esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Validation of the U-Smile Method
### Balanced Binary Classification  
In the first study, we introduced the U-smile method as a novel method for evaluating the added predictive value of a candidate predictor [6]. We built logistic regression models using real-world data from the Heart Disease dataset, available from the UCI Machine Learning Repository [47, 48, 49], as well as synthetic data generated from theoretical probability distributions under informative and non-informative scenarios, as well as under dependent and independent scenarios.  
A key finding was that the U-smile method consistently and reliably distinguished informative variables from non-informative ones. When informative variables were added to the reference model, the prediction improvement was reflected in double smile shapes of their U-smile plots. In contrast, non-informative variables generated flat or irregular shapes of their U-smile plots, indicating no meaningful improvement. Additionally, the U-smile method was robust to multicollinearity between variables included in the models. Compared to traditional evaluation metrics, such as the LRT and \(\Delta\)AUC, the U-smile method provided more granular insights by assessing the effect of a new variable on prediction improvement and worsening separately for events and non-events. Moreover, the U-smile method did not produce any false-positive or false-negative results for synthetic data.  
Also, we showed that the U-smile method's numerical coefficients are closely related to established measures. Namely, using specific weights, \(\Delta\)BS can be expressed in terms of the BA coefficients, the BSS in terms of the RB coefficients, and the I coefficient is derived from the NRI. The measures, combined with the graphical summary of the U-smile plot, enabled a comprehensive and intuitive evaluation of candidate variable usefulness under a balanced scenario.
# Enhanced Conjunction Assessment in LEO: A Hybrid Monte Carlo and Spline-Based Method Using TLE Data
## 1 Introduction
Although Monte Carlo simulations provide improved accuracy by capturing non-linear dynamics through statistical sampling, they typically demand significant computational resources and depend on accurate modeling of initial uncertainty distributions. This trade-off between accuracy and efficiency is well-documented in recent studies [12]. The increasing dynamic complexity of the orbital  
Figure 1: Historical analysis of cataloged space objects (1957–2025). environment renders static risk models inadequate. Therefore, there is a clear need for adaptive, data-driven, and computationally scalable methods that improve upon traditional techniques. As the frequency of potential conjunctions increases, traditional deterministic or linearized approaches struggle to provide reliable risk assessments. Nonlinear effects, non-Gaussian uncertainty distributions, and limited data availability necessitate the use of hybrid frameworks that combine statistical sampling, high-resolution temporal modeling, and flexible uncertainty representation. Several recent studies have demonstrated the advantages of such data-driven and adaptive approaches in operational conjunction analysis [13]. In response to these challenges, this study proposes a hybrid framework that combines Monte Carlo simulation, spline-based temporal refinement, and a multi-stage TCA estimation process. This approach aims to enhance the precision of collision probability assessments derived from TLE data while maintaining operational feasibility. The framework is validated through extensive real-world testing, demonstrating improved accuracy in both time and distance estimates, along with acceptable computational costs. Section 3 provides validation results utilizing TLE catalogs and benchmarks against empirical collision scenarios, concluding with Section 4.
## 3 Analysis of Bibliographic Results on Road Network Pricing
### Co-Citation Analysis
#### 3.4.2 Acceptability  
Acceptance of road pricing schemes is critical for their successful deployment and can be a major impediment to deployment [83]. In almost every city where road pricing was implemented, governments faced public resistance [55]. Edinburgh's cordon pricing scheme was rejected by around 75 percent of the voters [41]. Manchester's proposal was also rejected by around 79 percent of the voters [84]. Birmingham faced strong opposition to a proposed pricing scheme [38]. Numerous factors influence public acceptance, including privacy [17], equity [10], scheme understanding [3], complexity of implementation [85], distrust in governments [86], and uncertainty about benefits [47].  
The privacy concern is related to sharing personal information in automated payments; camera enforcement systems that are perceived as a major risk for illegal access to personal information; and infringements of privacy [85]. In cities where these concerns were adequately addressed, genuine efforts were made, and appropriate measures were put in place to deal with them [54].  
The equity concerns are related to impacts of road pricing on low-income and vulnerable groups who may not be well-served by reliable public transport [87]. These issues areaddressed by ensuring equal access to public transport and system design that does not discriminate against people with disability [62].  
Another concern with public acceptance is perceived complexity of pricing schemes. Information campaigns about how they work can help increase awareness [86]. Additionally, uncertainty about a scheme's effectiveness in reducing congestion and revenue allocation can reduce public confidence [17]. This can be addressed when generated revenue is directed to improve public and active transport initiatives [88]. [PERSON] and [PERSON] considered factors influencing public support and found the studied schemes generally gained support after deployment [38]. In a similar study by [PERSON], it was shown that public support for Stockholm's congestion charging reached 70 percent after trial [55; 86]. Another impacting factor is not taking a holistic view of transport taxation or political acceptance [3; 16; 89]. Finally, results of a comparative study by [PERSON] et al. revealed that acceptance of congestion pricing differs between cities and countries. Prior knowledge about road pricing was a major factor affecting public acceptance of proposed schemes in cities such as Tunis, Ulaanbaatar, and Damascus; this was contrary to findings for other cities such as Vienna, Athens, Como, and Dresden, where similar schemes were not popular [41].
###### Abstract  
High-voltage unipolar square wave pulsed electric fields (PEFs) can cause cell membrane rupture and cell death during a process termed irreversible electroporation (IRE). PEF effects are influenced by pulse parameters like number of pulses (NP), voltage (PV), width (PW), and interval (PI). This study systematically evaluates their effects on the conductivity and relative conductivity changes between untreated and PEF-treated regions of potato tissue across a frequency range of 1 Hz to 5 MHz by means of electrochemical impedance spectroscopy (EIS), using a custom-made four-point EIS probe with RG58/U coaxial cables. Potatoes were chosen as a plant-based PEF model to reduce animal experiments and untreated tissue showed minimal conductivity variation across regions. Relative conductivity changes were maximal at 1000 Hz. At 1000 Hz, significant conductivity differences between untreated and PEF-treated regions were observed from PV = 200 V, NP = 10, PW = 10 us, and PI = 50 ms onwards (most significant changes occurred for PV = 700 V; NP = 70; PW = 70 us; PI = 250 ms and 500 ms). Our results may be beneficial for multiphysics modelling of IRE with specific electrical properties, conductivity mapping with optimal contrast--such as in electrical impedance tomography--and development of IRE procedures.  
irreversible electroporation (IRE); pulsed electric fields (PEFs); electrochemical impedance spectroscopy; electrical conductivity; electrical conductivity parameter study Article
###### Abstract
Note that, due to the limitations of the previous approaches regarding transmission power systems, here, we adopt the GAMS optimization package and the BONMIN solver to solve the problem of the optimal placement and sizing of STATCOMs in electrical networks independent of the grid configuration, i.e., radial or meshed or with multiple generation sources. This can be considered as one of the main contributions of this work. Note that the contribution of this research is focused on proposing an exact MINLP to represent the problem of the optimal siting and sizing of STATCOMs in power systems operated with high- and medium-voltages, i.e., transmission meshed networks and radial distribution ones with a unique and reliable optimization model. This implies that the usage of the GAMS software and the BONMIN package is not the sole contribution. However, these tools allow one to focus on the mathematical formulation, not the optimization method as is recurrently done in the literature when metaheuristic methods are proposed. This is an advantage when the interest is to provide efficient and accurate optimization models for engineering problems. The remainder of this manuscript is structured as follows. Section 2 presents the MINLP formulation for the problem of the optimal siting and sizing of STATCOMs in power systems. Section 3 presents the main aspects of the solution methodology based on the GAMS optimization tool. Section 4 shows the main characteristics of the test systems composed of 14 and 30 nodes. Section 5 describes the main numerical results and analyzes and discusses them. Section 6 presents the conclusions derived from this study.
## 3 Results and Discussion
### Verification of the Models  
The validity of the predictive model was confirmed by comparing the predicted and the experimental values at optimal conditions. The values predicted by the model at optimal conditions were 24.54, 18.48, and 23.53 \(\mathrm{\SIUnitSymbolMicro g}\) per g of dry parsley for luteolin, caffeic acid, and apigenin, respectively, and the actual experimental values were 23.92 \(\pm\) 1.86, 19.10 \(\pm\) 0.75 and 25.29 \(\pm\) 1.82 \(\mathrm{\SIUnitSymbolMicro g}\)/g dry parsley. No significant differences were found between the predicted and the actual values (\(p\) > 0.05), indicating a high accuracy of response optimization.  
The desirability value may define the ideal solution's degree of precision. The closer the desirability value is to 1, the greater the optimization precision. Therefore, the model validation and response values are not substantially different from the predictions under ideal circumstances [29].
# Device Centric Throughput and QoS Optimization for IoTsin a Smart Building Using CRN-Techniques
## 2 System Model
### Traffic Classes of SUs  
We classify the SUs (i.e., IoT devices, mobile users and NMs) into six different classes based on their QoS requirements, as shown in Table 2. The video IoT devices and video mobile users are considered in different classes due to different demands in terms of BER and the stability index of the channel (SoC). The smoke or fire detectors and ventilation control IoT devices need a small amount of data, but need more reliability and stability of the channel for real-time communication. The NM represents a real-time building monitoring class, which requires highly stable and reliable channels. For clarity, we use the letter Ibefore IoT traffic class (e.g., Ivvideo represents the video class for IoT devices). Similarly, the requirements of other classes are highlighted in Table 2[47; 48].
## 3 Materials and Methods
### Datasets
#### 3.1.1 60 frames  
A camera Basler acA2500-14 uc USB 3.0 equipped with C125-0418-5M-P f4 mm lens was used to generate instances for this dataset. Each frame of the original video was displayed in the television's screen and captured by the camera before changing to the next frame. Taking into account the fact that there were originally 2399 frames, 47,980 frames were collected for each class, since 10 camera positions were used for each of the two devices (32-inch and 43-inch screen size). The original resolution of the captured images is \(2590\times 1942\). However, the following preprocessing steps were conducted: crop--to only preserve the pixels inside the screen, obtaining an ROI with roughly \(1152\times 824\) pixels; and resize to \(224\times 224\). Only one type of anomaly (mosaic) was simulated before the instances' capture. Each capture alternated between original frames and frames with the simulated mosaic so that later on, while constructing the videos, it was possible to insert other defects in any desired position, allowing the full control of where the anomalies would be placed and how long they would last.  
Defective frames with mosaic were simulated using the glitch-this module, version 1.0.2, randomly alternating the glitches' intensity between levels 2 and 10. Freezes, by adding multiple copies of a given frame, and darkened frames are examples of additional defects simulated after the frames' capture. In addition, the defects' duration was randomly chosen according to a normal distribution with a mean of 12 and standard deviation of 4, to determines the number of defective frames for each anomaly event. Finally, the produced frames provided 6240 video segments--among which precisely 4680 are anomalous instances (75%) and 1560 are normal (25%). Each video is composed of 60 frames, in order to increase the amount of examples for each class, and the last 30 frames of each sample overlap with the first 30 samples of the next one. As a consequence, the dataset was partitioned into training, validation and test sets, taking the frames shared by different videos of the same camera position into account so that all instances collected with a given camera position were placed in the same partition.
# A Single-Launch Deployment Strategy for Lunar Constellations
## 4 Analysis of the Constellation Deployment
### Application of the Deployment Strategy
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Element** & **Sat-1** & **Sat-2** & **Sat-3** & **Sat-4** & **Sat-5** \\ \hline \(x_{er}\) & 909 km & 859 km & 908 km & 908 km & 209 km \\ \(y_{er}\) & 2719 km & 2706 km & 2719 km & 2719 km & 2542 km \\ \(z_{er}\) & 3073 km & 3070 km & 3072 km & 3072 km & 3035 km \\ \(v_{x,er}\) & \(-239.0\) m/s & \(-239.2\) m/s & \(-238.6\) m/s & \(-238.6\) m/s & \(-239.4\) m/s \\ \(v_{y,er}\) & \(-62.2\) m/s & \(-61.9\) m/s & \(-62.1\) m/s & \(-62.1\) m/s & \(-58.8\) m/s \\ \(v_{z,er}\) & \(-12.7\) m/s & \(-12.7\) m/s & \(-12.7\) m/s & \(-12.7\) m/s & \(-13.0\) m/s \\ \(\Delta\
u_{x,er}\) & \(1.8\) m/s & \(263.3\) m/s & 308.3 m/s & 239.4 m/s & 144.2 m/s \\ \(\Delta\
u_{y,er}\) & \(252.9\) m/s & \(84.8\) m/s & \(97.8\) m/s & \(70\) m/s & 482.8 m/s \\ \(\Delta\
u_{z,er}\) & \(-68.2\) m/s & \(-87.1\) m/s & \(-72.5\) m/s & \(-22.6\) m/s & \(-143.0\) m/s \\ \hline \hline \end{tabular}
\end{table} 
Table 5: State variables in the equilibrium region before and after the maneuver. Figure 1: Spacecraft trajectory during Phase 1.
# Optimization of Effective Throughput in NOMA-Based Cognitive UAV Short-Packet Communication
## 3 Solutions of the Formulated Problems
### Joint Optimization and Analysis  
Based on the above analysis, we propose a joint optimization algorithm to obtain the optimal solution of (12). Details of the joint optimization algorithm are given in Algorithm 1.  
In Algorithm 1, the optimal sensing time is derived for the given information bits and power allocation. Then, the optimal information bits are derived for the given sensing time and power allocation. Then, the optimal power allocation is derived for the given sensing time and information bits. Finally, a joint optimization algorithm is used to obtain these optimal system parameters.  
The complexity analysis of Algorithm 1 is as follows. The complexity of obtaining the optimal sensing time in step 3 of Algorithm 1 is \(\mathcal{Q}_{1}=\mathsf{O}\big{(}\log_{2}(N/\delta)\big{)}\). The complexity of obtaining the optimal information bits in step 5 of Algorithm 1 is \(\mathcal{Q}_{2}=\mathsf{O}\big{(}\log_{2}(I_{\max}/\delta)\big{)}\), where \(\delta\) is the error tolerance of the bisection method. In step 6 of Algorithm 1, the complexity of obtaining the optimal power allocation is \(\mathcal{Q}_{3}=\mathsf{O}\big{(}(p_{1}^{ub}-p_{1}^{lb})/\theta\big{)}\). Therefore, the complexity of Algorithm 1 is \(\mathsf{O}\big{(}t_{\max}(\mathcal{Q}_{1}+\mathcal{Q}_{2}+\mathcal{Q}_{3}) \big{)}\), where \(t_{\max}\) is the maximum number of iterations of Algorithm 1. In the simulations, the algorithm usually converges within ten iterations. By using a similar analysis, the complexity of the exhaustive search algorithm is \(\mathsf{O}\big{(}NI_{\max}P/\sigma\big{)}\), where \(\sigma\) is the error tolerance of the exhaustive search algorithm. The complexity of Algorithm 1 is significantly lower than that of the exhaustive search algorithm. From the simulation results, it can be seen that the performance of Algorithm 1 is almost the same as the exhaustive search algorithm.  
```
1:Initialize \(N_{s}^{(0)}\),\(I^{(0)}\),\(p_{1}^{(0)}\),\(p_{2}^{(0)}\), iteration index \(t=0\), maximum iteration times \(t_{\max}\), error tolerance \(\zeta\);
2:repeat
3: With the given \(I^{(t)}\),\(p_{1}^{(t)}\),\(p_{2}^{(t)}\), find the optimal \(N_{s}^{*}\) using the bisection method;
4:\(c_{1}=\lfloor N_{s}^{*}\rfloor\), \(c_{2}=\lceil N_{s}^{*}\rceil\), \(N_{s}^{(t+1)}=\underset{(c_{1},c_{2})}{\text{arg max}}\,\Phi_{2}\);
5: With the given \(N_{s}^{(t+1)}\),\(p_{1}^{(t)}\),\(p_{2}^{(t)}\) calculate the optimal \(I^{(t+1)}\) using the bisection method;
6: With the given \(N_{s}^{(t+1)}\),\(I^{(t+1)}\) derive the optimal \(p_{1}^{(t+1)}\) using the one-dimensional line search algorithm, and then \(p_{2}^{(t+1)}=P-p_{1}^{(t+1)}\);
7: Set \(t=t+1\);
8:until (\(t\geq t_{\max}\))&&(\(\left|\Phi_{2}^{(t)}-\Phi_{2}^{(t-1)}\right|\leq\zeta\));
9:\(N_{s}^{(t)}\),\(I^{(t)}\),\(p_{1}^{(t)}\),\(p_{2}^{(t)}\),\(\Phi_{2}^{(t)}\).
```  
**Algorithm 1** Proposed Algorithm for Problem (12)
# SAR Target Incremental Recognition Based on Hybrid Loss Function and Class-Bias Correction
## 4 Experiments and Discussions
### Experiment Dataset  
The dataset samples used in the experiment of this paper are from the MSTAR co-database. The dataset is obtained from a cluster-beam SAR with a resolution of \(0.3\text{ m}\times 0.3\text{ m}\), which operates in X-band and HH polarization. The dataset contains 10 classes of military targets, such as armored vehicles, tanks, rocket launchers, etc. Figure 3 shows the SAR images of 10 classes of targets and their corresponding optical image training examples. According to the official recommendations given by the MSTAR dataset, the samples with a depression angle of \(17^{\circ}\) are selected as the training dataset, and the samples with a depression angle of \(15^{\circ}\) are selected as the test dataset in this experiment.  
Figure 3: Optical image (**top**) and SAR image (**bottom**) of 10 types of targets.
## 2 Results  
In this paper, we tested the efficiency of silver-nitrate solution on 125 strains isolated from patients with leg ulcers. The tested strains were predominantly _Staphylococcus_ spp. (47%) (_Staphylococcus aureus_, _S. chromogenes_, _S. xylosus_, _S. varnerii_, _S. haemolyticus_, _S. sciri_, _S. epidermidis_, _S. hominis_) and _Enterobacteriaceae_ (17%) (_Enterobacter aerogenes_, _E. intermedius_, _Serratia marcescens_, _S. odorifera_, _Klebsiella pneumoniae/thinoscleromatis_), followed by _Enterococcus_ spp. (13%) (_E. faecalis_, _E. ficecium_), non- _Enterobacteriaceae_ (12%) _Chryseomonas luteo_, _Pseudomonas aeruginosa_, _Acaligenes faecalis_, _Burkholderia cepacia_) and streptococci (11%) (_Lactococcus lactis_, _Aerococccus viridans_, _Streptococcus uberis_).  
Qualitative Screening of the Spectrum of Sensitivity of Different Microbial Strains to Silver-Nitrate Solution  
The qualitative screening of antiseptic activity was performed on all isolates and was represented as the average value of the growth-inhibition diameter obtained for the individual strains belonging to _Staphylococcus_ spp., _Enterobacteriaceae_, _Enterococcus_ spp., non-_Enterobacteriaceae_ and streptococci.  
Our results showed that the 1/6000 silver-nitrate solution exhibited the best inhibitory effect against the growth of _Staphylococcus_ spp. strains (as revealed by the highest growth-inhibition-zone diameters), while the most resistant proved to be the _Enterococcus_ spp. strains (Figure 1).
## 1 Introduction
The model involves a numerical formalism allowing for simulation of the displacements of frontal seismically-active blocks in the subduction zone during different phases of the seismic cycle, assuming that rear portion of the continental lithosphere (which corresponds to island arc) is a single structural element that is not separated into smaller blocks and does not experience its own horizontal displacements. However, the GPS-measured velocities along with geological and seismological data clearly indicate that the structure in the rear part of the subduction region also consists of separate segments divided by large faults rooted into the contact zone of interacting lithospheric plates [18,21]. Due to the relatively weak interaction between neighboring blocks, each of them can deform nearly independently of adjacent structural segments, while the overall deformation of the entire system may exhibit a very complex pattern. During seismic events, the rear blocks, similar to frontal ones, may experience almost instantaneous offsets towards the trench. Once the stress is released, a slow straightening of the rear blocks occurs in the postseismic period, partly restrained by the viscous friction at the bottom of the block due to the underlying asthenospheric layer. The above considerations indicate the need for modification of the original model [20] in order to take into account the discontinuities both in the frontal and rear parts of the island arc. An important motivation for model improvement is also the fact that geodetic observations of Earth's surface motions are mainly carried out onshore, that is, within the rear segment of subduction system, which troubles the direct interpretation of the available displacement data within the framework of the original single-segment model. A very brief analysis of the generalized model has been published in [22]. In this paper, we discuss the motivation for application of keyboard model in order to explain observed motion patterns in subduction regions, with primary focus on its generalization, leading to the modified two-segment keyboard model. We also present some preliminary simulation data calculated assuming elastic spring-like interaction between the frontal and rear segments.
# Deep Learning-Based Optical Camera Communication with a 2D MIMO-OOK Scheme for IoT Networks
## 3 System Architecture
### Anchor Detection  
Because Region of Interest (RoI) algorithms [27; 28] solve complex problems with high accuracy, they are frequently employed for object detection in OCC systems. Object-based and feature-based detection are the two principal methods that these algorithms usually employ [29; 30]. The feature-based technique finds likely pixels and groups them to create a return on investment. However, as not every pixel in optical images has strong distinguishing characteristics, this method's inability to reliably recognize a complete object is a disadvantage. On the other hand, by utilizing structural and shape-based information, the object-based approach provides enhanced RoI detection. Using an RoI technique, we locate anchors at the LED array's four corners in this study. The receiver can effectively detect these four outermost LEDs using the RoI method because they are always in the ON state. The locations of the other LEDs in the array may be quickly ascertained and decoded if the coordinates of these four reference points are known.