esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
### Classification of LiDAR Data  
Recent developments in the classification of LiDAR data for autonomous driving have been focusing on enhancing object detection and semantic segmentation through the use of deep learning and sensor fusion techniques. [PERSON] et al. [36] presented SalsaNet, i.e., a deep learning model developed for the efficient semantic segmentation of 3D LiDAR point clouds for autonomous driving. A bird-eye-view (BEV) projection is used to segment roads and vehicles using an encoder-decoder architecture. Moreover, they developed an automatic process for labeling LiDAR data by transfering labels from camera images to LiDAR point clouds. A multimodal 3D object detection framework based on LiDAR and camera data was developed by [PERSON] et al. [37]. They utilized a feature fusion network to improve detection accuracy in complex driving environments. [PERSON] et al. [38] developed a deep learning model for the classification of LiDAR point clouds, by using a convolutional neural network (CNN) to extract spatial features and enhance object recognition using LiDAR point clouds. The authors in [39] presented a lightweight semantic segmentation network optimized for embedded systems, enabling efficient inferences on hardware with constrained resources without compromising accuracy. There has been growing emphasis on integrating multi-modal data to improve the accuracy of classification. By combining LiDAR point clouds with RGB images, the FusionNet architecture enhances object recognition in complex environments by leveraging complementary information from both sources. In addition, transformer-based models have been explored for the classification of point clouds. Self-attention mechanisms are used to capture long-range dependencies within point clouds, which have been demonstrated to perform better than conventional algorithms on benchmark datasets [40]. We introduce a generative model-based technique for classifying static and dynamic tracks using LiDAR data for AV navigation that differs from existing approaches. In contrast to previous work that heavily relied on supervised learning and feature extraction, we use a combination of DBNs and interaction arrays to model the interactions between the ego vehicle and its surroundings.
## 3 Proposed Algorithm
### Preliminaries
#### 3.1.2 Skewness Coefficient  
The skewness coefficient is one way to measure the skewness of a distribution, a measure of a probability distribution's asymmetry. A distribution is said to be skewed if its curve is twisted either toward the left or the right. [PERSON]'s coefficient of skewness is the most significant measure of skewness. It is sometimes referred to as Pearson's skewness coefficient.  
When a dataset's skewness is measured, it typically takes the form of a bell curve. The skewness of normal distributions is zero. As a result, the distribution becomes symmetrical concerning the mean. Still, there are situations in which skewness is not symmetric. It can be either positive or negative in these circumstances.  
When a distribution's tail is more prominent on the right than the left, it is said to be positively skewed. The skewness coefficient is assumed to be positive since the distribution is positive. The majority of the values thus turn out to be to the left of the mean. This indicates that the values on the right side are the most extreme.  
Negative skewness, on the other hand, occurs when the tail is more pronounced on the left rather than the right side. Contrary to positive skewness, most of the values are found on the right side of the mean in negative skewness. As such, the most extreme values are found to be further to the left.  
Formula (2) describes how to calculate the skewness coefficient.  
\[\gamma=\frac{\frac{1}{n}\Sigma_{i=1}^{n}\big{(}x_{i}-\overline{X}\big{)}^{3}}{ \big{[}\frac{1}{n}\Sigma_{i=1}^{n}\big{(}x_{i}-\overline{X}\big{)}^{2}\big{]} ^{\frac{3}{2}}} \tag{2}\]  
In Formula (2), \(\overline{X}=\frac{1}{n}\Sigma_{i=1}^{n}\)\(x_{i}\) (sometimes expressed as \(EX\)).
## 2 Related Works  
### Medical Image Segmentation  
Recent studies have made significant progress and demonstrated commendable performance in advancing medical image segmentation algorithms. A comprehensive overview of current artificial intelligence studies pertaining to chest and breast CT image analysis is reviewed in [21; 22], including some existing medical image segmentation methods [23], probabilistic models for handling noise [24]; U-Net for semantic segmentation [25] and attention mechanism for localizing organs like the pancreas [26], attention-guided CNNs for localizing organs like the pancreas [27], output space adaptation to tackle domain shifts [28] and cross-domain X-ray segmentation by unsupervised adversarial networks [29]. In summary, current AI research on CT images covers areas such as segmentation, noise modeling, attention mechanisms, domain adaptation, and cross-modality learning. Methods leverage convolutional neural networks, probabilistic models, adversarial training, and more. There remain opportunities to further improve the accuracy, robustness, and generalization of these approaches.  
Our work proposes a transformer-powered framework that synergistically leverages both labeled annotations and unlabeled medical volumes concurrently by deeply interlinking their representations to enable seamless feature propagation, improving performance under constrained expert supervision and in clinical situations with limited annotated data.
## 4 Experimental Principles  
### LACGAN-Based Small-Sample Data-Augmentation Method  
#### 4.1.1 Auxiliary-Classifier Generative Adversarial Networks  
To address the issue of potential multi-type communication jamming in complex electromagnetic environments, this paper proposes a jamming data-augmentation method using a Layer-normalized Auxiliary-Classifier Generative Adversarial Network (LACGAN) to supplement samples that follow the same distribution as real samples. A traditional GAN is unsupervised learning and cannot generate interference samples of specific categories. The LACGAN is a multi-task learning method based on conditional Generative Adversarial Networks. It can utilize the auxiliary classifier in the discriminator to achieve precise control over the category attributes of generated samples. By combining adversarial loss and classification loss, the generator can synthesize realistic samples with specified features.  
The LACGAN inputs both the jamming signal \(x\) and the label \(l\) into \(G\) simultaneously. In \(D\), while determining whether the input sample is a real sample through the auxiliary classifier, it also accomplishes the classification task. When a single sample is input into the LACGAN, the discriminator will output two values: one is the adversarial loss \(L_{\mathrm{adv}}\) that measures the distribution difference between the generated sample and the real sample; the other is the classification loss \(L_{D}\) that distinguishes the category label of the generatedsample from the target jamming type. Similarly, the loss function of the LACGAN consists of the discriminative loss \(L_{\text{adv}}\) and the classification loss \(L_{D}\).  
The overall loss after summarization is as follows:  
\[L_{\text{adv}}=E_{x\sim P_{data}(x)}[\log(D_{ac}(x))]+E_{z\sim P_{z}(z)}[\log(1- D_{ac}(G(z,l)))] \tag{6}\]  
\[L_{cls}=E_{x\sim P_{data}(x)}[\log(L_{cls}(l_{x}|x))]+E_{z\sim P_{z}(z)}[\log L _{cls}(l|G(z,l)))], \tag{7}\]  
where \(D_{ac}(x)\) is the probability that \(D\) judges the input \(x\) as a real sample; \(D_{ac}(G(z,y))\) is the probability that the generated sample is judged as a real sample after passing through the discriminator; \(L_{cls}(l_{x}|x)\) represents the classification loss of \(D\) for \(x\), and \(l_{x}\) is the real label of \(x\); and \(L_{cls}(y|G(z,l))\) represents the classification loss for the generated sample.  
The overall loss after summarization is as follows:  
\[\max_{D}V(D,G)=L_{adv}+L_{D} \tag{8}\]  
\[\max_{G}V(D,G)=-L_{adv}+L_{D}. \tag{9}\]
# Optimized Passive Defense Measures via IR Imaging
## 3 Radiation Stability Analysis  
We are considering the radiation energy distribution function \(f(\lambda,T)\) as a real valued map \(f:\mathbb{R}^{2}\rightarrow\mathbb{R}\) as defined by the Equation (1). First of all, notice that the non-removable singularities of the function \(f(\lambda,T)\) are the points \((0,T),T>0\) and \((\lambda,0),\lambda>0\), since for any given \(a\in\mathbb{R}\), we have the following limiting radiating configurations  
\[\lim_{\lambda\to a,T\to T_{0}}f(\lambda,T) = \left\{\begin{array}  
where the associated coefficients \(\{B_{1},B_{2}\}\) arise linearly in \(s\) as  
\[B_{1} = s-2,\] \[B_{2} = s+2. \tag{12}\]  
The interrelation between pixel and temperature variations of the radiation energy \(f(\lambda,T)\) is designated via the mixed partial derivative  
\[\frac{\partial^{2}f}{\partial\lambda\partial T}=\frac{Ase^{s}}{ \lambda^{6}T(e^{s}-1)^{3}}\left(C_{1}e^{s}+C_{2}\right), \tag{13}\]  
where the coefficients \(\{C_{1},C_{2}\}\) can be cascaded as  
\[C_{1} = s-6,\] \[C_{2} = s+6. \tag{14}\]  
Note that for \(\{\lambda,T\}\) satisfying \(\lambda T=0.201405\), we have  
\[\frac{\partial^{2}f}{\partial\lambda^{2}}\left(\frac{0.201405}{T },T\right)\approx 15461.2T^{7}. \tag{15}\]  
Therefore, for a fixed \(T>0\), it implies that \(f(\lambda,T)\) attains its local minimum at the point \(\left(\frac{0.201405}{T},T\right)\). Similarly, for a fixed \(T<0\) it follows that \(f(\lambda,T)\) has a local maximum at the point \(\left(\frac{0.201405}{T},T\right).\)  
Replacing all the second derivatives concerning the pixel, glow capacities and their interrelation as in the Equations (9), (11) and (13), the discriminant  
\[D=\frac{\partial^{2}f}{\partial\lambda^{2}}\frac{\partial^{2}f}{ \partial T^{2}}-\left(\frac{\partial^{2}f}{\partial\lambda\partial T}\right) ^{2} \tag{16}\]  
leads to the following representation  
\[D = \frac{A^{2}s\epsilon^{s}}{\lambda^{12}T^{2}(e^{s}-1)^{6}}\left( \alpha e^{3s}+\beta e^{2s}+\gamma e^{s}+\delta\right), \tag{17}\]  
where the expansion coefficients \(\{\alpha,\beta,\gamma,\delta\}\) are given by  
\[\alpha = A_{1}B_{1}-sC_{1}^{2},\] \[\beta = A_{1}B_{2}+A_{2}B_{1}-2 sC_{1}C_{2},\] \[\gamma = A_{2}B_{2}+A_{3}B_{1}-sC_{2}^{2},\] \[\delta = A_{3}B_{2}. \tag{18}\]  
Here, the components \(\{A_{i},B_{i},C_{i}\}\) read as in Equations (10), (12) and (14) respectively.  
Following the above analysis, we provide a block diagram towards the classification of the stability ranges of the [PERSON]'s distribution law as in Figure 4.
## 4 Case Study Application: Towards an Efficient Terminal Container in the Port of Augusta
### Container Terminal Characteristics and Future Projects  
Even though the utilization of the container terminal in the port of Augusta is currently very low, it has huge development possibilities. The biggest advantage of the terminal are its exceptional conditions at the berth and the storage subsystem which makes the acceptance of larger ships possible. At the moment the infrastructural conditions are sufficient to enable the reception of ships that currently are calling at the port of Catania, nevertheless the terminal lacks of appropriate mechanization and good hinterland connections. The detailed characteristics are presented in Table 1, Figures 4 and 5.  
The terminal is currently connected to the hinterland by an arterial road linked to the A44 Highway, which gives access to the main logistic centres currently located close to the city of Catania. The port operational plan also outlines a future railway connection with the single rail track connecting the port to the railway logistic terminal Catania Bicocca.  
Infrastructure developments from the three-year port operational plan for 2019-2021 [28] outline an expansion strategy for the container terminal of the Port of Augusta (with a current size of about 100.000 m\({}^{2}\)), of which some work yards are already active; the expansion is structured in different phases and forecasts a total increase of about 116.000 m\({}^{2}\) (Figure 4).  
This would give the terminal the possibility to increase its capacity and obtain new services, including those with larger ships, which would give the port greater regional importance and the possibility to compete for wider hinterland area. In that case it could became a threat even for the port of Palermo of which traffic was in 2018 estimated at 15,962 TEU and at the moment represents the only competitor to the port of Catania in the container business.
## 2 Mathematical Formulation of OPF Problem  
The standard IEEE 30 bus system consists of twenty-four load buses and six generator buses to which thermal generators are connected. In the proposed modified system, three thermal generators are replaced by two WT and a SPV source to formulate OPF, which is an optimization problem with inequality and equality constraints. The generation at a bus should not violate the line capacity constraints while replacing RES; in this study, the same method of replacing thermal generators with RES has been adopted as in in reference [8; 46]. To forecast the output power of SPV and WT sources, lognormal and Weibull probability density functions (PDF) have been used, respectively. The application of the proposed HPSO-GWO algorithm is tested on the modified IEEE 30-bus system (hereafter test system) while considering different objective functions such as total generation cost, power losses, emissions, and cumulative voltage deviation in the system. The control parameters and other details of the test system under study are summarized in Table 1. Along with thermal generators, the adopted test system also includes SPV and WT, which are presented in Figure 1. The uncertain output power of wind and SPV sources must be compensated by the output power of thermal generators or reserve sources. The total generation cost of the entire system represents a combination of costs of all thermal generators, SPV and WT, plus reserve and penalty costs for overestimation and underestimation of wind and SPV sources, respectively. The subsequent sections explain cost function formulations for each type of source.  
Figure 1: Adapted IEEE 30 bus network with integrated WT and SPV sources.
# Spectrally-Selective Energy-Harvesting Solar Windows for Public Infrastructure Applications
## 3 Results
### Principal Results and Future Directions
This figure relates to the maximum electric output power expected in real conditions at 32\({}^{\circ}\) Sun altitude angle and would be increased by a further 7% or 8% if standard cell-surface temperature of 25 \({}^{\circ}\)C was used instead (for the NOCT of 40 \({}^{\circ}\)C) together with a temperature coefficient of power (\(-0.39\%/^{\circ}\)C). However, for the bus stop application, it is preferable to make measurements of the actual output power at realistic cell-surface temperatures, which are normally in excess of 40 \({}^{\circ}\)C in Australian summer conditions. Accounting for the sunlight incidence geometry, only about 84.8% of the total direct-irradiation flux cross-section was intercepted by the vertically-positioned windows for Sun altitude angle of 32\({}^{\circ}\). Therefore, we can generate an idealized conditions-based prediction for the maximum electric output rating of this PV wall (when illuminated by 1000 W/m\({}^{2}\) sunlight at normal incidence achieved by tilting the windows backwards until an optimum output is achieved). Therefore, P(max, est. for the optimally-tilted wall angle, at 1000 W/m\({}^{2}\)) = 102.8 W.  
Considering that the total wall area covered by the six framed windows was about 5.1 m\({}^{2}\) for a maximum predicted power output rating close to 102.8 W\({}_{\text{p}}\). These data correspond to the maximum electric output per unit optimally-angled PV wall area, which is up to 20.2 W/m\({}^{2}\). This figure is slightly less than 24 to 26 W/m\({}^{2}\) per unit of the framed window area measured with individual windows due to using conservatively-estimated weather-related data. The overall performance of a 6-window parallel-connected wall module was limited by its module fill factor (MFF), which is slightly smaller than the MFFs of individual (and unshaded) optimally-tilted windows. In all calculations, we also neglected the horizontal-orientation angle effects arising due to the geometric wall angle of the bus stop not being aligned perfectly with the Sun azimuth direction. As is evident from Figure 9 (taken in slightly cloudier conditions, but only about one hour after making the electrical output measurements), the 849 W/m\({}^{2}\) figure used in irradiation estimates was more than appropriate in terms of conservativeness. Making direct irradiation intensity measurements in field conditions simultaneously with the I-V curve measurements was not a perfect option at the time due to the cloud-coverage variations taking place even during the data-logging process duration. Several media outlets worldwide including ABC Australia have published reports on this trial bus-stop installation of solar windows in March 2017. Figure 9: Solar window-powered bus stop installed by Adshel, Inc. (Melbourne, Australia).
# Performance Analysis of Troposphere Scattering Communication Channel with Chirp-BOK Modulation
## 2 System Model with Chirp-BOK Modulation
### Tropocatter Channel Model
Assuming that the bandwidth of the low-pass input of the channel is limited tothe order of \(r/2\), and under the band-pass condition, it is equal to \(B=r\), where \(r\) represents the symbol rate. Therefore, the low-pass input signal is sampled at a minimum sampling rate of \(r\) points per second, with a sampling interval represented by \(T=1/r\), i.e., tap delay. With transmit signal \(\tilde{x}(t)\) input, the output \(\tilde{y}(t)\) of the troposcatter multipath channel with the tap delay line model (TDL) can be described as  
\[\tilde{y}(t)=T\sum_{n=0}^{N-1}\tilde{x}(t-nT)\tilde{c}(nT,t)=\sum_{n=0}^{N-1 }\tilde{x}(t-nT)\tilde{g}_{n}(t), \tag{7}\]  
where \(\tilde{g}_{n}(t)=T\tilde{c}(nT,t)\), \(N\) denotes the number of taps in the TDL model, i.e., the number of multipaths in the scattering channel. For the Rayleigh fading channel, \(\tilde{g}_{n}(t)\) is the tap gain, which is a complex Gaussian process with a zero mean [30]. Under the WSSUS assumption, the gain of each tap is uncorrelated, their power spectral density is determined by the Doppler spectrum, and the magnitude of the Doppler shift reflects the rate at which the tap coefficient changes. In consideration of the aforementioned characteristics of the scattering channel, and its multipath structure that is always consistent with the Rayleigh distribution, an equal-delay multipath Rayleigh fading model is employed for the simulation of non-anomalous propagation within the scattering channel. In this paper, the seven-path Rayleigh fading channel model with equal delay is adopted for the purposes of simulation analysis.
# A Status Evaluation of Rock Instability in Metal Mines Based on the SPA-IAHP-PCN Model
## 5 Conclusions  
The assessment of rock instability risks in metal mines requires a precise and comprehensive approach. In this study, a novel method based on the SPA-IAHP-PCN model was proposed to evaluate the instability state of rock masses, combining static and dynamic analyses for enhanced reliability. The method systematically integrates a multi-parameter risk assessment indicator system that incorporates cumulative event counts, microseismic energy, apparent stress, and seismic moments. These parameters provide complementary insights into the frequency, potential energy, and mechanical state of rock masses, forming a robust foundation for accurate risk quantification.  
To address uncertainties in weight determination, the IAHP method was employed to establish the preliminary weight ranges through expert judgment, which were further refined into precise values using the SPA method. The PCN framework complements these efforts by enabling trend prediction and comprehensive evaluations. A case study conducted in a lead-zinc mine in Shaanxi Province demonstrated the method's ability to identify risk levels and forecast trends effectively.  
Validation using subsequent microseismic monitoring data revealed alignment between observed trends and model predictions, underscoring the method's practicality and reliability. This approach supports proactive mine safety management by identifying instability risks early and enabling targeted interventions. The SPA-IAHP-PCN model has the potential for scalable application across diverse mining environments, contributing to enhanced safety governance and sustainable mining practices.  
Conceptualization, F.Y. and X.L.; methodology, X.L.; software, S.D.; validation, X.L., L.D. and H.W.; formal analysis, F.Y.; investigation, D.S.; resources, L.D.; data curation, S.D.; writing--original draft preparation, F.Y.; writing--review and editing, X.L.; visualization, S.D.; supervision, H.W.; project administration, F.Y.; funding acquisition, F.Y. and X.L. All authors have read and agreed to the published version of the manuscript.  
The study was supported by the National Natural Science Foundation of China (52474280, 52104108), and the Fundamental Research Funds for the Central Universities of Central South University (NO. 1053320232623).  
Not applicable.  
Data Availability Statement: Data will be made available upon request.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## 4 Discussion
### Inter-Elemental Relationship and Related Groups
The distance between Klaipeda and Krakow is about 630 km, meanwhile from Gdynia and Gdansk (W direction) where sometimes coal combustion is also used is only 220 km. As mentioned, Br is a technogenic tracer of biomass burning, while the sources of S can be oil or coal burning and automotive diesel [94]. However, oil or coal burning for heating is not used for decades in Lithuania. The research of fly ash from waste incinerator plants has revealed high contents of Cl and S [104], while combustion of different types of Br containing plastic wastes is possible source of Br [74]. To this end, it should be mentioned that anomalous high contents of these three elements were determined both in ash of Klaipeda waste incineration plant and in dust of production stevedored in C and D terminals (Table S2). On the other hand, we cannot reject the possibility that when the winds have opposite direction, part of S and accompanying elements V and Ni can be transferred from \"Mazeikiai Refinery\". Strong association of Mg-Ca (cluster R2) which includes the indicators of carbonate rocks (dolomites, marls, limestones, etc.) and the group of the main crustal elements (R3) indicates that part of the contents of Ca and Mg in dust is pedogenic. It is not strange, because after the regression of the sea [24], soil of the seashore was developed on marine quaternary sediments which contain mollusk shells. Some researchers [82] ascribe Mg to sea salt indicators. However, the fact that Mg and Ca in cluster R2 are somewhat separated from \"crustal\" elements of cluster R3 enables to presume that there are also other Ca and Mg input ways to ambient air. The first way can be related to cement production [94,111] as well as construction industry. The second way is their uplift to ambient air during road construction, since either gravel containing limestone or dolomite crushed stone comprise the floor of the asphalt pavement. Open storage of dolomite crushed stone takes place in southeastern part of the city.