esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Study--Goldeneye CCS Site
### Detectable Area vs. Detection Probability  
In Figure 10, we have plotted the area covered against the detection probability. First we observe that the detected area is greater with larger leakages. Secondly, the 30T and 300T scenario have small difference in percentage of area detected up to approximately 80% detection probability. Thirdly, if we demand high prediction probability the different leak scenarios can detect an area around 1-10%, 0.1-1% and 0.1-0.01% for the 3000T, 300T, and 30T leak scenario, respectively. This gives us insight on the size of the footprint we can detect and thus some insight in how to optimize sensor layout.  
Figure 8: Prediction from the BCNN on the 3000T test data. (**Left panel**) The plot shows the predictive mean leak probability of the 1736 nodes with 200 forward MC realization on each instance. The red line shows where the predictive mean leak probability is above a value of 0.95. (**Right panel**) The plot shows the uncertainty in the prediction. Red line shows where the standard deviation is above 0.15 indicating areas where the prediction is uncertain. See Figure 9 for more details about the uncertainty in observation locations 1, 2, and 3.  
Figure 9: Kernel density estimation (KDE) of the three observations in Figure 8. We have used the seaborn visualization library, i.e., Gaussian kernel with Scott method for estimation the kernel bandwidth. The KDE smoothens the empirical distribution, thus exceeding the estimate beyond the possible range of \([0,1]\). We thus limit the plot to be within the bounds, which means that this KDE does not summarize to 1.  
Simulated leaks have been used to predict footprints subsequently being used to optimize sensor layout [61; 62; 63; 64; 65]. They used a threshold that was based on the Cseep method [66] that is dependent on measurements of different biochemical tracers. We argue that using deep-learning methods and BCNN can lower this threshold, increasing the detectable footprints and thus the decreasing the number of sensors needed to monitor a specific area.
###### Abstract
Additionally, AuNPs can disrupt the integrity of fungal cell membranes, leading to structural alterations in hyphae and ultimately resulting in cell death [28]. The addition of AuNPs appears to be a safe choice, as evidenced by animal model studies indicating that AuNPs can be safely used without causing significant acute or chronic toxicity. AuNPs tend to accumulate in organs such as the liver and spleen. However, studies have demonstrated that they are rapidly cleared from the bloodstream without causing significant organ toxicity [29]. In the case of topical application, the risk of toxicity is significantly lower. Some studies have indicated that percutaneous penetration of nanoparticles is limited to the upper areas of hair follicles or the superficial layers of the stratum corneum (SC). Gold nanoparticles (AuNPs) with sizes ranging from 22 to 186 nm were observed in vivo within the epidermal layers just below the SC of rat skin [30]. This study aimed to investigate the applicability and antifungal therapeutic potential of a dermal form of carbopool-based hydrogels containing ITZ enriched with gold nanoparticles. It is worth noting that there are relatively few studies attempting to obtain a dermal formulation of ITZ; so far nano-spanalastics gel [31], transferosomes [32], and non-aqueous formulations [33], as well as liposomes [34], have been developed. To the best of our knowledge, no dermal formulation of ITZ is registered on the European market and ITZ is available on the US market as a combination formulation for topical application. Therefore, it is important to obtain a stable dermal formulation of ITZ and to verify the impact of the addition of AuNPs on the efficacy of the dermal formulation of ITZ. The literature shows that the use of poly(lactic-co-glycolic) PLGA as a carrier for ITZ is effective in inhibiting fungal growth, with the potential to reduce the concentration of ITZ, indicating increased bioavailability and efficacy of the drug [35]. Literature data also shows that thioconazole deposited on AuNPs showed enhanced activity against fungal cells, suggesting that ITZ may similarly benefit from the enhanced delivery and stability provided by AuNPs [36]. Trials have also been conducted to evaluate the efficacy of ITZ with silver nanoparticles (AgNPs) [37]. However, none of these studies discuss the possibility of obtaining hydrogels containing ITZ-coated AuNPs and their activity against a reference ITZ-resistant strain of _Candidia albicans_.
# Artificial Intelligence Applications in Smart Healthcare: A Survey
## 5 Challenges and Existing Solutions
### Scalability
#### 5.9.2 Existing Solutions  
The prevalent solution for achieving scalability in AI applications is modular architecture. This approach involves breaking down an application into distinct, self-contained modules, each responsible for specific functions. Such a structure allows for the seamless addition of new tasks or datasets without overhauling the entire system. The modular architecture supports parallel processing of these modules, significantly enhancing speed and efficiency, especially when dealing with extensive patient data. For instance, in a healthcare AI application, the architecture might include separate modules for processing patient data, performing predictive analytics, and generating reports. Each module operates independently and concurrently, which improves overall performance [153]. A practical example of this is the Modular Health Information System at Mount Sinai. This system integrates various specialized modules to handle tasks like patient monitoring, data analysis, and reporting. This modular approach enables effective management of  
Figure 7: The overview of scalability.  
large volumes of patient data and flexibility to adapt to evolving needs without extensive system modifications.  
Cloud computing also offers a robust solution for scalability. It provides scalable computing resources that adjust dynamically to meet the demands of AI workloads without the need for expensive on-premises infrastructure. Cloud services deliver scalable distributed storage systems that are essential for managing vast amounts of medical data, which is crucial for training and optimizing AI models while ensuring data security and reliability [154]. For example, Google Cloud Platform's AI and machine learning services automatically scale resources according to workload demands. Hospitals and research institutions use cloud-based solutions to train and optimize AI models on extensive datasets, benefiting from real-time resource expansion and load balancing [155]. Amazon Web Services (AWS) exemplifies this by enabling efficient scaling of computational power and storage for large-scale genomic data analysis, thus maintaining system stability and performance during high-demand periods [156].  
However, traditional benchmarking methods, which involve creating a new benchmark for each possible workload, are not scalable or feasible for big data and AI benchmarking. Consequently, new methodologies have been proposed and adopted to address these limitations. For instance, BigDataBench 4.0 and Mystique are designed to evaluate the performance of big data and AI systems more effectively. BigDataBench 4.0 offers a comprehensive benchmark suite for data-intensive applications, including those in healthcare AI. Mystique focuses on assessing the performance of AI systems in handling large-scale data processing tasks. These new benchmarking methodologies create more realistic and scalable benchmarks that align with the practical challenges and requirements of modern AI applications [157; 158].
# Assessment of Carbon Flux and Soil Moisture in Wetlands Applying Sentinel-1 Data
## 3 Results
### Soil Moisture
The coefficient of determination (R2) between SM and \(\sigma^{\circ}\) VH is comparable to the R2 obtained between SM and \(\sigma^{\circ}\) VV. The R2 are slightly lower from that obtained for particular habitat classes separately (Table 4). Table 5 presents results of these statistical analyses which could be applied for mapping soil moisture without the need for classification of the vegetation habitat types.
# A Novel Hybrid Unsupervised Learning Approach for Enhanced Cybersecurity in the IoT
## 3 Dataset
### Cic-Ids2017  
The CIC-IDS2017 dataset, created by the Canadian Institute of Cyber Security (CIC) [36], profiles intrusion traffic using a simulated attack scenario involving seven attack families.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Class** & **Type** & **Count** & **Total** \\ \hline Benign & Benign & - & 13,484,708 \\ DDoS & HOIC & 686,012 & 1,263,933 \\  & LOIC-UDP & 1730 & \\  & LOIC-HTTP & 576,191 & \\ DoS & Hulk & 461,912 & 654,300 \\  & GoldenEye & 41,508 & \\  & Slowloris & 10,990 & \\  & SlowHTTPTest & 139,890 & \\ Brute force & FTP & 193,360 & 380,949 \\  & SSH & 187,589 & \\ Bot & Bot & - & 286,191 \\ Infiltration & Infiltration & - & 161,934 \\ Web & Web & 611 & 928 \\  & XSS & 230 & \\  & SQL Injection & 87 & \\ Total instances & & & 16,232,943 \\ \hline \hline \end{tabular}
\end{table}
Table 1: CSECIC-IDS2018 dataset network traffic.  
Figure 3: Distribution of attacks in CSECIC-IDS2018 dataset.  
The testbed infrastructure was partitioned into an attack network and a victim network, each equipped with standard devices such as firewalls, routers, switches, and a variety of operating systems (including Linux, Windows, and Macintosh). The dataset emphasizes a wide range of attacks, anonymity, inclusion of protocols, comprehensive traffic capture, network configuration details, metadata, labeled data samples, and diverse feature characteristics. In contrast to the restricted attack categories found in KDDCUP99 and NSL-KDD, the CIC-IDS2017 dataset encompasses a wider spectrum of attacks, including DDoS, DoS, brute force, XSS, SQL injection, botnet activities, web attacks, and infiltration attempts, as shown in Table 2. The dataset consists of 2,830,540 labeled flows, each containing 83 features categorized using the CICFlowMeter V4.0 tool, resulting in a dataset that is high-dimensional, multiclass, and imbalanced.
## 5 Results and Comparison with Other Models
### Inference Time of the Custom Model vs. the VGG-16-Based Model  
An important aspect to consider when using trained models for large datasets is the inference time. In this criterion, models with shorter times are preferred. For this purpose, the training and inference times of the proposed models were compared. Figure 11a shows the training times (in minutes) and Figure 11b shows the inference times by image (in seconds). All tests were carried out using one of the GPUs (e.g., Nvidia K80, T4, P4 and  
Figure 8: Impact of the optimizer in the transfer-learning-based model in terms of HTER (the lower the better).  
Figure 9: Performance evaluation of the custom model and the transfer-learning-based model: P, R, and F1-score (the higher the better).  
P100) that Google Colaboratory provide for the users; in addition, Tensorflow was used. However, under this configuration the GPU is not selected by the user but by Google.  
According to Figure 11, the VGG-16-based model takes about three times longer to train than the custom model. However, in terms of inference times, the fine-tuned model takes about twelve times longer than the custom model. The big difference between the inference times lies in the number of parameters of each one, while in the custom model it is 16 million, in the transfer-learning-based model it is 82 million. Therefore, the custom model is a good solution for high-volume image classification because it does not require a high-performance device.  
In summary, the VGG-16-based model classifies the original and colorized images more accurately than the custom model (about +12% for F1-score) but requires longer training (\(\times 3\)) and inference times (\(\times 12\)).
## 6 Conclusions  
In this paper, we proposed a federated multi-task learning (FMTL) framework that jointly performs sleep staging and sleep-disordered breathing (SDB) severity classification. By integrating a shared CNN-GRU-attention encoder with task-specific output heads, the framework learns comprehensive representations from multichannel PSG signals. It leverages federated learning to enable cross-institutional collaboration without compromising data privacy. Extensive experiments on three public PSG datasets simulated by clients (APPLES, SHHS, and HMC). For sleep staging, the model achieved accuracies of 85.3% (APPLES), 87.1% (SHHS_rest), and 79.3% (HMC), with [PERSON]'s Kappa scores all exceeding 0.71, reflecting high agreement with expert annotations. For SDB severity classification, it attained macro-F1 scores of 77.6%, 76.4%, and 79.1% on the same datasets, validating its capability to effectively differentiate between normal, mild, moderate, and severe SDB cases. The results demonstrated the model's effectiveness and generalizability under non-IID conditions. The proposed framework consistently achieved high performance in both tasks across diverse populations and clinical settings.  
Conceptualization, S.L. and R.T.; data curation, S.L. and R.T.; formal analysis, Y.W.; methodology, S.L. and R.T.; supervision, Y.W. and Z.W.; writing--original draft, S.L.; writing--review and editing, R.T., Y.W. and Z.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This study uses three publicly available datasets: APPLES, SHHS, and the HMC dataset. The APPLES and SHHS datasets are hosted by the National Sleep Research Resource (NSRR). Access requires formal application and approval through the NSRR platform. Detailed information and access procedures can be found at the following links: APPLES: [[https://sleepdata.org/datasets/apples](https://sleepdata.org/datasets/apples)]([https://sleepdata.org/datasets/apples](https://sleepdata.org/datasets/apples)), accessed on 27 September 2024; SHHS: [[https://sleepdata.org/datasets/shhs](https://sleepdata.org/datasets/shhs)]([https://sleepdata.org/datasets/shhs](https://sleepdata.org/datasets/shhs)), accessed on 15 June 2023. The HMC Sleep Staging dataset is available via PhysioNet for open access. Further details and access instructions are provided at [[https://physionet.org/content/hmc-sleep-staging/1.1/](https://physionet.org/content/hmc-sleep-staging/1.1/)]([https://physionet.org/content/hmc-sleep-staging/1.1/](https://physionet.org/content/hmc-sleep-staging/1.1/)).  
The authors declare no conflicts of interest.
## Glossary  
\begin{tabular}{l l} ADC & Analog to Digital Converter \\ AI & Artificial Intelligence \\ BM & Beamformer \\ DAC & Digital to Analog Converter \\ DT & Digital Twins \\ EIRP & Equivalent Isotropic Radiated Power \\ EM-EH & Electromagnetic Energy Harvesting \\ eMBB & Enhanced Mobile Broadband \\ FCC & Federal Commission of Communications \\ GEO & Geosynchronous Earth Orbit \\ HAPS & High-Altitude Platform Stations \\ HPA & High Power Amplifiers \\ IEEE & Institute of Electrical and Electronic Engineers \\ ITU & International Telecommunication Union \\ IoT & Internet of Things \\ IoV & Internet of Vehicles \\ LEO & Low Earth Orbit \\ LNA & Low Noise Amplifier \\ LOS & Line-of-Sight \\ MEMS & Microelectromechanical Systems \\ MIMO & Multiple Input Multiple Output \\ ML & Machine Learning \\ mMTC & Massive Machine Type Communications \\ NTN & Non-Terrestrial Networks \\ OAT & Over-the-Air \\ PAE & Power Added Efficiency \\ PCB & Printed Circuit Board \\ \end{tabular}
# Online Learning-Based Adaptive Device-Free Localization in Time-Varying Indoor Environment
## 3 Preliminaries
### CSI Primer  
With the use of technologies such as MIMO and OFDM in WiFi networks, it has become possible to obtain CSI in commercial WiFi devices [29]. CSI is a physical quantity that describes the channel properties of the communication link between the transmitter and receiver and describes the fading factor of the signal on each transmission path and characterizes the phase and amplitude information of each subcarrier in the channel [25,30]. The signal transmission model is as follows  
\[\mathbf{R}=\mathbf{Z}\mathbf{O}+\mathbf{u}, \tag{1}\]  
where \(\mathbf{R}\) and \(\mathbf{O}\) are the received and transmitted signal vectors, respectively, \(\mathbf{Z}\) is the channel frequency response matrix and \(\mathbf{u}\) is the noise vector. Normally, the channel frequency response needs to be obtained with the help of specialized instruments, but it is now possible to obtain the channel frequency response in the form of channel status information using the CSITool. The channel frequency response can be expressed as  
\[\mathbf{Z}=[CSI_{1},CSI_{2},\ldots CSI_{k},\ldots,CSI_{M}]^{T}, \tag{2}\]  
where \(M\) denotes the number of subcarriers in the orthogonal frequency division multiplexing system and \(CSI_{k}\) denotes the frequency response of the \(k\)th subcarrier. \(CSI_{k}\) can be expressed as  
\[CSI_{k}=|CSI_{k}|e^{i\theta_{k}}, \tag{3}\]  
where \(|CSI_{k}|\) is the amplitude information and \(\theta_{k}\) is the phase information of the \(k\)th subcarrier.
# Modelling of ML-Enablers in 5G Radio Access Network-Conceptual Proposal of Computational Framework
## 3 Applying AI/ML into 5G RAN: Proposed Methodology
### Input Data--Classification Analysis
#### 3.2.2 Key Performance Indicators for RAN Optimization Use Cases
To increase the chances to supply automated data collection for a particular target use case, the radio measurements accessible through MDT configuration and reporting procedures are identified as applicable to the AI/ML framework also [10]. Such a set of the key performance indicators for RAN Optimization specific use cases, for ML-driven data collection (accessible via RRC procedures), can be grouped as indicated in Table 2.  
For the purpose of the ML-based evaluation method, we consider the network function requests that parametrized input data to train an ML model that is RAN optimization use case specific (as per Table 2). Consequently, the input data function may target the data set to be denoted as:  
\[B=\{\textit{Coverage, Mobility, QoS}\}, \tag{4}\]  
where coverage, mobility, and \(QoS\) denote key performance measurements taken per RAN optimization use case. Network function targeting measurement results taken specifically only for a single RAN optimization use case (e.g., coverage optimization) admits a subset of dataset B:  
\[b_{\textit{Coverage}}=\{\textit{RSRP, RSRQ, SINR}\}. \tag{5}\]  
As a result, each metric collected as an individual entry, or a subset of dataset B will represent a key performance indicator of the RAN Optimization use case.
# Chitin and Chitosan in the Alcoholic and Non-Alcoholic Beverage Industry: An Overview
## 4 Chitosan Immobilization  
Chitosan, like alginate, forms a gel by ionotropic gelation (or coacervation) and is a polymer with numerous applications in immobilization technology of enzymes and microorganisms, due to its nontoxic, biocompatible, biodegradable, and antimicrobial properties [111].  
Chitosan-bearing protonated amino groups can interact with a broad variety of natural or synthetic anionic species, such as negatively charged proteins, DNA [112; 113], and synthetic basic polymers, such as sodium tripolyphosphate (TPP) [114; 115] to form ionic complexes. Ionotropic gelation has been used in the production of polymeric (micro and nan) particles for many applications, namely in biomedicine [116], and the pharmaceutical industry, including interferon [117; 118; 119] and antioxidant administration [120]. This technique is versatile and relatively simple. It is possible to produce particles in a wide range of sizes [121]. This ionic gelation method to prepare Chitosan/TPP nanoparticles presents the advantages of simple operation, low equipment requirements, low cost, good repeatability, environmentally friendly, and easy large-scale preparation [122; 123], Figure 2.  
In the literature, three other immobilization methods are mentioned: (i) solvent evaporation method, (ii) neutralization method, and (iii) crosslinking method [124]:  
1. Solvent evaporation method or its variant-spray drying method [125] is mostly used for the preparation of membranes and films, effective in the preparation of enzymatically active surfaces placed on tips of electrodes [124] or for the entrapment of the hydrophobic drugs [126; 127].
2. The neutralization method is usually exploited to produce chitosan precipitates, membranes, fibers, and spherical beads of different sizes and porosities. Beads are obtained by dropping a chitosan solution into a solution of NaOH prepared in water-ethanol mixtures once ethanol facilitates the solidification of the beads [124]. Nowadays, this method is also used to produce chitosan films with favorable physical and barrier properties for improving the preservation of chilled meat [128].
3. Crosslinking method where an acidic chitosan solution is exposed to straightforward crosslinking by mixing with a crosslinking agent, which results in gelling [124]. Recently, a new emulsification-crosslinking method was developed by [PERSON] et al. [129] for the preparation of chitosan microspheres using an aqueous alkali-urea solution, as an alternative to the commonly used acidic solvents, to dissolve chitosan.  
Figure 2: Schematic illustration of the preparation procedure for protein chitosan immobilization. Protein-loaded chitosan beads can be prepared by an ionic gelation technique. Chitosan (Chit) is firstly mixed with protein (Prot) to provide a homogeneous solution (Chit + Prot). Then interacts with sodium tripolyphosphate (TPP) solution to allow the protein to be encapsulated during the ionic gelation process.
## 2 Materials and Methods
### The Structure of the CNN  
To reveal the correspondence between geometric characteristics and TSOM images, we resize the TSOM image to 256 \(\times\) 64 pixels, and then construct a suitable CNN model for training. CNN models usually consist of a convolution layer, pooling layer, activation layer, and full connection layer. An improved CNN model is designed based on the traditional CNN model, as shown in Figure 6. The TSOM image is a grayscale image, so the single-channel image can be convolved. There are 5 convolution layers (including two \(3\times 5\) kernels and three \(3\times 3\) kernels), 4 pooling layers (being applied to the first four convolution layers), and 4 full connection layers in the CNN model. All convolution layers contain zero padding. The ReLU function is used as the activation function, and the maximum pooling function is used as the pooling layer. The parameters of the CNN model can be found in Figure 6. The mean-square loss is used as the final loss function to calculate the mean-square error between the output values and the actual values. The loss function equation is:  
\[loss(x_{i},y_{i})=(x_{i}-y_{i})^{2} \tag{1}\]  
where \(x_{i}\) indicates the true value of the target and \(y_{i}\) indicates the predicted value of the CNN model.  
Figure 5: Process to collect TSOM image dataset.  
Figure 6: Schematic diagram of improved CNN model.  
The training image set and test image set contain 3000 and 200 TSOM images, respectively, which are generated from one trench set. SEM is used to measure the width and depth by the cross-section of the trench center position, and the SEM result is taken as labels. The training data set and SEM results are applied to train the CNN model for learning the correspondence of TSOM images and trench parameters. The trained model is evaluated by the test data set.