esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Mercury Removal Performance of NbSs  
#### 3.3.1 Hg Removal and MeHg Changes in NbSs  
In line with confirmations of mercury pollution in soils, there has also been a research focus on cost-effective and sustainable treatment technologies like NbSs in recent years. Among the studied articles for the review on NbS treatment efficiency of mercury, 64% focused on constructed wetlands. Upon synthesis, constructed wetland performance was found to vary by configuration, with engineered systems supplemented with wetlands having unconventional soils and coagulants achieving the highest removal, while simpler plant-based systems still offered substantial uptake and ecological benefits. As summarized in Table 2, an inverse relationship between total mercury (THg) removal and MeHg production in NbSs was revealed. [PERSON] et al. [38], using a surface flow constructed wetland amended with Fe, reported 98.9% THg removal and a 33.7% MeHg reduction (\(p<0.01\)). [PERSON] et al. [39] observed similar trends in a vertical flow constructed wetland with biochar mesocosoms, with 92% THg removal and 43% MeHg reduction (\(p<0.05\)). In contrast, [PERSON] et al. [40], working with constructed wetland mesocosoms, achieved only 50% THg removal and noted up to an eightfold MeHg increase in summer (\(p=0.0001\)). [PERSON] et al. [41], using biochar-amended pots, reported 63% THg removal with a similar increase in MeHg (\(p<0.05\)). These findings suggest that lower THg retention may leave more bioavailable Hg for methylation, highlighting the need to assess both THg and MeHg in NbS evaluations. These findings align with established principles on methylation. According to the key literature, the process of mercury methylation in NbSs is largely driven by microbial and geochemical conditions. Sulfate-reducing bacteria (SRB) and iron-reducing bacteria (IRB) are known to be the primary methylators of inorganic mercury under anoxic conditions, converting Hg\({}^{2+}\) into the more toxic and bioaccumulative methylmercury [42; 43].
# A Low-Loss Circuit with High-Pass Low-Pass Broadband Flat Negative Group Delay Characteristics
## 2 Circuit Design and Analysis with High-Pass and Low-Pass Negative Group Delay
### High-Pass Flat Negative Group Delay Circuit
#### 2.1.6 High-Pass Negative Group Delay Circuits with Different \(L_{3}\) Values  
When the values of \(C_{1}\), \(R_{1}\), \(L_{1}\), \(R_{2}\), and \(L_{2}\) in the circuit remain unchanged from Table 1, we change the values of \(L_{3}\) to 5 nH, 50 nH, and 100 nH in sequence and observe the changes in the circuit transfer function and group delay. The circuit results in these three cases are shown in Table 7 and Figures 15 and 16. Through observation, it is found that, as the value of inductance \(L_{3}\) increases, the group delay of the high-pass circuit at 0 MHz increases. As the value of inductance \(L_{3}\) increases, the negative group delay value decreases, and the circuit insertion loss increases with the increase in inductance \(L_{3}\).
# Biogenic Nanoparticles: Synthesis, Characterisation and Applications
## 3 Applications  
### Medical Applications  
Advanced development in nanoparticles research has led to their increasing applications, in particular, recently in the biomedical sciences, including diagnostic, drug delivery systems, biosensing and bioimaging [6]. For example, different branches of medicine have taken an interest in nanoparticles as they can deliver the optimal dosage of drugs, result in improved efficiency and reduced side effects [89]. Based on their optical properties, NPs are selected in order to provide efficient contrast for biological and cell imaging applications, where the absorption and scattering properties and optical resonance wavelength of NPs such as Au and Ag can be calculated.  
Recently, magnetic nanoparticles such as magnetite (Fe\({}_{3}\)O\({}_{4}\)) have also been employed for biomedical applications [90]. Super magnetic iron oxide nanoparticles with the right chemistry are used in research to improve MRI, drug delivery, cell separation and immunoassays. For these applications, NPs must be small with high magnetization, have dimensions of no more than 100 nm and have a uniform size distribution ([PERSON] et al., 2010). Labelling antibodies to carry out identification of tissues is achieved by using a variety of dyes, enzymes, radioactive compounds or AuNPs ([PERSON] and [PERSON], 2010b) [91].  
Semiconductor and metal nanoparticles show enormous promise of cancer detection and therapy due to the light scattering and absorption properties. Gold nanoparticles can transform the absorbed electromagnetic energy from an electromagnetic field into heat and can be used to target specific cancerous tissues. In a study on Au-silica NPs for prostate cancer treatment [92], gold-silica nanoshells (GSNs) were combined with MRI-ultrasound imaging to selectively ablate prostate tumorous cells. These NPs absorbed NIR light, which caused an increase in temperature, in turn leading to hyperthermia and tumorous cell death.  
Silver NPs have been extensively used in wound dressings, catheters and numerous domestic products because of its antimicrobial effect, particularly compared to traditional silver ion solutions. Antimicrobial agents are essential in water disinfection, medicinal applications, textile and food packaging. The NPs are functionalized with different groups with the purpose of overcoming the varied bacteria populations. Materials such as ZnO, TiO\({}_{2}\), Cu- and Ni-based NPs, are being used for specific functions because of their ideal antibacterial activities.
# Improving Low-Frequency Panel Absorbers with Two-Dimensional Acoustic Black Hole
## 1 Introduction
The effects of different geometrical parameters and their coupling has been studied for a 1D ABH beam by [PERSON] [26] and [PERSON] [23]. Alternatively, tuned mass dampers [27] or active control patches [28; 29] can be used for improving the low-frequency behaviour. [PERSON] et al. [30] investigated the effects of ABH parameters such as size, orientation, number, residual thickness and damping layer on the sound insulation of circular plates. The experimental and numerical results showed that the circular plate containing a single ABH in the center position can enhance the transmission loss. It has also been claimed that the orientation of the ABH side has a small effect on the sound transmission loss of the studied plate. [PERSON] et al. [31] investigated the mid- and low-frequency performance of plates embedded with an array of ABHs. The energy focalization and sound radiation were investigated using FE models. This resulted in the better energy-gathering performance of the plate with the embedded ABH array compared to the plates embedding a large ABH or a single ABH. This paper focuses on improving the sound absorption of panels using a single 2D acoustic black hole. It is aimed at enhancing the modal behaviour of a panel absorber to increase the mode density of the absorber and benefit from local ABH modes. This paper is organized as follows: Section 2 contains the details of the investigation case and the numerical modeling procedure including Acoustic Black Hole generation, the meshing details and boundary and loading conditions. The results of the parametric analyses on the position, residual thickness and size of the ABH are given in Section 3. The experimental validations including the vibration and sound absorption measurements are presented in Section 4.
# Spectrum Demand Forecasting for IoT Services
## 3 A Market Study
### Services  
A service labeled as to be part of IoT is based on ambiguous commercial definitions. Several lists of very different IoT services can be found in the literature [1; 37], thus, for spectrum forecasting, it is necessary to find your own country's list. An initial step could be finding the main economic sectors and their IoT products and services. This step should not exclude a complete review of other economic sectors and services that already exist, for example, in neighboring countries. Special attention should be put on future IoT services that are clearly more difficult to identify. The following aspects should provide important hints:  
* Innovative technologies found in Section 3.1.
* IoT services already offered in developed or neighbor countries.
* Big industries that have not adopted IoT yet.
# Hierarchical Active Tracking Control for UAVs via Deep Reinforcement Learning
## 3 Methodology
### Auxiliary Segmentation and Motion-in-Depth Loss
#### 3.5.1 Auxiliary Segmentation Loss  
In the spatial features encoder, the output latent features are supposed to represent the spatial information of the dynamic target in our design. To provide denser training signals, we add the auxiliary segmentation loss after the convolutional block attention module, as shown in Figure 3.  
The output map of the convolutional block attention module is denoted as \(\mathbf{Z}_{n}\), and the ground-truth segmentation map is denoted as \(\mathbf{G}_{n}\) given input \(\mathbf{O}_{n}\), where \(\mathbf{Z}_{n}\) is followed by the sigmoid activation function \(\sigma\) to predict the probability map \(\mathbf{P}_{n}\) for each pixel in \(\mathbf{Z}_{n}\):  
\[p_{ij}^{n}=\sigma(z_{i,j})=\frac{1}{1+\exp(-z_{i,j})}\text{ for }(i,j)\in \mathbf{Z}_{n} \tag{13}\]  
Then, the auxiliary segmentation loss is the binary cross-entropy between the predicted probability \(p_{i,j}^{n}\) and ground truth \(g_{ij}^{n}\) for each pixel in \(\mathbf{Z}_{n}\):  
\[\begin{split}\mathcal{L}^{\text{Seg}}=&-\sum_{n=t- 2}^{t}\frac{1}{|\mathbf{P}_{n}|}\\ &\sum_{(i,j)\in\mathbf{P}_{n}}g_{ij}^{n}\log p_{ij}^{n}+\Big{(}1- g_{ij}^{n}\Big{)}\log\Big{(}1-p_{ij}^{n}\Big{)}\end{split} \tag{14}\]  
where \(g_{ij}^{n}\in\{0,1\}\), and \(1\) corresponds to the target class, while \(0\) corresponds to the background class.
## 4 Biological Applications
### Therapeutic Applications  
Nanoparticles offer a novel platform for the development of effective therapeutic treatments that offer specificity. The nanoparticles can be designed in such a way that allows the specific binding of a biological target to provoke a desired response. Both plasmonic and magnetic materials have proven to be useful in the thermal treatment of cancer. Likewise, drug-delivery systems that exploit the properties of these types of nanoparticles to release therapeutic agents have also received attention. In the following sections, we discuss treatments that are dependent on the optical or magnetic properties afforded by bimetallic nanoparticles for a variety of thermal treatments and drug-delivery applications. Table 2 provides a summary of the biological applications of bimetallic nanoparticles and the types of nanoparticles used in those applications.
## 3 Support for Real-Time Applications
Furthermore, the OS needs to provide an implementation of a routing protocol that constructs route according to the QoS requirements of applications. Moreover, it must support a MAC algorithm that schedules packets w.r.t. their priority.
## 3 Results and Discussion
### TPC of CSO  
The predicted TPC of the CSO of center point STDs 9, 10, and 11 (restriction die: 0.6 cm, temperature: 60 \({}^{\circ}\)C, and sieve size \(>\) 1.4 mm) was 11.73 \(\upmu\)g GAE/g of oil, whereas the actual TPC of the samples were 12.16, 9.90, and 14.31 \(\upmu\)g GAE/g of oil, respectively. Other center point STDs 20, 21, and 22 (restriction die: 0.6 cm, temperature: 60 \({}^{\circ}\)C, and sieve size \(<\) 1.4 mm) showed a predicted TPC of 18.86 \(\upmu\)g GAE/g of oil, whereas the actual values were 19.69, 26.04, and 18.93 \(\upmu\)g GAE/g of oil, respectively. A high TPC of 44.16 \(\upmu\)g GAE/g of oil was observed in STD 4 (Table 2).  
Analysis of the variance of the reduced cubic model for TPC showed that the model was significant (\(p\) = 0.0003) with adjusted R\({}^{2}\) (0.7228), predicted R\({}^{2}\) (0.5245), and nonsignificant lack of fit (\(p\) = 0.2398) values (Table 1). The results indicated that the reduced cubic model equation was acceptable for predicting the TPC of the CSO samples (Figure 4). The CCD-generated reduced cubic equation for TPC (\(\upmu\)g GAE/g of oil) was as follows (Equations (10) and (11)):  
Total phenolic content \[(\upmu\text{g GAE/g oil})=10^{2.0791-4.4564\text{A}}\ -0.0034\text{B}\ +0.0215\text{AB}\ +3.0426\text{A}^{2}\ (\text{For sieve size}\ >\ 1.4\ \text{mm})\] (10)  
Total phenolic content \[(\upmu\text{g GAE/g oil})=10^{0.1322-3.3970\text{A}}\ -0.0034\text{B}\ +0.0215\text{AB}\ -4.0654\text{A}^{2}\ (\text{For sieve size}\ <\ 1.4\ \text{mm})\] (11)  
where A = restriction die (0.4-0.8 mm) and B = temperature (40-80 \({}^{\circ}\)C).  
The TPC of the CSO was greatly affected by the extraction temperature (Figure 4). High TPC (44.16 \(\upmu\)g GAE/g of oil) was noted in the CSO samples extracted with the conditions of restriction die: 0.8 cm, temperature: 80 \({}^{\circ}\)C, and sieve size \(>\) 14 mm. The higher the extraction temperature, the higher the TPC values. Sieve size and restriction die had no influence on the TPC of the CSO samples. However, the impact of restriction dies on TPC was influenced by the sieve size (Figure 4). Similarly, virgin rapeseed oil extracted at higher temperatures has greater nutritional value [34]. Although thermal processes used in the roasting of cocoa seeds may degrade the phenolic compounds, the overall antioxidant properties of the roasted cocoa seeds can be maintained or enhanced by the formation of new antioxidants, such as the Maillard reaction products [36; 37].  
Figure 3: Response surface plot showing the effect of temperature and restriction die size on the peroxide value of CSO. (**a**) Sieve size: \(>\)1.4 mm, (**b**) Sieve size: \(<\)1.4 mm. Sieve size had no influence on the peroxide value of the CSO.
## 2 Overview of Modern Technologies and Frameworks in Smart Healthcare Systems
### Blockchain-Enabled Healthcare Systems  
One of the imperative requirements of the healthcare sector is to secure medical data (patient data) and efficiently manage different supply chains, such as pharmaceuticals, human organ donation, and medical appliances or infrastructure of smart healthcare ecosystems. Blockchain is a prominent technology that provides security, reliability, privacy, and interoperability, thereby transforming the healthcare system [40]. Blockchain has an immutable and decentralized ledger, where medical data are securely stored and safeguarded from data integrity attacks. The ledger is protected from cryptographic features, such as asymmetric keys, hashing, and digital signatures, making the data are tampered-proof [41]. Moreover, the ledger is decentralized, so if any small change occurs in the data transaction, it will be known by each blockchain member, thereby improving improving the transparency of the overall system. As the healthcare domain is always at a high risk of being exploited by attackers, leveraging blockchain technology facilitates secure patient data exchange, prevents disruptions, and efficiently manages medical resources. The authors of [42] presented a blockchain-based smart healthcare system to protect the data privacy of various healthcare system users. They developed different types of smart contracts to validate data transactions, access control, and decision-making in an open network. In addition, they utilized differential privacy mechanisms to preserve users' privacy. Their results show that the proposed scheme outperforms others in terms of reliability, stability, and system-level traceability [43].  
[PERSON] et al. in [28] proposed a secure and reliable blockchain-based scheme to counter security attacks on electronic healthcare record systems. They used private blockchain and swarm intelligence approaches to ensure secure data exchange across the IoT network. Further, [PERSON] et al. in [30] explored the use-case of blockchain technology in tackling diabetes disorder, especially during the COVID-19 pandemic. Using blockchain technology, they prioritized healthcare resources, such as medical beds, oxygen, insulin pumps, telemedicine, and a constant monitoring system for diabetic patients who were mostly affected by the pandemic. They applied blockchain-based technologies, such as interplanetary file systems, smart contracts, and the new economy movement, to encrypt and authenticate patient data and develop a proof-of-concept model. Their proposed system shows notable outcomes in terms of transaction speed, transaction fees, and power consumption.
## 1 Introduction  
Energy exploration and extraction have progressively veered towards complex reservoirs, including carbonates and tight sandstones, underscored by intricate and non-homogeneous pore structures. These complex formations are pivotal in addressing the escalating global energy demands, prompting advancements in rock physics and related empirical and theoretical models.  
The exploration of the nuanced interplay between microstructural geometries and macroscopic percolation properties of rocks has historically relied on experimental measurements and numerical simulations [1; 2; 3; 4]. Traditional digital rock reconstruction methods have been bifurcated into physical experiment methods like CT and SEM and numerical reconstruction methods leveraging statistical information [5; 6; 7]. While physical methods offer precision, they are often hampered by high costs and specialized equipment needs. Conversely, numerical methods, though cost-effective, sometimes compromise on the connectivity and complexity of the reconstructed rocks [8; 9].  
Deep learning, and particularly the ascent of GANs, has significantly ameliorated these challenges, enabling the generation of detailed and complex 3D digital rock models efficiently [10; 11; 12; 13]. These methods not only enhance the reconstruction quality but also provide avenues for the exploration of latent spaces and generation of more continuous and controllable images [14; 15].  
Recent advancements have seen the integration of prior information and conditional generative models to increase the diversity and control in digital rock reconstruction. [PERSON] et al. (2022) [16], [PERSON] et al. (2023) [17], and [PERSON] et al. (2023) [18] have pioneered methods that interweave deep learning with conditional information, achieving unprecedented precision in digital rock modeling. These efforts mark significant strides towards generating more natural, user-specified digital rock samples.  
Such integrative approaches, capitalizing on the strengths of both traditional and advanced methodologies, underscore the rapid evolution in the field of digital rock physics. The amalgamation of intricate experimental measurements, computational advancements, and deep learning is paving the way for unprecedented opportunities and challenges in the comprehensive understanding and optimization of complex reservoirs.
## 4 Performed Tests and Conditions
### UV Resistance Test  
The UV resistance test was performed according to ISO 4892-3:2016-04 [22]. In the case of the test material, the UV resistance (UVB-313 fluorescent lamps (Klimatest, Wroclaw, Poland)), was determined using method B-cycles no. 5. One test cycle (12 h) was conducted according to the following parameters [8]:  
* Sunlight period UVB fluorescent lamp 313 type 2 (40 W): 8 h;
* Temperature of the black plate: 50 \(\pm\) 1 \({}^{\circ}\)C (deviation from the standard: in the standard 70);
* Light irradiance: 0.48 W/m\({}^{2}\);
* UVB-free condensation period: 4 h at 50 \(\pm\) 1 \({}^{\circ}\)C on black tile.  
Figure 5: Samples mounted in the device during the solar resistance test (tested under the light of an arc xenon lamp).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Period of Exposure** & \
\begin{tabular}{c} **102 min of Light** \\ **Exposure** \\ \end{tabular} &
\begin{tabular}{c} **18 min of Light Exposure** \\ **and Water Spraying** \\ \end{tabular} \\ \hline Irradiance during exposure W/m\({}^{2}\) & 60 \(\pm\) 2 & 60 \(\pm\) 2 \\ (wavelength: 300–400 nm) & 65 \(\pm\) 3 & - \\ \hline Black plate temperature \({}^{\circ}\)C & 65 \(\pm\) 3 & - \\ \hline Chamber temperature \({}^{\circ}\)C & 38 \(\pm\) 3 & - \\ \hline Relative humidity \% & 50 \(\pm\) 10 & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ageing conditions for cycle 4 of method A.  
Deviation from the standard: the lower temperature of the black tile is due to the thermal strength of the cured product (without annealing), which, according to the technical data sheet of the epoxy system used in the samples, is between \(-10\) and \(50\)\({}^{\circ}\)C. Therefore, the temperature of the black plate was reduced so as not to tighten up the test too much. The test was performed on an Atlas UV Tester (Klimatest, Wroclaw, Poland), which is a UV fluorescence test instrument that allows simultaneous condensation of the samples under selected atmospheric conditions. The appearance of the specimens during the test is shown in Figure 6. Initially, the test specimens were planned to be exposed to UVB light for 36 cycles (432 h), but due to the strong degradation of the resin observed, it was decided to reduce the duration of the test to 24 cycles (288 h).
## 4 Security Attacks
### Application Layer
#### 4.5.2 Attacks on Cold Wallets  
Cold wallets, also known as blockchain hardware wallets [27; 31], store private keys offline and are generally considered more secure than their online counterparts. However, these wallets can still be compromised through sophisticated methods such as bug implantation. For example, an Evil Maid attack involves an adversary physically accessing a device and implanting malicious software that extracts private keys, PINs, recovery seeds, and passphrases from the wallet. In one notable incident in 2019, during a routine transfer of funds to a cold wallet at the UPbit cryptocurrency exchange, attackers exploited timing vulnerabilities to steal $342,000 worth of ETH. This incident highlights that even offline storage solutions can be vulnerable if physical or procedural security measures are inadequate.