esa-sceva/satcom-chunk-collection huggingface dataset:
# A Network Traffic Abnormal Detection Method: Sketch-Based Profile Evolution
## 2 Related Works
The distance-based method, i.e., the method based on similarity [34], classifies anomalies by the way of determining them and performs anomaly detection by calculating the degree of similarity between Profile and sample data. In the existing literature, there are many methods to calculate the distance, but some of them are the existing methods. For example, the Mahalanobis Distance [35], the Weighted Jaccard Distance [36], and the KL divergence [37], and there is also a self-defined distance function Similarity search [38]. [PERSON] et al. [39] used the Affinity Propagation (AP) clustering algorithm to cluster normal behaviors and detected anomalies by calculating the Euclidean distance between the samples and normal behavior clustering. The dataset used in the experiment was comprised only of HTTP packets. [PERSON] et al. [40] apply the signal processing framework to anomaly detection believing that network traffic is similar to signal. Traffic is defined as a mixture of normal traffic, abnormal traffic, and noise, and traffic is modeled, and then anomaly detection is performed using intrinsic similarity. [PERSON] et al. [41] used n-dimensional vectors to represent each sample, and used cosine distance to calculate the cosine angle of two vectors to represent the similarity coefficient. [PERSON] et al. [42] used PCA to generate the digital features of network traffic and used Dynamic Time Warping (DTW) to calculate the similarity of two sequences in computing the similarity of network traffic. DTW is an algorithm used to find the optimal queue in speech recognition. Most of the existing anomaly detection methods are related to intrusion detection datasets, and a small fraction of anomaly detection methods based on traffic statistics cannot solve the fine-grained anomaly problem for a single IP user. The Sketch-based Traffic modeling method proposed in this paper is a distance-based method that maps the network traffic to specific areas according to the structuring process of network traffic to form a Traffic Graph (TG), so that the abnormal method can directly deal with the original network traffic and generate fine-grained features. Then, CNN and LSTM neural networks are used to extract the Profile of network traffic behavior to realize the evolution behavior modeling. The corresponding distance formula and TG are used to determine the position of outliers, and the fine-grained detection of outliers is realized. The anomaly detection model improves the existing anomaly detection and enables the direct processing of the original network data, but the analysis of the type of anomaly requires further research.
# An Assessment of Agent-Based Modelling Tools for Community-Based Adaptation to Climate Change
## 3 Results
### Ascape  
Ascape demonstrates notable capabilities in modelling community dynamics and exhibits adaptability for different community sizes, rendering it a suitable choice for various scenarios. Its strength lies in its ability to adjust models to accommodate different community sizes, providing a versatile platform for simulating diverse contexts. However, a significant limitation of Ascape becomes apparent when considering its application in CBA modelling, particularly in the incorporation of socio-economic factors. While Ascape excels in easily integrating demographic factors into models, its functionality falls short when attempting to represent the intricate interplay of socio-economic variables within the community dynamics. This limitation hinders the tool's capacity to comprehensively capture the socio-economic dimensions crucial for understanding and adapting to climate-induced challenges. Another noteworthy drawback of Ascape in the context of CBA modelling is its inability to facilitate participatory modelling. Participatory modelling involves active engagement with community members in the modelling process, ensuring that local knowledge and perspectives are incorporated. Ascape's lack of support for participatory modelling restricts its ability to benefit from the insights and experiences of the community, potentially leading to less accurate and relevant model outcomes. Handling diverse data types is a fundamental requirement for comprehensive CBA modelling, considering the multidimensional nature of environmental and socio-economic challenges. Unfortunately, Ascape faces limitations in this aspect, lacking robust features for efficiently managing and integrating diverse data types relevant to CBA initiatives. This constraint can impede the tool's effectiveness in capturing the complexity of community environments and may compromise the accuracy of simulation outcomes. Moreover, Ascape's inadequacy extends to the absence of support for risk and uncertainty assessment, as well as feedback and monitoring mechanisms--essential components for robust CBA modelling. The ability to assess risks, account for uncertainties, and incorporate feedback loops is critical for understanding the dynamic nature of community responses to environmental changes. The absence of these features in Ascape diminishes its suitability for projects where a comprehensive evaluation of potential risks and the long-term impact of adaptation measures is imperative.
# Optimized Downlink Scheduling over LTE Network Based on Artificial Neural Network
## 2 Literature Review
Artificial neural network (ANN) techniques are used in the GRNN-RBFNN model and other proposed methods to optimize downlink scheduling over LTE networks. These models use historical data to learn and predict optimal scheduling policies for different users. However, some challenges still need to be addressed in LTE network scheduling. One such problem is ensuring fairness among users with different QoS requirements. The scheduling algorithms used in these models may not always be able to provide equal QoS to all users, leading to a lower fairness index for some users. Therefore, further research is needed to develop scheduling algorithms to ensure fairness among users with varying QoS requirements. Another challenge is the dynamic nature of the LTE network, which may lead to fluctuations in network conditions and user demands. This can make it difficult to predict optimal scheduling policies accurately. Therefore, there is a need to develop adaptive scheduling algorithms that can adjust to changes in various network channels' conditions and user demands in real time considering the data over the network. The proposed methods have shown promise for optimizing downlink scheduling on LTE networks. To improve the performance of LTE network scheduling, the problems of fairness and changing network conditions still need to be studied. The proposed approach of using artificial neural networks (ANNs) for downlink scheduling in LTE networks is suitable for solving the critical problem of optimizing the allocation of radio resources to users. ANNs are powerful machine-learning models that can learn from historical data to identify patterns and relationships. In the last few years, many researchers have used ANN models to enhance the performance of the downlink communication system in LTE networks. Many models have been used; some studies considered channel estimation, while others investigated the condition of the user devices or estimated the mobile location. Predictive analyses of various ANN models for predicting and classifying the data over the LTE network were conducted in some other studies. Several studies have reported significant improvements in various performance metrics, such as throughput, fairness, and quality of service, when using ANNs for downlink scheduling.
## 5 Modeling of Existing Processes and Situations. Results and Discussion
### Simulation of Changes in Air Buoyancy Coefficient C
#### 5.1.4 Buoyancy Coefficient Dependency on Air and Petroleum Product Density  
Figure 6a shows the dependency of the buoyancy coefficient in air on the density of air and petroleum products. Figure 6b shows two sections of this plane.  
The presented graphs highlight these facts:  
1. The dependency of the buoyancy coefficient on air density is linear, whereas this dependency on the density of the product is non-linear.
2. Range of the buoyancy coefficient values is: 1.0007-1.0025 or (0.07-0.25) %.
3. The lighter the product is (i.e., the lower its density) and the higher the density of air, the greater the buoyancy effect.
# A Comprehensive Evaluation of Possible RNSS Signals in the S-Band for the KPS
## 2 S-Band and Adjacent Bands Systems
### Navigation with Indian Constellation  
India's NavIC is a regional RDSS system consisting of three geostationary Earth orbits (GEOs) and two inclined geosynchronous orbits (IGSOs) inclined at 29\({}^{\circ}\) from the equatorial plane. There are three satellites in GEO (at 32.5\({}^{\circ}\) E, 83\({}^{\circ}\) E, and 131.5\({}^{\circ}\) E) and two satellites in each IGSO (with longitude crossing at 55\({}^{\circ}\) E and 111.75\({}^{\circ}\) E). The primary service area includes the region extending up to 1500 km from the Indian border. NavIC plans to deploy four additional IGSO satellites to improve the service availability, accuracy, and continuity and expand the coverage [27].  
Currently, NavIC is the only satellite navigation system that transmits a navigation signal in the S-band, providing a standard positioning service (SPS) and a restricted service (RS) [28]. The carrier frequency of NavIC in the S-band is at 2492.028 MHz, and the design parameters for these signals are given in Table 2.
# Treatment Methods for Antibiotic Mycelial Residues: A Review
## 3 Treatment Methods for AMR
### Multi-Technology Coupling
#### 3.4.1. Incineration Coupled with Advanced Dehydration Technologies  
Incineration technology requires pretreatment dehydration processes, yet AMR typically exhibits high moisture content, necessitating dehydration prior to incineration. Native AMR contains tightly bound biogels or mycelium-water complexes, rendering conventional mechanical dehydration methods (e.g., centrifugation, compression) ineffective [34; 37]. Extensive research confirms that advanced pretreatment technologies--such as microwave irradiation or HT--significantly enhance AMR dewaterability.  
For biomass wastes, like AMR, microwave irradiation degrades the matrix, modifies particle size distribution, and disrupts microfloc structures through cell wall lysis, thereby releasing intracellular mucoid polymers (containing proteins, polysaccharides, DNA, etc.) and improving dehydration efficiency [69]. Compared to conventional heating, microwave pretreatment offers superior penetration depth, rapid heating rates, energy efficiency, and non-contact operation, achieving enhanced dewatering performance with a reduced processing time [70; 119].  
HT induces more pronounced phase transformations than microwave processing. Under conditions of 180-200 \({}^{\circ}\)C for 30 min, HT reduces the AMR moisture content from more than 80 wt% to less than 50 wt%, achieving 40% solid recovery [37]. Hydrolysis-condensation converts volatile nitrogen (amino acids, ammonia) to gaseous N for removal, lowering solid product N-content to <6.0 wt%, enabling 20-30% NOx reduction during subsequent incineration [95]. Coupling with air-staged combustion further reduces NOx by 45% versus conventional combustion, with enhanced efficacy at higher temperatures, demonstrating synergistic pollution control and effective nitrogen management.
## 2 Materials and Methods
### MCDM Methods  
#### 2.2.1 CRITIC Method  
The CRITIC method aims to determine objective weights of relative importance in multi-criteria decision-making problems [26]. The most important feature that distinguishes this method from other methods is that the standard deviations and correlations of the criteria are taken into consideration when weighting, not the results obtained from expert opinions [27]. For this reason, the CRITIC method, which provides consistent results in determining criteria weights, was used in this study.  
The CRITIC method is a type of correlation method. It is used to determine the standard deviations of the criterion values and the criterion contrasts of the paired columns [28]. The CRITIC method consists of five steps, and the steps of the method are as follows [26].  
* Step 1: Creating the Decision Matrix The decision matrix contains the criterion values corresponding to different alternatives. It is created as in Equation (1). \[X=\left[x_{ij}\right]_{m\times n}=\begin{bmatrix}x_{11}&\ldots&x_{1n}\\ \vdots&\ddots&\vdots\\ x_{m1}&\ldots&x_{mn}\end{bmatrix}\] (1) \(x_{ij}\), is the _jth_ criterion value of alternative \(i\).
* Step 2: Normalization of Decision Matrix In the normalization process, (2) is used for maximization-oriented criteria and (3) for minimization-oriented criteria. \[r_{ij}=\frac{x_{ij}-x_{j}^{min}}{x_{j}^{max}-x_{j}^{min}}\] (2) \[r_{ij}=\frac{x_{j}^{max}-x_{ij}}{x_{j}^{max}-x_{j}^{min}}\] (3)
* Step 3: Creating the Relationship Coefficient Matrix The relationship coefficients (\(\rho_{jk}\)) used to measure the degree of relationships between the evaluation criteria are calculated as in (4). \[\rho_{jk}=\frac{\sum_{i=1}^{m}\left(r_{ij}-\overline{r_{j}}\right).(r_{ik}- \overline{r_{k}})}{\sqrt{\sum_{i=1}^{m}\left(r_{ij}-\overline{r_{j}}\right)^{2 }.\sum_{i=1}^{m}\left(r_{ik}-\overline{r_{k}}\right)^{2}}}\;j,k=1,2\ldots \ldots\ldots\ldots\ldots\ldots\ldots\ldots\ldots\ldots\ldots\ldots\ldots\] (4)
* Step 4: Calculating \(C_{j}\) Values \(C_{j}\), which combines both features and expresses the total information contained in criterion j, is calculated using the standard deviation of the column values of the normalized decision matrix. Then, (5) and (6) can be used for these operations. \[C_{j}=\sigma_{j}\sum_{k=1}^{n}\left(1-\rho_{jk}\right)j=1,2\ldots\ldots\ldots n\] (5)\[\sigma_{j}=\sqrt{\frac{\sum_{i=1}^{m}\left(r_{ij}-\overline{r}_{j}\right)^{2}}{m-1}} \tag{6}\]
* Step 5: Calculation of Criteria Weights The objective weights of the criteria can be calculated using (7). \[\mathrm{W}_{j}=\frac{c_{j}}{\sum_{k=1}^{n}c_{k}}j,k=1,2\ldots\ldots.n\] (7) Objective weight values are ranked from largest to smallest. It is concluded that the criterion with the highest weight is more important.
## 2 Methodology  
### Articles Search and Selection Criteria  
To review the literature on the adoption and effectiveness of Gen AI in patient care, a comprehensive search was conducted using relevant keywords across major scientific databases, including IEEE Xplore, Springer Link, Scopus, and PubMed. The systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [1]. Initially, duplicate records were removed, and a detailed screening of titles and abstracts was performed to refine the selection. Keywords used in the search included \"generative AI in patient care\", \"generative artificial intelligence in personalized care plans\", \"Gen AI in personalized patient care plans\", and \"generative AI and patient care plans\". Subsequently, a thorough review of the full-text articles was conducted to assess their eligibility. The inclusion criteria for this review were as follows:  
* Original research articles, primarily journal publications;
* Studies published between 2021 and 2024 (inclusive);
* Research focused on the application of generative AI in patient care;
* Targeted inpatient and community care settings;
* Articles written and published in English.  
Exclusion criteria included non-original research (e.g., editorials, commentaries, or reviews). Additionally, studies exclusively focused on patient monitoring, remote monitoring, intensive/emergency care, or other general monitoring tools were not considered, as the review specifically targeted the use of generative AI in direct patient care planning.
## 3 Results  
### Sensitivity Experiments  
#### 3.1.1 Soil Stratification Schemes  
As shown in Figure 4a, the simulated soil water content was more sensitive to the soil stratification schemes. In Scheme 1, the simulated soil water content in the upper 5 m closely aligned with the observed values, yielding _pbias_, _NSE_, and _RMSE_ values of \(-0.83\%\), \(0.63\), and \(0.01\) cm\({}^{3}\)-cm\({}^{-3}\), respectively. In contrast, Scheme 2 overestimated the soil water content from June 2015 to December 2018, resulting in a substantially lower _NSE_ of \(0.15\). The simulated leaf area index and root distribution demonstrated low sensitivity to the soil stratification schemes, showing minimal differences between Scheme 1 and Scheme 2. The simulated root distribution in both schemes closely aligned with the observations, yielding _NSE_ values up to \(0.86\) and _RMSE_ values less than \(4.0\%\) (Figure 4d). The _NSE_ for the leaf area index ranged from \(0.59\) to \(0.62\), and _pbias_ ranged from \(1.4\%\) to \(6.8\%\). Given the relatively short observation period for the leaf area index, we further compared the simulated leaf area index with the MODIS-derived data from 2014 to 2018 (Figure 4c). Overall, the simulated, observed, and MODIS-derived leaf area indices followed similar seasonal patterns. However, the MODIS leaf area index was much higher during the wet seasons of 2014 and 2015. Previous studies also reported that the MODIS leaf area index tends to be higher than field observations during wet periods [69].
# 3D Object Detection Using Frustums and Attention Modules for Images and Point Clouds
## 3 Frustum ConvNet with Attention (FCAM)
### Frustum ConvNet  
We designed the 3D object detector based on the framework of Frustum ConvNet, as shown in Figure 1a. At first, Frustum-level features are obtained from the Frustum through PointNet and attention modules, which are re-formed as a 2D feature map. Next, the PointNets are applied to each Frustum, and the PointNets with shared weights form the parallel streams of Frustum ConvNet [1]. For point cloud classification, the PointNet takes \(n\) points as the input, and the output comprises the classification scores for the \(d\) classes. The coordinates of the 3D space point clouds minus the centre coordinates of the Frustum to constitute a 1D vector and serve as the input of FCN. L-dimension vectors form a 2D feature map \(F\) with the size \(L\ \times\ d\), which will be used as the input of a subsequent FCN [24]. Finally, the 2D feature maps are used as the input of Fully Convolutional Networks (FCNs) [24] for 3D prediction. Then, we use the detection head for classification and regression.  
In the PointNet, we apply our improved Convolutional Block Attention Modules (CBAMs) in the hidden layer of Multilayer Perceptron (MLP), as shown in Figure 1b. Output features of the CBAM attention module are multiplied with the input feature of the MLP to obtain the final fused features.
# Safe and Sustainable Design of Composite Smart Poles for Wireless Technologies
## 3 Results
### Signal Attenuation at the GHz-Regime  
Any enclosing of the (5G) radios inside a housing or shaft requires analyzing the RF signal attenuation due to the surrounding enclosure or radome wall. The design of the details, i.e., radomes or'signal windows' in cutouts, must satisfy experimental verification regarding the attenuation. In this study, the radomes were designed not be load-carrying parts so that reinforcements (fibers) were not needed for the radomes. PREPERM polymer (see Section 2.5) was analyzed here, and attenuation defined in terms of dielectric loss (DL). In particular, the long-term properties of the radome materials in outdoor environments were not well-known. Therefore, the effects of UV radiation (from the sun) were considered in this study. In reality, the signal windows might need to be painted that makes the measurements with painted samples necessary. According to the measurement results, shown in Figure 12, the minimum paint design is crucial for a high signal penetration. The effect of a paint layer had a significant impact on the signal attenuation (i.e., dielectric loss (DL)) and the treatment increased the DL levels 90-175%. Slightly lower increase was measured as the UV degradation was increased (longer UV exposure time); the color of the paint did not observably affect the attenuation.
# GraphEPN: A Deep Learning Framework for B-Cell Epitope Prediction Leveraging Graph Neural Networks
## 2 Materials and Methods  
### Datasets  
This study utilizes three independent datasets to pre-train the VQ-VAE, train the graph transformer model, and conduct independent evaluations.  
SAbDab_1323 Dataset: SAbDab_1323 is derived from SAbDab [13] and comprises 1323 nonredundant protein chains, each containing at least one epitope residue. This dataset is employed to retrain the VQ-VAE model, focusing on learning critical residue representations involved in antibody interactions. The resulting representations provide refined feature vectors crucial for downstream tasks, enhancing the model's ability to capture essential structural information.  
SAbDab_665 Dataset: This dataset, also obtained from SAbDab, contains 665 protein chains annotated with BCEs [14]. BCEs are residues on antigens that interact with antibodies, with the distance between any heavy atom of the epitope and the antibody being less than 4.0 A. CD-HIT clustering was applied to reduce sequence identity to below 70% to ensure sequence independence between the pretraining and training datasets. Only antigen chains with at least one epitope residue were retained for model training.  
Blind_42 Dataset: The Blind_42 dataset consists of 42 nonredundant protein structures specifically designated for independent testing. Sequence identity between this dataset and SAbDab_1323 and SAbDab_665 was reduced to less than 30% via BLAST+ (version 2.14.0) for sequence alignment and homology filtering. The Blind_42 dataset exhibits significant label imbalance, with only 7.08% of the 12,570 samples labeled as positive (epitope residues), whereas negative samples outnumber positive samples by approximately 13.12 times. This dataset evaluates the model's generalizability to unseen data and its robustness under highly imbalanced conditions.