esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Detection Precision
Figure 9 below shows the experimental results, with the X-axis representing recall and the Y-axis representing precision. The dark purple curve represents the proposed method, while the light purple and pink curves correspond to the results of BOW2 and SRLCD, respectively. The core idea of SRLCD is salient region recognition. Still, the local dataset consists of around-view images that have been cropped and stitched, lacking distinct salient regions, making SRLCD unsuitable for this scenario. Therefore, we compare only the proposed method and BOW2 on the local dataset. In addition to the precision-recall curve, another critical metric for evaluating detection performance is the maximum recall at 100% precision, which reflects the boundary performance of the method. A higher recall at this boundary point indicates better detection performance. The experimental results are shown in Figure 10 below, where the X-axis represents the test sequences from KITTI and the Y-axis represents recall. The results demonstrate that the proposed method achieves higher recall at the precision boundary than the  
Figure 8: Experimental trajectory and loop-closure ground truth in local datasets. Figure 7: Experimental trajectory and loop-closure ground truth. From left to right: sequences 00, 05, 06, 07 from KITTI. comparison methods. Notably, in sequence 07, while the proposed method achieved higher recall than SRLCD, it did not outperform BOW2. To evaluate the detection performance of the proposed method in low-luminance scenarios, we reduced the overall luminance of all image data to 15% of the original. Figure 11 shows the image effects before and after luminance reduction. Since SRLCD performs loop-closure detection based on salient regions and is sensitive to image contrast, it is unsuitable for low-luminance scenarios. Therefore, this experiment only compares the proposed method with BOW2. The experimental results are shown in Figure 12. Overall, the detection performance of the proposed method shows a slight decrease but still outperforms the comparison method. Figure 11: Schematic of image processing. The left image is the original, and the right image has luminance reduced to 15%. Figure 10: Maximum recall at 100% precision. Figure 9: Precision–recall curves based on 00 sequence (**left**) and local dataset (**right**).
## 2 Results and Discussion
### Medical Context
The exchange of knowledge and the opportunity to discuss medical problems have a positive effect on patients, as GPA describes here:  
GPA: \"It provides professional confidence for us and for the patient to hear that we have talked to specialists about their condition.\"  
GPB explains that the medical discussions provide safety for the patients:  
GPB: \"We are able to do much more locally than we were before. It was possible to give intravenous antibiotics, but it was not possible in the same way [without guidance]. Now, for me, I've experienced it [as being] much safer than it was.\"  
SC: Those patients we are discussing get treatment on rather high level, not far from ours [the specialist level]. It creates safety for us [the specialists]. Both GPA and GPB describe how the feedback provided by videoconferencing gives them confidence in their decisions and methods of treatment. At the same time, they also gain access to a broader spectrum of illness cases and treatment methods. SC describes how the GPs access specialist knowledge and the way in which this access also creates safety for the specialist, giving a high level of treatment to the patient from a distance.
# Proposal for the Evaluation of Eco-Efficient Concrete
## 3 Proposed Assessment Method for the Eco-Efficiency of Concrete
### Eco-Efficiency Assessment Process  
Concrete eco-efficiency assesses the environmental performance, cost performance, and durability performance of concrete according to input materials and energy sources during the production of concrete (cradle-to-gate). The environmental aspect assesses the environmental load of raw materials and energy sources used during the production of concrete based on a lifecycle assessment (LCA) technique.  
The lifecycle impact assessment of concrete was performed separately for raw materials, transportation, and manufacturing stages. For the impact assessment, a damage-oriented (endpoint) method was applied to identify the degree of effects of environmental emissions from concrete production on human beings and the ecosystem [14].  
Six types of environmental impacts, global warming, acidification, eutrophication, resource consumption, effects on the ozone layer, and creation of photochemical oxides, were selected as subjects of assessment. We analyzed the effects of these six environmental impact categories on safeguard subjects, including human health, social assets, fauna, flora, and primary manufacturing.  
Database presented in the previous study [15] was applied to convert the effects into cost values.  
The previous study is the first research of its kind in Korea to create lifecycle environmental impact indicators in the current unit. Because there are no other theories or DB specialized for construction materials and concrete in Korea, the DB of our previous work is appropriate for calculating the environmental costs of concrete herein.  
Figure 4: Eco-efficiency concept for concrete.  
Economic performance is deduced by combining different lifecycle costs, such as raw materials and energy sources (oil and electric). This study used DB on standard unit prices of materials for the production of concrete and energy sources provided by the Korean Price Information System [16].  
Durability evaluates the expected service life of concrete based on carbonation among diverse deterioration phenomena. Among existing carbonation prediction equations, a formula presented by the Japanese Society of Civil Engineers was used herein to derive this study's equations for the service life of concrete.
## 4 Experimental Evaluations and Results
### Contrastive Explanations with BEERL  
In the exploitation phase, an RL agent chooses an action that maximizes the cumulative reward, and understanding why it is preferred over the other is important. When contrasting the possible actions (e.g., why no tilt is preferred than tilting up), the generated explanations from our method present detailed information as shown in Figure 10 where they consist of input feature and reward component tuples. Figure 10a shows the explanation of why no tilt is preferred than tilting down where the tilt input feature contributes negatively to all reward components. On the other hand, the sum of positive contributions is greater than the disadvantage of not tilting up. When the \(MSX^{+}\) concept is applied, simpler explanations are produced, such as the following: the contribution of '\(RSRP\)[1] to SINR[\(R\)]' and 'base value[\(l\)] to SINR[\(R\)]' ([I] and [R] indicate input feature and reward component, respectively) are enough to overcome the disadvantage of not selecting action tilting down (shown as textured bar in Figure 10). Since both reasons contribute to the SINR reward component, the Algorithm 1 further compresses it by aggregating the contribution of each reward component(\(MSX^{\text{C}}\)). Thus, the compressed explanation can be interpreted as '_The contribution of SINR reward component is enough to overcome the negative impact of not choosing tilting down action_'.  
Figure 10b shows another constrastive explanation of why no tilt is preferred than tilting up. The \(MSX^{+}\) of these explanations informs that the contribution of '_Tilt[\(l\)] to AvgSINR[\(R\)]', Tilt[\(l\)] to AvgRSRP[\(R\)]', 'base val[\(l\)] to AvgSINR[\(R\)]', and '_base val[\(l\)] to AvgThroughput[\(R\)]'_ are enough to overcome the negative contribution of not tilting up. Furthermore, they are compressed by aggregating the contribution of the input feature resulting  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Action** & **RSRP** & **SINR** & **Throughput** \\ \hline Tilt-down & 12.522070 & 8.453173 & 17.904276 \\ No-tilt & 12.652522 & 9.103990 & 18.448515 \\ Tilt-up & 12.470411 & 8.746243 & 18.138926 \\ \hline Mean & 12.548335 & 8.767802 & 18.163904 \\ Range & 0.182111 & 0.650817 & 0.544239 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Base value for each action and reward component at the RL level.  
in an explanation that can be interpreted as _'The contribution of Tilt input feature and base values are enough to overcome the negative impact of not choosing tilting up action'_. It should be noted that the base values in Figure 10 are the difference among the actions that the reward weight adjustment does not address. Adjusting the reward weight cannot change the proportion of it within each reward component (i.e., making it less dominant than the input features).
# Optimizing Apache Spark MLlib: Predictive Performance of Large-Scale Models for Big Data Analytics
## 4 Experimental Results
### Model Performance Comparison  
We compared the performance of different models by evaluating metrics such as mean absolute error (MAE) and \(R^{2}\). The \(R^{2}\) metric measures the proportion of the variance in the dependent variable that is predictable from the independent variable, while the mean absolute error (MAE) measures the average absolute difference between the predicted and actual values.  
In order to provide a more comprehensive evaluation of model accuracy, we also included Mean Squared Error (MSE) as an extra performance metric alongside \(R^{2}\) and MAE. A squared perspective on prediction errors is offered by MSE which penalizes larger deviations more. The byproduct of this metric is to give us a sense of how the errors are distributed and which values are outliers. The results indicate that Gradient Boosting performed best over all the experiments, confirming its suitability for modeling resource utilization patterns in Apache Spark MLlib.
## 2 Background and Related Work
### Related Work
Recognizing that the quality and diversity of vulnerability descriptions greatly impact prediction accuracy, the authors use generative AI (GPT-3.5 Turbo) to generate CVSS descriptions and introduce a fine-tuned BERT-CNN model to predict CVSS vector metrics. Through experiments using both original and AI-generated data, the authors demonstrate that their proposed architecture significantly enhances the accuracy of vulnerability assessment, outperforming state-of-the-art approaches. In [39], the authors discuss the VULTURE project, an AI-powered cybersecurity platform designed to address the challenges posed by increasingly sophisticated cyberattacks. Traditional cybersecurity methods struggle to keep up with the speed and complexity of modern threats. VULTURE leverages generative AI (GenAI) and large language models (LLMs) to enhance vulnerability prediction, automate penetration testing, improve intrusion detection, and enable advanced cyber-physical risk profiling. The work explores the platform's architecture, key innovations, potential impact, and outlines future research directions. By and large, generative AI can significantly bolster vulnerability management by providing organizations with sophisticated tools for code analysis and predictive insights. This technology allows for the automation of routine tasks, enabling security teams to focus on higher-level strategic activities. For instance, generative AI can swiftly identify and respond to emerging threats, matching the speed and sophistication of malicious actors. Furthermore, selecting an appropriate generative AI model is crucial for effective vulnerability detection. Organizations should consider various generative models, such as Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs), tailored to their specific needs and the nature of their data [16]. The integration of these models facilitates more precise vulnerability scoring by enabling the analysis of large datasets and the identification of hidden patterns. Hence, the effectiveness of generative AI in vulnerability scoring relies heavily on the quality and diversity of the training data used. High-quality datasets are essential for training models to recognize potential vulnerabilities accurately. In short, the evaluation of AI models' robustness against adversarial attacks using Common Vulnerability Scoring System (CVSS) metrics provides critical insights into their cybersecurity preparedness. AI models can be assessed for resilience by simulating adversarial attacks and generating CVSS scores through the systematic evaluation of attributes such as attack vector, attack complexity, and required privileges.
# A Message Passing-Assisted Iterative Noise Cancellation Method for Clipped OTFS-BFDM Systems
###### Abstract
In [25], [PERSON] and [PERSON] adjusted the clipping filter coefficient to achieve a good tradeoff between the PAPR and BER performance of the pilot-embedded OTFS system. However, the coefficient is filled in the guard region to avoid interferences between data and pilot signals, degrading the spectral efficiency. In [26], an MP-assisted (MP-A) clipping method was developed to reduce the PAPR of SCMA-OFDM system. However, the clipping is limited to the Nyquist sampling rate, and the clipping noise is slightly mitigated rather than canceled. Motivated by these, at the receiver, we propose an MP-assisted iterative cancellation (MP-AIC) method to cancel the clipping noise in clipped OTFS-BFDM systems. Furthermore, at the transmitter, one-iteration clipping and filtering (OCF) [26] is used to prevent peak regeneration and maintain the sparsity of ECM. In the MP-AIC, the reference clipping noise is first constructed, then the revised received signal is obtained by subtracting the reference clipping noise from the initial received signal, and finally the residual signal is extracted from the revised received signal for feedback to the MP detector. Through several iterations, the influence of clipping noise on MP is minimized, and the clipping noise can be considered to be eliminated. Consequently, this method can further improve the decoding accuracy compared with the MP-A method and accelerate the BER convergence without compromising the spectral efficiency. The key contributions of this paper can be summarized as follows:  
* The OTFS-BFDM input-output relationship is formulated in matrix form, and the problem of high PAPR in OTFS-BFDM systems is presented by simulation. * The OCF method for PAPR reduction is considered to maintain the sparsity of ECM and the statistical properties of clipping noise are analyzed. * The proposed MP-AIC method is described in detail, where the residual signal is calculated, and initial probability of MP decoding in each iteration is the convergence probability of the next iteration. The remainder of the paper is organized as follows. Section 2 introduces the OTFS-BFDM system and the problem of PAPR. Section 3 elaborates the proposed MP-AIC method of Clipping Noise. The simulation results and analysis is presented in Section 4 and the paper is concluded in Section 5. In addition, we use mathematical symbols as shown in Table 1 throughout the paper.
## 3 Results and Discussion
### Comparison with Existing Techniques  
At the end of the experimentation phase, a comprehensive comparison of classification and segmentation was conducted on existing methodologies. The comparison was performed on five training instances for the Indian Pines and Pavia University datasets, respectively, as shown in Tables 3 and 6. It can be noted that RS, ME, MI, and BT showed relatively poor performance as compared to MBT. This was attributed to the fact that former techniques usually select samples from complex areas, having the least confidence, which causes difficulty in learning procedure [36]. In comparison, the MBT outperformed as compared to the other active learning techniques, as it selected only those samples that were lying in the vicinity of high-density samples in each class.  
[PERSON] et al. [59] introduced morphological attribute profiles (MAPs). Morphological profile filters are used to extract structural information. These calculated profiles are more flexible and lead to better investigation of the contextual information by using different morphological attribute transformations. [PERSON] et al. [60] employed a convolutional neural network to encode spectral and spatial information. The output is fed to a multi-layer perceptron for classification. The overall accuracy of 98% was achieved when the author used 30 samples of the Indian Pines dataset with deep neural networks. The relatively low performance by using even higher number of samples is the inherent capability of deep learning strategies as given in [61]. The remarkable segmentation results of the proposed methodology were owed to the use of well-classified data before the segmentation phase. The algorithm consumed relatively more resources for computation as compared to conventional solutions. Similarly, a semi-supervised approach for segmentation was introduced by [PERSON] et al. [42] in which the posterior class probabilities were calculated by MLR algorithm. Maximum-a-posteriori (MAP) was computed on this output to calculate the best classification label. The multi-level logistic (MLL) algorithm was then employed with Markov random fields (MRF) to compute the segmentation [44,45]. Moreover, MRF works on the model of Gibbs energy to calculate the best segmentation label, based on current pixel potential for the neighboring pixels, to decide whether this pixel belongs to the current region or not [46,47].
## 3 Results
### WVTR and Light Transmittance of Stretchable Gas Barrier Film  
We compared the WVTR values of 30 laminated layers of PDDA/PVA (MMT), PDDA (GO)/PVA, and PDDA (GO)/PVA (MMT). When GO and MMT are laminated, compared to when each is used alone, the materials are more stably bonded by hydrogen bonding of crosslinking effects between GO and MMT, thus filling the vacancies formed when the layers are laminated individually and extending the diffusion path length for a water molecule. As shown in Figure 7, the WVTR value of the PDDA (GO)/PVA (MMT) films was greatly reduced. We compared the WVTR values after 100 stretching cycles (30% strain) of the 30 laminated layers of PDDA (GO)/PVA (MMT) on the stretchable substrate subjected to the applied pre-strain. The WVTR values of the gas barrier films were analyzed using the Ca test method. Figure 8a shows that the WVTR values of the samples with a 30-layer PDDA (GO)/PVA (MMT)-coated stretchable substrate without pre-strain, with 10% pre-strain, and with 30% pre-strain were 8.8, 2.8 \(\times\) 10\({}^{-1}\), and 2.5 \(\times\) 10\({}^{-2}\) g/m\({}^{2}\) day. The OTR values of the films were analyzed by MOCON. As shown in Figure 8b, the 30-layer PDDA (GO)/PVA (MMT)-coated stretchable substrate without pre-strain was 178 cc/m\({}^{2}\)-day, which decreased to 43.88 and 11.91 cc/m\({}^{2}\)-day for the samples subjected to 10% and 30% pre-strain, respectively. Wrinkles were formed in the film with the gas barrier laminated after applying 30% pre-strain. Therefore, when the stretching test (30% strain) was performed, the barrier layer did not crack, and the stretchable gas barrier film showed improved WVTR and OTR values. As light transmittance is also important for gas barrier films, the transmittance of the 30-layer PDDA (GO)/PVA (MMT)-coated Ecoflex/PDMS substrate was measured using a UV-Vis spectrophotometer. Figure 9 shows the transmittance values of the uncoated Ecoflex/PDMS substrate and the 30-layer coating of the PDDA (GO)/PVA (MMT) substrate at 550 nm were 86.54% and 80.84%, respectively. This shows that the PDDA (GO)/PVA (MMT) 30-layer stretchable gas barrier film was sufficiently transparent. Therefore, this stretchable gas barrier film can be used in electronic devices requiring transparency and stretchability.  
Figure 8: (**a**) WVTR and (**b**) OTR values of the prepared stretchable gas barrier films.  
Figure 7: WVTR results of PDDA/PVA (MMT), PDDA (GO)/PVA, and PDDA (GO)/PVA (MMT) gas barrier films.
## 3 Analysis and Discussion  
### Semi-Arid Lands  
Semi-arid lands cover approximately 17.7% of the Earth's terrestrial surface [23; 24]. The United Nations Environment Programme (UNEP) characterizes semi-arid regions as areas where the annual precipitation (P) ranges from one-fifth to one-half of the potential evapotranspiration (ET) (0.2 < P/ET \(\leq\) 0.5) [25; 26].  
This paper specifically focuses on these semi-arid lands, following the aridity index (AI) developed by UNEP and is in concordance with the United Nations Convention to Combat Desertification (UNCCD) criteria for defining semi-arid lands [24].  
In the central-western region of the Continental United States (CONUS), which is the focus of this article, there are 2.7 million km\({}^{2}\) of semi-arid lands localized in 17 states and that represent 35% of the total area in the US Central-Western region [27] (Figure 2).  
Regarding drought, the US Drought Monitor (USDM) categorizes drought--with a scale from D0 (Abnormally Dry) to D4 (Exceptional Drought)--according to SM, streamflow, and precipitation levels parameters. The USDM reported in April 2023 that 12% of land in the Western states was classified as experiencing extreme or exceptional drought, and an additional 20% was classified as severe. Data reported by the USDM demonstrate that drought incidence in the western United States during the summer of 2023 exceeded all previous droughts in the region since 2000 [28].  
The above indicates the complex and increasingly accelerating challenges of drought and land degradation. This also highlights the critical importance of developing strategies to mitigate impacts and improve ecological restoration efforts which focus on enhancing SM conditions in semi-arid lands. Understanding and monitoring SM dynamics is essential for preserving vegetation and soil health in fragile ecosystems, highlighting the need to stay abreast of key advancements, benefits, and gaps in SM monitoring. Additionally, it is crucial to identify and evaluate the most relevant tools, methods, and satellite products, along with field data, for monitoring SM in semi-arid lands, which is the primary focus of this review.