esa-sceva/satcom-chunk-collection huggingface dataset:
# Radiation Damage of Polydimethylsiloxane and Polyimide by X-ray Free-Electron Laser
## 3 Results  
### X-ray Scattering Background of PDMS and PI  
When microcrystals are enclosed or deposited on a film and exposed to XFEL, diffraction from the crystal sample and X-ray background scattering from the film occur [25]. X-ray background scattering from the film is noise, which affects the signal-to-noise ratio (SNR) of the signal from the target molecule [25]. Therefore, the quality of the PDMS and PI films with low X-ray background scattering is vital. To check the use of the PDMS and PI films, we investigated X-ray background scattering of the PDMS and PI used in this experiment. We first attempted to investigate the X-ray scattering of PDMS and PI by exposing them to an XFEL. However, images acquired during the experiment showed a high background noise, with or without the film, indicating that noise was generated from the XFEL used in the experiment and the experimental environment. Accordingly, the scattering of the film using the XFEL could not be accurately validated; therefore, we checked the quality of the films using synchrotron X-rays. We measured the X-ray scattering of PDMS with various thicknesses (20 and 90 \(\upmu\)m) and PI (25 \(\upmu\)m) by exposing them to X-rays for 100 ms (Figure 1). The X-ray background scattering of PDMS was observed around 7.5 A and16 A (Figure 1A). When the X-ray exposure time was 100 ms, the 20 and 90 \(\upmu\)m-thicknesses of PDMS films had analogue-to-digital units (ADU) values of 12.1/12.7 and 26.3/30.8, respectively, at a scattering area of 7.5/16 A. The X-ray background scattering of PI was observed at approximately 15 A (Figure 1B). When the X-ray exposure time was 100 ms, the 25 \(\upmu\)m-thickness of the PI film had ADU values of 11.6. The background scattering of the films used in this experiment is negligible when analyzing the diffraction signals (Figure 1C). After X-ray exposure, the PDMS and PI films were not damaged by synchrotron X-rays. Therefore, these two polymer films can be reused at synchrotron X-rays, as in previous synchrotron applications.
## 3 Master-Slave Optimization Framework
### Master Stage: Multi-Vorse Optimizer (MVO)
#### 3.1.6 Boundary Control and Termination  
All updated values are subjected to boundary constraints to ensure feasibility:  
\[U_{i,j}\in[U_{Min}(j),U_{Max}(j)]\quad\forall i,j. \tag{28}\]  
The algorithm proceeds iteratively, applying exploration and exploitation operators in each generation. The process continues until a predefined number of iterations \(\mathit{iter}_{Max}\) is reached or another convergence criterion is satisfied.
## 5 Conclusions  
This paper presents two models to describe realistic problems in a logistic network. For the BPL problem, the MIP method finds an optimal solution for small network instances and is competitive with the presented heuristic methods. For large network instances, the computation time increases rapidly and the MIP method provides a suboptimal solution needing a rather large computation time. The EA and ABC heuristic methods require, on average, at least an order of magnitude less computation time, although the suboptimal result is approx. 5-10% worse in relation to the exact MIP method. In the case of the model extended by the ELP time parameters, for small problem instances, the MIP method is still 2-3% better than the heuristics. On the other hand, for medium and large problem instances, the exact MIP method does not find an integer solution at all in a reasonable time, while the heuristics give a satisfactory suboptimal integer solution in an acceptable computational time.  
Finally, it is worth noting that the results apply to a logistic network of practical importance and therefore, provide additional guidance for network operators who are planning to expand the logistic network, and that the proposed heuristic algorithms give suboptimal results, especially for a more difficult problem with more than 20 nodes. It should be noted that there is still potential for improving the results obtained by the heuristics and this will be our goal in future research work in this area.  
Conceptualization, S.K. and E.W.; methodology, S.K. and E.W.; software, M.B. and E.W.; validation, S.K. and E.W.; formal analysis, S.K. and E.W.; investigation, S.K., E.W. and M.B.; data curation, S.K. and M.B.; writing--original draft preparation, S.K., E.W. and M.B.; writing--review and editing, S.K. and E.W.; visualisation, S.K. and E.W.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Center for Research and Development under grant POIR.04.01.04-00-0054/17-00. LAS Project under the title Simulation and analysis methods of logistics networks for postal operators. Funding of The National Centre for Research and Development in Poland.  
Not applicable.  
Data Availability Statement: Data can be found at: [[https://github.com/ewarchul/applsci-2022](https://github.com/ewarchul/applsci-2022)]([https://github.com/ewarchul/applsci-2022](https://github.com/ewarchul/applsci-2022)) /tree/main/src/ampl.  
The authors declare no conflict of interest.  
Figure 3: Box plot of calculation time for heuristic methods for the BLP—(**a**) and ELP models—(**b**) model.
# Performance of Millimeter Wave Dense Cellular Network Using Stretched Exponential Path Loss Model
## 3 SINR Coverage Probability and Area Spectral Efficiency  
This section presents mathematical expressions for analyzing coverage probability and area spectral efficiency of a typical UE based on the system model defined in Section 2.
## 2 Literature Review
### Target-Oriented Sentiment Analysis
Literature studies revealed that a sentiment polarity extracted from a text depends on the target. However, most of the existing sentiment analysis methods predict general sentiment polarities, and in several cases, they may make wrong predictions on sentiment polarities. In particular, where the target is implicit, i.e., it does not appear in the given text, the methods predicting sentiment polarities from targets do not work [42]. Recently target-oriented sentiment analysis gained more and more attention, especially with the rise of social media and public opinion. SemEval-2015 Task 12 [43] and SemEval-2016 Task 5 [44] formalize ABSA as a task for target-aspect-sentiment detection from a sentence. To tackle these limitations, this paper proposes a dictionary-based method for target-oriented sentiment detection. It relies on a target dictionary obtained from a set of target words [42]. Figure 1 gives three review examples in the restaurant dataset from SemEval-2016 Task 5. The first example shows that the sentiment cannot be determined by the sentence and the aspect. The second example shows that the sentiment cannot be determined by the sentence and the target. That is, the sentiment depends on both the target and the aspect. Moreover, the target of the last example can be given implicitly; i.e., it is assigned NULL and does not contain any word in the sentence [42]. A review of a product or service can express sentiment towards various aspects. For example, a restaurant review can push positively about the food but express anger towards the quality of service. There is now a growing amount of work in detecting aspects of products and also sentiment towards these aspects [10; 17].
## 2 Definition of Lane Changing for a Platoon  
The typical lane-changing scenario of a platoon is described in this section. As shown in Figure 1, the platoon is the series of vehicles in red, \(LV\) is the leading vehicle, \(MV\) is the middle vehicle, and \(RV\) is the rear vehicle in the subject platoon. The platoon is directly related to the spatial position and motion states of vehicles in yellow, black, green, and purple. The yellow vehicle is the following vehicle in the current lane \(FV\), and the black vehicle is the preceding vehicle in the current lane \(PV\). Meanwhile, the green and purple vehicles are the following vehicle \(TFV\) and the preceding vehicle \(TPV\) in the target lane.  
There are many types of crash accidents, including lateral scuffing, a side crash, and a rear-end crash. When the platoon is changing lanes, the last two crash forms are possible between platoon vehicles and \(TFV/TPV/PV\), which is shown in Figure 2.  
Based on the above analysis of the lane-changing scenario, vehicles with a great impact on the safety of a lane-changing platoon were analyzed. That is, the front and rear vehicles in the current lane and the target lane. However, the vehicle behind usually decides its actions according to the motion state of the vehicle in front; therefore, the vehicle \(FV\) has no effect on the platoon.  
The platoon vehicles and non-platoon vehicles may be involved in crashes as follows. Rear-end collisions could happen between platoon vehicles \(LV/MV/TV\) with the following vehicle in the target lane \(TFV\), as shown in Figure 2a,e; between platoon vehicles \(LV/MV/TV\) with the preceding vehicle in the same lane \(PV\), as shown in Figure 2d,h; and between the leading vehicle \(LV\) and the preceding vehicle in the target lane \(TPV\), as shown in Figure 2b. A side crash could happen between the leading vehicle \(LV\) and the preceding vehicle in the target lane \(TPV\), as shown in Figure 2c. During the platoon-lane-changing process, rear collisions and side crashes may occur between vehicles in the platoon, as shown in Figure 2f,g. Many researchers have studied collisions between non-platoon vehicles. Thus, this study focused on avoiding the two kinds of conflicts in Figure 2f,g, which occur with intra-platoon vehicles.  
In addition, this study only focused on the case of lane changing from the fast lane to the slow lane for analysis; the contrary lane changing is similar to this case and is omitted because of space limitation.  
Figure 1: The typical lane-changing scenario of the platoon.  
Figure 2: Eight types of crash accidents in the platoon-lane-changing scenario.
## 4 Numerical Simulations
### Constitutive Model  
The Perzyna viscoplastic material model, when combined with the kinematic hardening rule, gives the rate of accumulated plastic strain (\(\dot{p}\)). All experiments were carried out in the tensile mode, thus the constitutive model is described for the uniaxial loading case (pure tension). The value of the accumulated plastic strain rate is calculated as follows [30]:  
\[\dot{p}=\
u\bigg{(}\frac{\sigma-\alpha}{\sigma_{y}}-1\bigg{)}^{1/m}, \tag{1}\]  
Figure 6: Creep test of alloy (**a**) SAC305 and (**b**) SACX0807, hourglass specimen, temperature 22 \({}^{\circ}\)C, axial force 1430 N.  
Figure 7: Room temperature creep strain rates for SAC305 (**a**) and SACX0807 (**b**) solder, logarithmic scale (comparison of different samples for few selected points).  
where \(\alpha\) is a kinematic stress (\"backstress\"), \(\
u\) is the viscosity of the material, \(m\) is the hardening exponent, \(\sigma_{y}\) is the initial yield strength of the material and \(\sigma\) is the value of the axial stress. It is clear from Equation (1) that the model is not dependent on temperature and if it is used, then it must be calibrated for each temperature separately.  
The backstress is given by a superposition of \(M\) parts (Chaboche kinematic hardening model [37]) and the analytical solution of monotonic tensile loading is expressed as follows:  
\[\alpha=C_{M}\,\varepsilon_{p}+\sum_{i=1}^{M-1}\frac{C_{i}}{\gamma_{i}}\,\left( 1-e^{-\gamma_{i}\,\varepsilon_{p}}\right), \tag{2}\]  
where \(\varepsilon_{p}\) is the plastic strain and \(C_{i}\), \(\gamma_{i}\) are material parameters. If the cyclic creep of the material does not occur, the last superposition parameter \(\gamma_{M}=0\) is chosen; therefore, the linear term \(C_{M}\,\varepsilon_{p}\) appears in Equation (2). In this study, three backstress parts are considered (\(M=3\)).  
In combination with a nonlinear kinematic hardening rule, the Perzyna model can only capture the primary creep. Therefore, it is necessary to add a suitable creep model to describe the secondary creep. The Norton model [31] appears to be suitable, and for the case of a constant test temperature, the rate of creep strain can be expressed as a power function of stress, as follows:  
\[\dot{\varepsilon}_{\sigma}=C_{4}\,\sigma^{C_{5}}, \tag{3}\]  
where \(C_{4}\) and \(C_{5}\) are the material parameters of the Norton model. We must identify [PERSON]'s modulus, yield strength, two parameters for Perzyna model, \(2M-1\) for Chaboche model and two parameters for the [PERSON] model.
# Real Image Deblurring Based on Implicit Degradation Representations and Reblur Estimation
## 3 Proposed Method
### Loss Function  
In order to constrain the similarity between the reblurred image \(y_{re}\) obtained by the reblurring process and the original real blurred image \(y\) so that they are as consistent as possible, this paper not only uses the \(\mathcal{L}_{2}\) loss function to constrain the similarity at the low-level pixel level, but also uses the perceptual loss function to constrain the similarity of the high-level abstract features. Specifically, for the reblurring process, the loss function \(\mathcal{L}_{re}\) is defined as follows:  
\[\begin{cases}\mathcal{L}_{2}=\|y-y_{re}\|_{2}^{2}\\ \mathcal{L}_{per}=\text{{perceptual}}(y,y_{re})\\ \mathcal{L}_{re}=\mathcal{L}_{2}+\mathcal{L}_{per}\end{cases} \tag{7}\]  
where \(\text{{perceptual}}(\cdot)\) is the perceptual loss function [34], expressed as  
\[\mathcal{L}_{per}=\frac{1}{WHC}\sqrt{\sum_{x=1}^{W}\sum_{y=1}^{H}\sum_{c=1}^{ C}(\Phi_{x,y,c}^{l}(y)-\Phi_{x,y,c}^{l}(y_{re}))^{2}} \tag{8}\]  
where \(\Phi_{x,y,c}^{l}(\cdot)\) denotes the output features of the classifier network from the \(l\)-th layer, \(C\) is the number of channels in the \(l\)-th layer, and \(W\) and \(H\) denote the width and height of the image, respectively. Instead of directly comparing the values of each pixel, the perceptual loss function compares the differences in the high-level feature space, as in deep networkstrained for classification tasks (e.g., VGG19 [35]). For the deblurring process, apart from using the \(\mathcal{L}_{2}\) loss function to calculate the difference in pixel values between the deblurred image \(x_{de}\) and real sharp image \(x\), the Structural SIMilarity (SSIM) loss function is used to calculate differences in structure, using the loss function \(\mathcal{L}_{de}\) as follows:  
\[\begin{cases}\mathcal{L}_{2}=\|x-x_{de}\|_{2}^{2}\\ \mathcal{L}_{\text{ssim}}=1-ssim(x,x_{de})\\ \mathcal{L}_{de}=\mathcal{L}_{2}+\mathcal{L}_{\text{ssim}}\end{cases} \tag{9}\]  
where \(ssim(\cdot)\) refers to the SSIM loss function [36], with the expression shown as  
\[ssim(x,y)=\frac{(2\mu_{x}\mu_{y}+C_{1})(2\sigma_{xy}+C_{2})}{(\mu_{x}^{2}\mu_{ y}^{2}+C_{1})(\sigma_{x}^{2}\sigma_{y}^{2}+C_{2})} \tag{10}\]  
where \(\mu_{x}\) and \(\mu_{y}\) denote the mean value of image \(x\) and \(y\), respectively, \(\sigma_{x}^{2}\) and \(\sigma_{y}^{2}\) denote the variance of image \(x\) and \(y\), respectively, \(\sigma_{xy}\) is the covariance between the two, and \(C_{1}\) and \(C_{2}\) are very small constants used to maintain stability. In summary, the loss function used by IDR\({}^{2}\)ENet is  
\[\mathcal{L}_{\text{IDR}^{2}\text{ENet}}=\lambda\mathcal{L}_{re}+\mathcal{L}_{de} \tag{11}\]  
where \(\lambda\) denotes the regularization factor between \(\mathcal{L}_{re}\) and \(\mathcal{L}_{de}\).
## 2 Biosensors in Patient Health Monitoring
### Diverse Biosensors Enable Comprehensive Health Monitoring
#### 2.2.1 ECG Biosensors  
Overview:  
In the realm of biosensors, ECG biosensors, particularly those represented by cutting-edge technologies like the AD8232 shown in Figure 2, hold a distinctive position. These biosensors are meticulously engineered for continuously monitoring the heart's electrical activity, providing invaluable insights into cardiovascular health [25].  
Principle of Operation:  
The operational principle of ECG biosensors revolves around the configuration of the electrodes (Figure 3) that are strategically placed on the patient's chest. These electrodes serve as the front-line components capturing the intricate electrical impulses generated with each heartbeat. The captured signals undergo a transformative process, resulting in graphical ECGs [26]. This graphical representation of the heart's rhythm serves as a diagnostic tool for identifying various cardiac conditions.  
Specifications:  
* Electrode configuration: Multiple electrodes, strategically positioned on the chest, ensure comprehensive coverage and accurate signal capture.
* Frequency range: Operating within a specific frequency range is paramount for faithful representations of heart signals.
* Signal resolution: High resolution is a critical specification, ensuring that the biosensor can precisely interpret the complex patterns inherent in ECGs. Features:
* Fully integrated single-lead ECG front-end: All-in-one ECG front-end functionality for simplified integration.
* Common-mode rejection ratio (CMRR): A high CMRR of 80 dB (dc to 60 Hz) ensures precise signal acquisition.
* Versatile electrode configurations: Supports two- or three-electrode configurations for adaptable monitoring setups.
* Qualified for automated applications: Certified and designed for reliability in automated applications.
* Single-supply operation: Operates seamlessly on a single supply voltage ranging from 2.0 V to 3.5 V.
* Fast restore: This feature enhances filter settling for improved signal quality.  
Figure 3: Electrodes of the AD8232 biosensor are placed on the patient’s body.  
Figure 2: The AD8232 biosensor is a compact, single-lead, front-end empowering monitor of ECG signals.  
* Compact size: Dimensions are 3.5 cm \(\times\) 3 cm for space-efficient and portable applications. Patient Monitoring Aspects: In the patient monitoring landscape, ECG biosensors have emerged as indispensable tools. Their real-time capabilities facilitate continuous cardiac monitoring, enabling the prompt detection of irregularities in the heart rhythm. This real-time monitoring proves instrumental in early intervention, ultimately mitigating the risk of adverse cardiac events.
## 2 Materials and Methods
### Acoustic Emission  
In total, eight AE sensors were mounted at the bottom TRC skin in a configuration illustrated in Figure 1d (red-colored spots). Vaseline was used to ensure the optimal surface contact with the TRC, and the sensors were supported by a magnetic holder. Resonant R15 sensors (150 kHz) were provided by Physical Acoustics and connected to a 40 dB pre-amplification system. AE continuously monitored the damage activity, both at the loading and pausing cycles, and the time periods corresponding to each loading cycle were manually tracked. The AE data were collected and stored by the AEwin software and post-processed by the Noesis software. The amplitude threshold was set at 35 dB, a value fixed considering ambient noise and vibrations/sounds from the deposition of the bags. Wave source planar localization was based on the triangulation algorithms of the AEwin software. Due to the limited TRC thickness, the in-depth accuracy of the localization analysis was questioned, and therefore the AE events were projected in a planar view covering half of the TRC bottom side. The TRC wave propagation velocity was measured at the intact stage, averaging at 3000 m/s. Further details on the AE setup are given in Table 2.
# A Comparison of Segmentation Methods for Semantic OctoMap Generation
## 2 Related Work  
### Semantic Segmentation  
Semantic segmentation has been successfully deployed across multiple domains. In agriculture, it enables automated monitoring of crop health [18; 19]; in remote sensing, pixel-level classification of satellite and aerial imagery provides accurate land-cover mapping and change detection [20; 21]; in medical imaging, it supports automated diagnostic assessment by delivering precise voxel-level delineations of organs, tumors, and other pathological structures in MRI and CT scans [22; 23]; and in autonomous driving, real-time scene understanding enhances vehicle perception and obstacle avoidance [24; 25].  
Fully Convolutional Networks (FCNs) first pioneered end-to-end per-pixel segmentation by replacing dense classifiers with learned upsampling layers. U-Net [10] enhanced boundary accuracy with a symmetric encoder-decoder architecture and skip connections. DeepLabv3+ [9] introduced an Xception backbone combined with Atrous Spatial PyramidPooling (ASPP) for multi-scale context aggregation and a lightweight decoder to maintain resolution. Feature Pyramid Networks (FPN) [11] fused low-resolution semantic features with high-resolution spatial details using a top-down pathway and lateral connections. SegFormer [8] demonstrated that a hierarchical Vision Transformer encoder, combined with a lightweight MLP decoder, can match or surpass convolutional models in performance. In [26], a multiscale double branch U2 Net architecture was proposed consisting of a spatial U-Net and a spectral U-Net for image fusion tasks that enable effective learning in a hierarchical manner. In the automotive domain, the most recent architectures focus on multimodal fusion, and hybrid convolution-transformer backbones to maximize mIoU while preserving real-time speeds. For example, hybrid models such as FFTNet [27] combine lightweight convolutional encoders with transformer backbones that include Feature Alignment Modules (FAMs) and Pyramid Compression Pooling Modules (PCPMs), allowing them to reach over 82.5% mIoU on the Cityscapes validation set while still running in real time.
## 2 Model Construction  
### Markov Decision Process  
The classic Markov Decision Process (MDP) is composed of a five-tuple: \(<S,A,P,R,\gamma>\). Here, \(S\) represents the state space; \(A\) denotes the action space; \(P\) is the state transition probability function, expressed as \(P:S\times A\to S\), representing the probability of transitioning to the next state \(s^{\prime}\) after taking an action in a given state \(s\); R is the reward function, expressed as \(R:S\times A\times\to R\), indicating the feedback received after executing an action in a given state \(s\); \(\gamma\) is the discount factor, representing the degree to which recent feedback influences future rewards, with \(\gamma\in[0,1]\). Intersection signal control, as a traffic management strategy, can optimize traffic flow and reduce congestion. By adjusting signal timing through an intelligent signal system, intersections can be managed flexibly according to real-time traffic flow, thereby optimizing intersection performance. When addressing the signal control problem, it can be regarded as an MDP.  
In DRL-based traffic signal control, the agent is represented by the traffic signal lights, and the environment corresponds to the road traffic conditions. In the decision-making process, the agent uses the input state to select appropriate actions and receives rewards as feedback from the environment. By designing suitable algorithms for the agent and training it to interact with the environment, the system can optimize signal timing and enable the agent to autonomously learn eco-friendly control strategies. The overall framework is illustrated in Figure 1.