between semantics also implies information about the place. Therefore, the method proposed in this work uses both episemantic and spatial distribution information to pursue a complete description of the scene. Some studies use graph models to encode scenesand their topologies, but building graphs from images is a difficult problem, especially in sparse landmark scenes [58; 59]. In this work, semantic information is explicitly encoded as semantic vectors, and spatial geometric relationships are represented in the form of histograms. The descriptor constructed by this encoding method has a compact structure and good interpretability, which is commonly used in various scenarios.
## 3 Results and Discussion
### Cytotoxic Activity of Free and Encapsulated LaDEO and CnEO  
As shown in Section 3.1, LaDEO and CnEO were the most active EOs against lung A549 and colon HCT-116 cells. Nevertheless, EO employment in pharmaceutical products has some restrictions due to its high volatility, low aqueous solubility, chemical instability, and low bioavailability [30]. Intending to determine the potential advantages of EO encapsulation into SLN, the cytotoxic activity of free and SLN/EO (SLN/LaDEO and SLN/CnEO) was explored in A549 and HCT-116 cells. The cells were incubated with increasing amounts of free and encapsulated EO (25-400 \(\upmu\)L/L) and equivalent amounts of unloaded SLN for 24 h (Figure 3).  
Free EO and SLN/EO decreased cell viability in a concentration-dependent manner in both cell lines. Encapsulation of LaDEO and CnEO increased their cytotoxicity in HCT-116 cells from 200 \(\upmu\)L/L, where cell viability decreased from 44.0 and 49.6% (free EO) to 21.3 and 15.4% (SLN/EO), respectively (Figure 3A; \(p<\) 0.001 in both cases). The effect of EO nanoencapsulation against A549 cells was most important since a significantly enhanced cytotoxic activity was observed from 50 \(\upmu\)L/L CnEO and 100 \(\upmu\)L/L LaDEO (Figure 3B). A549 cell viability decreased from 96.5 to 63.2% and from 64.6 to 31.3% after encapsulation of 100 \(\upmu\)L/L and 200 \(\upmu\)L/L LaDEO, respectively (\(p<\) 0.001). CnEO incorporation into SLN decreased cell viability from 103.4 to 60.6% at 50 \(\upmu\)L/L, and from 68.8 to 35.1% at  
Figure 1: TEM images of SLN (**A**), SLN/LaDEO (**B**), and SLN/CnEO (**C**). Scale bar: 200 nm.  
100 \(\mu\)L/L, respectively (\(p\) < 0.001). Empty SLN in equivalent quantities did not produce any cytotoxicity at all in any of the two cell lines (Figure 3C) [31].  
The improvement of EO activity by encapsulation may be attributed to a series of aspects such as extended EO stability in the culture medium at 37 \({}^{\circ}\)C, augmented EO permeation through the cell membrane, the sustained presence of cytotoxic amounts of EO resulting from a controlled release from SLN, possible evasion of expulsive mechanisms, and rapid metabolism of the main bioactive VOC due to SLN protection [32; 33; 34].  
The IC\({}_{50}\) values of SLN/LaDEO and SLN/CnEO in A549 and HCT-116 cells are shown in Table 2.  
Based on these findings, the most promising system (SLN/CnEO against A549 cells) was explored for further studies.
# Detecting Reinforced Concrete Rebars Using Ground Penetrating Radars
## 4 Signal Processing  
### Time Zero  
The time zero is defined based on the purpose of the signal processing. The time zero can be the position of the first positive peak of the direct wave [51]. [PERSON] et al. [48] proposed a method that best fits the relationship between two-wave travel time and two-way travel length of the electromagnetic wave in the air. The positive peak times of the reflection waves from the copper plate located at different depths below the antenna were used in the best fitting to find the time zero. The following equation can determine the first positive peak:  
\[t_{p}=\frac{L}{V_{0}}+t_{0} \tag{1}\]  
where: \(t_{0}\) is time zero; \(t_{p}\) is positive peak time; \(V_{0}\) is wave velocity in the air; \(L\) is two-way travel length calculated as:  
\[L=2\sqrt{z^{2}+\left(\frac{S}{2}\right)^{2}} \tag{2}\]  
where: \(z\) is depth of the copper plate; \(S\) is distance between the transmitter and the receiver.  
Figure 2 displays the best data-fitting curve utilizing Equation (2), with a time zero of 1.3268 ns. However, the calculated wave velocity of 1/0.0026 = 385 mm/ns is significantly higher than the electromagnetic wave velocity propagated in the air, which is 299 mm/ns.  
This study proposes a straightforward method that does not require a metal plate to calibrate the time zero. The positive peak of the reflected signal from the rebar corresponds to the negative peak of the transmitted signal due to the reversal of its phase upon reflection from an infinite dielectric object. Therefore, the time zero can be determined by utilizing the signal's negative peak of the direct wave. As the signal travels through the air from the transmitter to the receiver, this method can accurately establish the time zero.  
\[t_{0}=t_{nda}-\frac{S}{V_{0}} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameters** & **Unit** & **Value** & **Description** \\ \hline SPN & - & 512–16,384 & Samples per scan \\ SPD & - & Varies & Scan per second \\ SPM (N\({}_{\text{s}}\)) & - & 196.85–1000 & Scan per meter \\ Top position & m & 0.08 & Level of the antenna \\ Range & m & 0.8 & Scan depth \\ Scan spacing & mm & Varies & Scan spacing in a scan line \\ Antenna frequency & GHz & 1.6 & \\ T-R offset & mm & 58 & Distance between transmitter and receiver \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of the GPR device.  
Figure 2: Best data fitting curve for time zero.  
In this context, \(t_{nda}\) represents the negative peak time of the direct wave in the air, as depicted in Figure 3. For instance, a \(t_{nda}\) of approximately 1.48 ns was observed at various distances (converted from traces) during a scan, as illustrated in Figure 4. Consequently, the time zero of approximately 1.3 ns can be computed using Equation (3).
# Line of Sight and Image Motion Compensation for Step and Stare Imaging System
## 3 LOS and Motion Compensation Kinematics and Algorithm
### Control Process  
The imaging time of each frame is 60 ms.After receiving the target position information, the command and algorithm unit starts to calculate the working parameters required by the gimbal and FSMs. According to the method described in Section 3.3, the gimbaled command angles are calculated in real-time based on information such as the position of the target point and the aircraft, the attitude of the aircraft. Similarly, as described in Section 3.4, the velocities of image motion compensation are calculated. After the frame cycle starts, the gimbaled command angle required by the current target position is updated in real time and sent to the gimbal subsystem, and the \"step\" command is started at the same time. Then the gimbal is controlled to move to the position indicated by the command angle. To reserve start time for FSMs, the command and algorithm unit send image motion compensation (IMC) command before exposure. During the exposure, the gimbal subsystem keeps the LOS stable, and FSMs compensate according to the real-time velocities. After the exposure is completed, the command and algorithm unit starts to send the gimbaled command angles of the next frame, which is repeated to achieve high-precision positioning and high-resolution imaging. The control flow chart is shown in Figure 7.  
Figure 7: Control flow chart of the system.
# Characterization of HVSR and VRSR in the Loess Plateau of China Based on Strong-Motion Data
## 1 Introduction
The anomalous destruction, including widespread building collapses and infrastructure damage, revealed significant amplification effects of site conditions, topography, and loess thickness on seismic waves during propagation [26; 27]. The 2013 Zhangxian-Minzian earthquake further confirmed the notable impact of site conditions, topography, and loess thickness on ground motion in the Loess Plateau. The earthquake triggered secondary disasters, such as landslides and debris flows, causing severe casualties and property losses. The interplay of loess thickness, topographic undulations, and complex geological structures in the seismic zones intensified the severity of earthquake damage [28; 29]. Similarly, the 2023 Jishishan earthquake, occurring at the northeastern edge of the Loess Plateau, resulted in casualties and building collapses. The event was linked to heterogeneity in the deep structure of the Loess Plateau, particularly regarding rapid changes in its physical parameters. The damage revealed the significant influences of site conditions, topography, and loess thickness on ground motion while also highlighting the role of deep structural activity in earthquake generation [30; 31]. These seismic events underscore the close relationship between earthquake damage characteristics in the Loess Plateau and site conditions. Regions with thicker loess deposits and more pronounced topographic variations exhibit stronger ground motion amplification effects. Therefore, studying the dynamic properties of loess holds both theoretical significance and practical value for engineering applications. This study analyzes and summarizes the main characteristics of the average spectral ratio curves in the Loess Plateau using strong-motion records from the region processed via HVSR and VRSR methods. The findings provide valuable insights into the dynamic behaviors of loess sites and offer a reference for seismic design in loess-covered regions.