esa-sceva/satcom-chunk-collection huggingface dataset:
# Wireless Caching: Making Radio Access Networks More than Bit-Pipelines
## 2 Proactive Service: Gains, Costs, and Needs
### A Case Study: Caching-Enhanced mmWave Coverage  
Caching enables a low-cost and flexible deployment of base stations (BSs) working in mmWave or even Thz bands. Transmissions over extremely high frequency bands provide much wider bandwidth and much higher throughput but suffer from poor coverage due to severe path loss and blocking. To overcome this, massive multiple-input multiple-output (MIMO) and intelligent reflecting surfaces (IRS) have been extensively studied.  
Caching provides an alternative solution [20]. With caching, mmWave BSs do not necessarily cover every corner of the whole area. As shown in Figure 2, mobile users enter and leave the area covered by mmWave signals dynamically. When high-speed access is available, a user will cache, as much as possible, popular content in which it may be interested. After leaving the mmWave covered area, the user may find its requests partially served by its local cache. The cache-empowered hierarchical RAN improves the user's QoS without dense deployment of mmWave BSs. Similarly, this idea is helpful when there is a human body blocking effect.  
Caching also allows mmWave BSs to sleep. Therefore, they can be powered by unstable renewable energy. Further, when wireless fronthaul and backhaul are available, the deployment of energy-harvesting-aided mmWave BSs is made more flexible. There is no need to build fixed infrastructures, including a power grid and wired fronthaul and backhaul, for mmWave BSs. Once a site is available, they can be simply placed there. To avoid inter-cell interference, an intelligent spectrum coordinator allocates the spectrum if a nearby mmWave BS is detected. In this case, the deployment of mmWave BSs becomes highly flexible.
## 2 Materials and Methods
### Identification of Atranorin Biosynthetic Gene Cluster and Phylogeny  
To identify atranorin candidate genes, we downloaded sequences from PKS16 of _Cladonia gravi_ (GenBank: ADM79459; 2089 aa) and PKS23 of _Stereocaulon alpinum_ (GenBank: QXF68953; 2500 aa). Both PKSs were previously reported as possible candidates involved in the biosynthesis of atranorin (see Section 3.1.1 Atranorin in Section 3 Results and Discussion for details). As atranorin synthesis involves an oxidation, the cluster must contain a cytochrome P450 as well as additional genes, \(O\)-methyltransferasease (OMT), and transporter gene [29]. The presence of these genes was verified by our annotation results, showing OMT (PF08241), cytochrome P450 (PF00067), and transporter gene (PF00400) present in the putative atranorin BGC in _B. rubella_. The gene arrows plot, comparing two atranorin clusters in _Bacidia rubella_ and _Cladonia rangiferina_, was drawn using the gggenes v0.4.1 R package ([[https://github.com/wilkox/gggenes/](https://github.com/wilkox/gggenes/)]([https://github.com/wilkox/gggenes/](https://github.com/wilkox/gggenes/)); accessed on 20 April 2022).  
We used BLASTP v2.9.0+ [46] to detect the orthologs of those characterized sequences in all PKS sequences we identified on the twenty-three studied genomes. We filtered BLAST output to retain sequences with a minimum identity of 30% over the alignment length and a minimum query coverage of 50%, sorted for the highest bit score and lowest e-value. Additionally, we used the best blast subject hits with the highest percent similarity to the query gene. The best hits from the blast results (>30%) were selected for the PKS23 alignment. The taxon selection was confirmed with the clade from the large TI-PKS phylogenetic tree.  
We aligned the 29 identified amino acid sequences using MAFFT v7.480 [47] and calculated the maximum likelihood using IQ-TREE v2.1.4 [48] with 1000 ultrafast bootstrap replicates, after selecting the best-fitting substitution model (LG+G8+F) with ModelFinder [49]. The final alignment comprised 29 sequences from 16 taxa, with 2320 amino acid sites, 2071 distinct patterns, 1756 parsimony-informative, 221 singleton sites, and 343 constant sites.
## 4 Experiment Results
### Comparison of Accuracy, Computational Load, and Inference Speed  
To evaluate the practical feasibility of deploying deep learning models for pharmaceutical classification, we compared multiple architectures in terms of accuracy, inference speed, and computational efficiency. These factors are critical in determining the suitability of a model for real-time mobile applications, where both classification precision and processing latency must be optimized.  
Figure 14: Heatmap of model classification accuracy by pharmaceutical type. Red areas indicate high accuracy, while blue areas represent low classification performance. Given its strong predictive capability for pharmaceuticals, the ResNet-101 model demonstrates the highest performance in individual drug classification.  
Figure 15 illustrates the inference speed comparison of different deep learning models. As shown in the figure, MobileNet and SqueezeNet exhibit the fastest inference times, making them highly efficient for real-time applications. However, their low classification accuracy (below 2%) makes them unsuitable for high-precision pharmaceutical identification. In contrast, ResNet101, despite having a slightly higher inference time (58.72 ms), offers the highest classification accuracy (98.51%). Therefore, ResNet101 provides an optimal balance between accuracy and computational efficiency, making it the best candidate for real-time mobile-based pharmaceutical classification.  
To complement the analysis presented in Figure 15, Table 2 provides a detailed comparison of model accuracy, inference speed, and model size. The results indicate that ResNet101 outperforms all other architectures in classification accuracy (98.51%), while maintaining a reasonable inference time (58.72 ms) and a manageable model size (181 MB). Although EfficientNet B6 and ResNet152 show competitive accuracy levels (95.40% and 95.94%, respectively), they require longer inference times (86.26 ms and 80.49 ms), making them less efficient for real-time deployment.  
On the other hand, DenseNet models demonstrate moderate accuracy levels (60.48-81.75%) but exhibit inferior inference speeds compared to ResNet architectures, highlighting the limitations of densely connected feature propagation for large-scale image classification tasks. Meanwhile, MobileNet and SqueezeNet models, despite their lightweight structure and rapid inference times (23.07-28.35 ms), fail to achieve adequate classification accuracy (<2%), rendering them impractical for pharmaceutical identification.  
These results further emphasize that ResNet101 provides the optimal trade-off between classification accuracy and computational cost, making it the most suitable architecture for real-time pharmaceutical classification on mobile platforms. The findings also highlight the limitations of lightweight CNNs, such as MobileNet and SqueezeNet, which, despite their efficiency, fail to meet classification accuracy requirements for pharmaceutical identification.  
Figure 15: Inference speed comparison of different deep learning architectures for pharmaceutical classification.
## 3 Results and Discussion  
### Quality Control of the Analytical Data  
The accuracy of the analytical procedures for the determination of the Cd, Pb, and Zn concentrations in extracts from the sequential extraction procedure and the total element concentrations in the soil samples was checked by analyzing the SPS-SW1 Reference material for the measurements of elements in surface waters and certified reference materials CRM 320R Trace elements in river sediment. As evident from data given in Tables S2 and S3, the determined concentrations of the elements agreed well with the reported certified values (the agreement between the results was better than \(\pm\)5%), which confirmed the accuracy of the analytical procedures. The expanded uncertainty of the analytical procedures applied was better than \(\pm\)3% (\(k\) = 2).
# Enhancing Soft Tissue Differentiation with Different Dual-Energy CT Systems: A Phantom Study
## 1 Introduction
Several studies on phantoms and patients have evaluated the effect of the technical differences of the DECT platforms on spectral separation and performance [6; 7; 8; 9; 10; 11; 12; 13; 14; 15]. Data resulting from the low- and high-energy acquisitions can be combined in multiple ways to produce images suitable for diagnostic radiology or radiotherapy treatment planning, encouraging the use of DECT imaging in clinical practice. It has been demonstrated that the virtual monochromatic images (VMI) reconstructed at energies greater than 100 keV allow for reducing beam hardening, scatter, and metal artifacts with respect to SECT imaging [16; 17]. Moreover, VMI may even present a better quality as compared to a standard CT acquired at the same dose; when the optimal energy is selected, a better visualization of contrast-enhanced tissues and noise reduction can be obtained [18; 19; 20; 21; 22]. DECT data may also be used for the classification of materials [23; 24]. The possibility to obtain the effective atomic number (Zeff) and electron density (\(\rho_{\text{el}}\)) maps of tissues yields a more precise dose calculation with protons and photons in radiotherapy [25; 26]. Finally, DECT may be employed for the virtual suppression of the contrast medium to visualize which image regions were enhanced and how they would appear without iodine or, alternatively, to create maps reflecting the iodine distribution in tissues. The virtual non-contrast-enhanced DECT imaging is useful for a more accurate dose calculation in radiotherapy treatment planning to avoid the tissue composition manual attribution needed in iodine-enhanced structures [8; 27; 28]. Although several studies explored the potential of DECT images in diagnostic radiology for improving tasks such as iodine quantification and material differentiation, few have focused on the quantitative and qualitative assessment among different technologies. The aim of this study was to quantitatively investigate the advantages of VMI in differentiating various soft tissues compared to single-energy CT images (SECT), considering four CT scanners each representing a different DECT technology. We specifically aimed to find the optimal energy of VMI to differentiate soft tissues of multiple anatomical regions (e.g., breast, abdomen, and pelvis) with similar densities (from 0.96 g/cm\({}^{3}\) of adipose to 1.16 g/cm\({}^{3}\) of trabecular bone) across DECT technologies. Finally, an evaluation was conducted on the performance differences among the various scanners.
## 4 Proposed Scheme
### Signature  
Signing a message \(M\) involves blinding it and using the private key for signing:  
* Blinding the message: The recipient calculates \(M^{\prime}\) such that \[M^{\prime}=M\cdot r(mod\ n)\] (3) where \(r\) is a secret random factor chosen by the recipient.
* Blind signing: The signer calculates the signature \(\sigma^{\prime}\) on the blinded message \(M^{\prime}\): \[\sigma^{\prime}=(M^{\prime})^{K_{priv}}(mod\ n).\] (4) The parameter \(T\) adjusts the level of anonymity by modifying the cryptographic operations.
* Transmitting the blinded signature: The signature \(\sigma^{\prime}\) is transmitted to the recipient.
## Materials and Methods
### Electrochemical Deposition of rGO-AuNP on the ITO Electrode  
GO, gold(III) chloride trihydrate (HAuCl4.3H2O), sodium phosphate monobasic anhydrous (purum p.a. \(\geq\)99.0%, BioUltra), sodium phosphate dibasic anhydrous (\(\geq\)99.5%), and potassium ferrocyanide were obtained from Sigma Aldrich, St. Louis, MO, USA. GO (1 mg/mL) was diluted to prepare 0.5 mg/mL in 25 mM sodium phosphate buffer (pH adjusted to 5.4). HAuCl4.3H2O was dissolved in deionized water (18 M\(\Omega\)cm) to a concentration of 0.5 mM.  
For the electrodeposition of rGO and AuNP onto the ITO electrode surface, the established method [2; 9] was employed. The potential applied on the ITO electrode using a potentiostat (CompactStat; Ivium, Eindhoven, The Netherlands) was \(-\)1.4 V vs. Ag/AgCl for rGO deposition and \(-\)0.9 V vs. Ag/AgCl for AuNP deposition. The rGO-AuNP composites were obtained by deposition using two-step chronoamperometry at \(-\)1.4 V for rGO and at \(-\)0.9 V for AuNP, respectively.  
The optical properties of the ITO deposited by rGO and AuNP were characterized using a UV-Vis spectrophotometer (Optizen POP; Mecasys, Daejeon, Korea). Scanning electron microscopy (EM-30; COXEM, Deajeon, Korea) and transmission electron microscopy (Zeiss LEO 912 AB; Operating at a voltage of CM-200 kV) were used to determine the size and morphology of the decorated AuNPs in the hybrid nanocomposites deposited onto the ITO surface.  
Figure 1: Fabricated indium tin oxide (ITO) electrode that has a circular working electrode with a radius of 250 \(\upmu\)m separated by a distance of 2 mm from the relatively large counter electrode.
## 2 Theoretical Background
### BIM and BEM: Definitions and Synergies  
A clear distinction must be drawn between the building information model (a digital asset enabling stakeholder collaboration [16]) and building information modeling (the process of creating and leveraging this asset). The model serves as a dynamic repository for cost estimation, structural analysis, and--critically--energy simulations. When shared across stakeholders, it enhances design coordination, operational control, and simulation accuracy [17].  
Figure 1: BIM2 BEM-Flow workflow toolchain.  
The translation of BIM data into energy simulation inputs, termed \"BIM2 BEM\" [18], enables iterative energy optimization during the design phase. As illustrated in Figure 2, this workflow operates through feedback loops, where simulations progressively refine design decisions to align with performance-based operational targets. However, its effectiveness hinges on interoperability--the ability to exchange and consistently interpret data across tools. Three interoperability levels underpin reliable BIM2 BEM workflows [19]:  
1. **File/Syntax**: Error-free file exchange between tools.
2. **Visualization**: Accurate geometric representation across platforms.
3. **Semantic**: Shared understanding of data meaning and context.
# The Immobilization of Lipases on Porous Support by Adsorption and Hydrophobic Interaction Method
## 8 Conclusions and Expectation  
Lipase is the most versatile enzyme, which can be absorbed on hydrophobic supports, which experience the interfacial activation during the adsorption process. The adsorption of lipase on a porous support with a hydrophobic surface enhances the stability of lipase after immobilization. The combination of hydrophobic and porous supports is used in many industrial applications due to the low economic value and easy realization on a large scale. Porous carriers have many advantages compared with nonporous carriers. Many studies have shown that porous supports have a better surface area and are able to carry more enzymes with higher immobilization rates. Lipase is also significantly stable in porous supports due to the indirect exposure to the harsh conditions of the reaction process. Thus, the adsorption of lipase on porous supports with hydrophobic properties improves the reusability, pH stability, thermostability, and operational and storage stability of lipase for long-term and large-scale applications.
# Design of Adaptive Kalman Consensus Filters (a-KCF)
## 2 Problem Statement
### Consensus Protocols  
Several types of consensus protocols have been explored, and these have been utilized in many different scenarios over the years. In this paper, we mainly adopt the \"consensus in network\" strategy mentioned in [17].  
Using the background information provided in Section 2.1 and assuming that there are \(n\) integrator agents working cooperatively with dynamics \(\dot{x}_{i}=u_{i}\), the \"consensus in network\" strategy forces those agents to reach an agreement on their states by \(\dot{x}_{i}(t)=\sum_{j\in N_{i}}a_{ij}(\dot{x}_{j}(t)-\dot{x}_{i}(t))\).  
This distributed dynamic structure demonstrates that the \(i\)th agent updates its state by penalizing the state disagreement between its immediate neighbor \(j\in N_{i}\) and itself in order to ensure that all of those \(n\) agents finally agree on a common value as their ultimate state. Via this protocol, which [17] proved to be stable and convergent, we can say consensus is achieved through the communication and cooperation among the agents.  
Furthermore, we can explore the collective dynamics of individual agents in the graph \(\mathcal{G}\), which can be expressed as \(\dot{x}(t)=-Lx(t)\), where \(L\) is the aforementioned graph Laplacian in Section 2.1.
# Using Financial Instruments to Transfer the Information Security Risks
## 1. (a) MALICIOUS ATTACKS
#### 3.1.3 Risk Response  
The risk response strategies can broadly be categorized into four, as follows:  
1. **Avoidance:** This strategy involves avoiding the possible threats and attacks. The source of risk or the asset's exposure to the risk is eliminated. This strategy is usually used in the cases where the impact of the risk is more than the benefit of having that particular asset, such as full internet connectivity to all the computer systems in the company.
2. **Acceptance:** This strategy involves accepting the security risk and its potential impact as a cost of doing the business. This strategy is applied in the cases where the cost of implementing the security control is likely to be higher than the potential impact of the risk materialization.
3. **Reduction (Mitigation):** This strategy aims to reduce the risk exposure by implementing the appropriate security controls, security policies and compliance with regulatory requirements. It is a primary and commonly applied risk management strategy.
4. **Transference:** Risk transferring strategy is applied to partially transfer the risk exposure to a third party by outsourcing the security services or purchasing a cyber-insurance or taking an appropriate position in the information security financial instruments. In the light of various cyber security incidents where technical defenses failed to prevent the attacks, risk-transference strategy can play a vital role in mitigating the residual risk. Risk response objectives and strategies are shown in Table 3.
## 3 Theoretical Context
### Artificial Intelligence (AI) Application
[38] aims to discuss problems such as complex object types, large amounts of data collection, high transmission and computational demand, and weak real-time control and scheduling capability in constructing modern intelligent traffic information, physical fusion networks and cloud-based control. The underlying theory for modern intelligent traffic network control system is a current research topic. In the same way, the best design of the physical scheme is investigated to achieve an integrated control system that allows to link the transport information in an intelligent way. The scheme includes intelligent transportation edge control technology and intelligent transportation network virtualization technology. Based on the intelligent traffic flow data, various deep learning study methods are implemented in the cloud to predict the traffic flow and congestion of urban roads.
# Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review
## 2 Methodology
### Method  
This article reviewed papers concerning IoT solutions for EW systems focus on four different natural disasters: floods, earthquakes, tsunamis, and landslides. For each of these use cases a search process of scientific articles was conducted using four different search engines: Google Scholar, Elsevier, IEEE Xplore, and MDPI. The articles collected in each search process were preliminary filtered by year of publication in order to occupy a time frame spanning from 2017 to 2022 and review more recent papers and solutions. Older key reference papers have been cited when necessary during the discussion of generic architectures, protocols, or methods. Each search process for a chosen search engine brought a great number of results, so efficient search keywords were used. The main focus of the review is on EW systems architectures and IoT-related aspects, so articles that only proposed prediction algorithms, Machine Learning models for disaster forecasting, post-disaster recovery systems, or alert dissemination have not been included among the reviewed literature, unless they also provided the implementation or proposal of an IoT architecture for EW or dealt with some of its specific aspects. The reviewed articles contain (i) proposed or developed IoT and WSN solutions, (ii) methods and simulations regarding sensor networks optimization and performance evaluation, and (iii) considerations about network topologies or other aspects related to the constraints and requirements of an EWS. Taking these aspects into consideration, specific search keywords were selected for each search process of the four use cases examined in the reviewed. For example, keywords such as \"Early Warning System\", \"IoT\", \"Wireless Sensor Networks\", and \"Alert System\" were used for the research for each use case, while for a specific use case such as Tsunami Early Warning other keywords were also used, namely, \"Underwater Sensor Networks\", \"IoUT\", \"Ocean Monitoring\", and \"Sea Waves Monitoring\". Further reduction was obtained by excluding papers that did not directly deal with IoT-related topics and by crossing the specific keywords. The process of selecting research articles ends evaluating whether the selected papers could answer one or more of the Research Questions. For example, the search process of Tsunami Early Warning in the IEEE Xplore search engine brought more than 160 results, which were then reduced to 52 after the preliminary filtering by year. Subsequently, by crossing the keywords the total papers were reduced to a handful, and lastly only three papers were selected from this search engine for the review of this use case.