esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Evaluation
### SI Cancellation by Adaptive Filter  
Instead of using the spectrum analyzer to evaluate the SI cancellation performance of the antenna and AC board, the AF-based digital SI cancellation performance is evaluated  
Figure 11: SI cancellation result by analog circuit.  
Figure 10: Antenna SI isolation result.  
in some digital ways. Specifically, the performance of the digital part is evaluated by investigating the constellation plot, EVM, and BER of the whole system. The evaluated results of the digital part are provided and addressed in the following paragraphs.
## 5 Bioactive Properties of COS  
COS are natural antioxidants [9]. Several studies in vivo have proved that COS can inhibit reactive oxygen species (ROS) and, in consequence, different pathways of the cell damage caused by oxidative stress [64, 65]. The molecular mechanism that explains the antioxidant activities of the different COS families has not been completely elucidated [3], although it was postulated that free amino groups of chitosan could interact with free radicals giving rise to stable macroradicals. COS probably display their biological actions by modulating different targets. Interestingly, COS with a molecular mass lower than 5 kDa have displayed the highest antioxidant activities [66].  
COS display a myriad of bioactivities, including neuroprotective [67], anti-inflammatory [12], antihypertensive [68], antimicrobial [69], antiviral [70], antiangiogenic [71], antitumor [72] and prebiotic [19, 73].  
The reported results on the bioactivity of COS reflect clear discrepancies, which can be explained by the heterogeneity of the synthesized oligosaccharidesamples and the use of different bioassays. In many cases, the chemical composition and molecular weight of COS are not fully determined before the assay. To obtain a reproducible product for bioassays, the raw materials and the extent to which the substrate hydrolysis is allowed to proceed should be carefully controlled.  
COS have shown antimicrobial activity against bacteria, filamentous fungi and yeasts [15, 74]. The advantage of COS vs. chitosan is that the former can be used at pH > 6.5, whereas the polymer is insoluble in water. In this context, COS have been proposed as polycationic biocides [66]. The mechanism by which COS inhibit microbial growth could be related to the interaction of free NH\({}_{2}\) groups of glucosamine moieties with carboxylic residues present in the outer cell membranes of microorganisms [33, 75]. On the other hand, [PERSON] et al. reported that partially acetylated COS (_pa_COS) exhibit better antibacterial activity towards _Listeria monocytogenes_ and _Escherichia coli_ than fully deacetylated COS (_fd_COS), suggesting the essential role of acetamide moieties in the antimicrobial properties of these compounds [76].  
COS is also able to induce the resistance of plants and animals towards various viral infections [70; 77]. Although the mechanism is still unclear, the reduction of infectivity could be associated with a minor absorption of virus onto the cell membrane.  
The molecular features that determine the anti-tumor activity of COS have not been elucidated yet. However, [PERSON] et al. postulated that electrostatic interactions between tumor cells and polycationic chitosan oligosaccharides could alter the membrane permeability of such cells [78]. COS possess great potential as immunostimulatory agents in novel anticancer therapies [79]. The fermentation of COS in vitro and its effect on human fecal microbial community structure and metabolites have been recently reported [80]. In other contexts, COS and their derivatives can be employed in drug delivery of anticancer drugs [81].
# Sustainable Risk Identification Using Formal Ontologies +
## 3 Results
### Risk Definition  
Following the activity #2 guideline of the OnToRisk method, a Vulnerability-Induced Risk concept is formally defined using the established concepts and relations:  
\[\begin{array}{l}\text{vulnerabilityInducedRisk}\equiv\text{vulnerability and}\\ \text{((risksFunction some BusinessFunction) or}\\ \text{(risksInfo some SensitiveInformation))}\end{array} \tag{5}\]  
This formally defines the specific risk as a vulnerability which risks either a business function and/or sensitive information. Ideally, this definition could instantiate new information elements (of the VulnerabilityInducedRisk type). However, due to limitation in both the OWL ontology standard and the Protege ontology authoring tool, instantiation of new elements is not possible by inference, and instead this tags a Vulnerability typed individual element as a VulnerabilityInducedRisk. Accordingly, VulnerabilityInducedRisk is also considered as a subclass of Vulnerability (in addition to being a subclass of Cybersecurity Risk); this is shown in Figure 1. This is merely a technical adaptation, which has no effect on the results as it can be easily interpretated to the ideal case, and we discuss this shortly. The formal definition of the set of risks (\(R\)) in this implementation is:  
\[R\equiv\{v\in V\ |\ \left(\exists x\in BF\ \&\ (v,x)\in RF\right)\ or\ (\left(\exists y\in SI\ \&\ (v,y)\in RI\right)\} \tag{6}\]  
with:  
\(V\)--the set of Vulnerability class (i.e., concept) instantiations  
\(BF\)--the set of Business Function class instantiations  
\(RF\)--the set of risksFunction object properties instantiations  
\(SI\)--the set of Sensitive Information class instantiations  
\(RI\)--the set of risksInfo object properties instantiations  
i.e., the set of risks is a subset of all vulnerabilities that have either a risksFunction object property (stating the vulnerability risks an existing business function) or a riskInfo object property (stating the vulnerability risks existing sensitive information).  
The formal definition itself is more than a technical definition. This is the first concrete layer-3 risk definition, which extends the existing conceptual and abstract layer-2 enterprise risk definition (Cybersecurity risk). This fairly simple, formal ontology-based definition of a \"vulnerability-induced risk\" rigorously expresses a concrete type of risk. This specific risk type is of high importance to enterprise stakeholders, including its high-level management, and had not been declared until our OnToRisk implementation named it explicitly.
# Machine Learning- and Deep Learning-Based Multi-Model System for Hate Speech Detection on Facebook
## 4 Outcomes and Discussion
### Discussion and Limitations
#### 4.9.1 Comparative Analysis  
In comparison with the existing works, our proposed models achieved superior performance, challenging the prior state of the art set on Roman Urdu hate speech detection. More precisely, the LSTM model using FastText embeddings obtained a high F-score of 96.2%, significantly surpassing previous models like the BiLSTM using Word2 Vec embeddings introduced in [34] (reported F-score equal to 89.1%).  
This significant enhancement is mainly due to our optimal use of FastText embeddings, which are known to be effective with morphologically rich languages, including code-mixing [30]. We also improved the preprocessing steps for our methodological approach, after lemmatization and phonetic normalization, specifically towards the linguistic anomaly of Roman Urdu.  
In addition, the ensemble of classical and complex machine learning models used in the current work may also contribute to the generalization and robustness, as well as the generalization power and adaptiveness by tackling the shortfalls observed in prior works [2; 27]. Hybrid techniques, ensemble learning, and transformer-based methods, as indicated by recent advances reported in [1] and also in [1; 9], can be further explored in future studies, ensuring more accurate and robust results.
## 7 Architectural Challenges in Designing Any Smart Healthcare Application
### IoT Connectivity in Smart Healthcare
#### 7.1.2 Noma  
It is proven to be a promising technology by achieving wireless resource utilization [90]. The advantage of NOMA is that it allows the overlapping of signals from various devices using Power Domain Multiplexing (PDM) and Code Domain Multiplexing (CDM). Each device will undergo separate decoding using its Base Station using successive interference cancellation. It is proved that NOMA-based RA with multi-channel ALOHA is suitable for IoT device communication by allowing grant-free massive access. However, this scheme suffers from a few challenges in its implementation. Detection algorithms, decoding strategies, overloading factors, and receiver capacity are some challenges of NOMA.
# Concrete Gas Permeability: Implications for Hydrogen Storage Applications
## 1 Introduction
### Concrete Permeability
It has been studied that a lower w/c ratio significantly reduces permeability to lower levels by promoting a denser concrete matrix with fewer interconnected voids. Moreover, the incorporation of superplasticizers in the concrete mix enables the achievement of low w/c ratios, thereby producing concrete with enhanced durability and reduced permeability. These improvements are crucial for ensuring the long-term performance and sustainability of concrete structures [23; 24]. Another factor that may influence the permeability of concrete structures is the presence of cold joints. A cold joint refers to a point or interface where two separate batches of concrete meet and bond together, possibly improperly or less effectively [25]. Cold joints occur when there is a time gap between the placement of the first concrete pour and the subsequent pour. These joints can have various causes and consequences, especially concerning reductions in the structure's strength and durability [26; 27]. Hydrogen, aside from being explosive, has a small atom size, which makes it highly permeable and can cause hydrogen embrittlement in metals, making its storage very challenging. One of the standards that studies gas permeability in concrete is UNE 83981 [28], which determines the permeability of hardened concrete to oxygen. Determining O2 permeability is of critical importance due to the fact that it has been demonstrated that in the presence of oxygen and a sufficient amount of chloride ions dissolved in the water in the concrete pores, steel corrosion occurs, even under high alkaline conditions. In such circumstances, structural failures can occur due to fractures or weak points, as mentioned above. As gases of different sizes also penetrate concrete but have not been studied, this study includes studying oxygen, helium, nitrogen, and argon. Helium is particularly important because hydrogen cannot be directly tested due to its explosiveness, and helium is the gas that most closely resembles it in size. Therefore, data can be extrapolated to approximate hydrogen permeability [29; 30; 31]. The objective is to verify if the variation among different gases is significant and, if so, to evaluate and differentiate their behavior. The comparison between specimens with and without cold joints is also evaluated to determine the durability issues that they can cause to the structure regarding gas permeability. Additionally, the data will be used to estimate hydrogen permeability based on gas pressure and concrete thickness, which leads to estimating the containment capacity.
# Sample-Efficient Deep Learning Techniques for Burn Severity Assessment with Limited Data Conditions
## 2 Burn Severity Assessment
### CNN Models  
Deep learning techniques have made significant achievements in image classification for diagnosing skin diseases, e.g., the CNN-based classifier for skin diseases [11], the transfer learning-based skin cancer detector [12], and deep learning-based skin lesion classification [13]. In this research, given the dataset of skin burn images, we employed CNNs for burn severity assessment, as they are known to be superior in image recognition tasks and their pretrained knowledge on large image datasets can be transferred to other tasks, including the analysis of various types of medical images [14; 15]. We used ResNet-152 [16], a 152-layer large network variant of ResNet models which have proven high performance in several medical image analysis studies, e.g., burn classification and segmentation [17; 18; 19], skin cancer classification [20], CT image improvement [21], and chest X-ray diagnosis [22]. We also note that a pretrained model of ResNet-152 is publicly available and easy to use. Furthermore, as ResNet-152 is commonly used without significant modification for several sample-efficient deep learning techniques which are described in the next section, we evaluated the techniques in a consistent manner for the same deep learning structure.  
We set the performance achieved by ResNet-152 as our comparison baseline. For the baseline reference, a ResNet-152 model was trained from scratch on our entire dataset, as shown in Table 1. We also utilized the pretrained ResNet-152 model for which the knowledge was transferred to our burn severity assessment tasks through model fine-tuning with a small number of burn images in the dataset. Unlike end-to-end training from scratch, in transfer learning (TL), the parameters of a pretrained model, except for those in the last layers, are usually retained and not updated. Fine-tuning was used with a small number of samples to update the last layers only to achieve some degree of adaptation, while exploiting the pretrained knowledge. This TL procedure enables rapid domain adaptation between image recognition tasks [23; 24].  
A pretrained model of ResNet-152 can be established using the ImageNet dataset [25] which contains over 14 million images and is applied as a well-initialized network for task-specific learning, burn severity assessment in our case. In TL, we modified the head (the upper fully connected layer) of a vanilla ResNet-152 structure according to the burn classifications, initializing it using the Kaiming uniform initialization [26].
# Bonded-Particle Model with Nonlinear Elastic Tensile Stiffness for Rock-Like Materials
## 2 Method Description
### Dimensionless Parameters in NET-BPM  
In the conventional BPM, the micro parameters are normal stiffness \(k_{n}\), shear stiffness \(k_{s}\), bending stiffness \(k_{m}\), ultimate normal strain \(\epsilon_{0}^{n}\), ultimate pure shear strain \(\epsilon_{0}^{s}\), upper bound of shear strain \(\epsilon_{max}^{s}\), ultimate relative rotation angle \(\theta_{0}\), friction angles \(\varphi\) and \(\varphi_{f}\), translational damping constant \(\alpha^{t}\) and rotational damping constant \(\alpha^{t}\), etc. The macro parameters are UCS, TS, the ratio of UCS/TS, [PERSON]'s modulus \(E\) and [PERSON]'s ratio \(\upsilon\), etc. A few more micro parameters, which are \(w\) and \(b\) are included in the NET-BPM. In addition, because [PERSON]'s modulus and [PERSON]'s ratio in compression and tension are different, they are denoted as \(E_{c}\) and \(E_{t}\), \(\upsilon_{c}\) and \(\upsilon_{t}\), respectively. In this paper, the effects of particle size, particle arrangement, porosity and density of the specimens are not investigated. [PERSON]'s modulus and [PERSON]'s ratio discussed here are the apparent ones. By using dimensionless analysis similar to [28; 34], the dimensionless micro parameters are \(k_{s}/k_{n}\), \(k_{m}/(k_{n}R)\), \(\epsilon_{0}^{s}/\epsilon_{0}^{n}\), \(\epsilon_{max}^{s}/\epsilon_{0}^{n}\), \(\varphi\), \(\varphi_{f}\), \(w\), \(b\), and the dimensionless macro parameters are \(E_{c}R^{2}/k_{n}\), \(\upsilon_{c}\), \(\sigma_{c}R^{2}/k_{n}\epsilon_{0}^{n}\), \(\tau_{t}R^{2}/k_{n}\epsilon_{0}^{n}\), \(E_{t}/E_{c}\), \(\upsilon_{t}/\upsilon_{c}\) in our model. \(R\) is the characteristic length which is equal to \(R_{max}\). As the nonlinear elastic model is applied, the ratio of \(E_{c}\) to \(E_{t}\) and the ratio of \(\upsilon_{c}\) to \(\upsilon_{t}\) are not equal to 1 anymore, so \(E_{t}/E_{c}\) and \(\upsilon_{t}/\upsilon_{c}\) should be taken into consideration.
## 2 Results and Discussion
### Characteristics of the T/O Compounds in Freshwaters of the World
#### 2.4.1 Spatiotemporal Distribution of T/O Compounds in Freshwater Sources Worldwide
For instance, Dianchi Lake recorded 570 ng\(\cdot\)L\({}^{-1}\) in September 2003 [46], Songhua Lake reached 8.9 ng\(\cdot\)L\({}^{-1}\) in September 2017 [22], and the main canal of the Middle Route of the South-to-North Water Diversion Project had concentrations with ranges of 11.973 \(\pm\) 20.643 ng\(\cdot\)L\({}^{-1}\) between September 2018 and August 2019 [71]. Further, the LiangxiRiver had a concentration of 350 ng\(\cdot\)L\({}^{-1}\) in September 2018 [51], and raw water from a Chinese city registered a concentration of 358 ng\(\cdot\)L\({}^{-1}\)[26]. In contrast, limited data are available for ION concentrations in water bodies outside of China. This suggests that further research and monitoring efforts are needed to comprehensively understand the occurrence and impact of ION in global aquatic systems.
# Adapting Satellite Soundings for Operational Forecasting within the Hazardous Weather Testbed
## Materials and Methods
### Datasets
#### 2.1.2 Modified NUCAPS Soundings  
Modified NUCAPS (Figure 2c) was introduced to the HWT in 2017 to test the usefulness of boundary layer adjustments for predicting severe weather; this was developed as a result of feedback from the first time NUCAPS was demonstrated at HWT [22]. The NUCAPS boundary layer representation remains an ongoing challenge because there is high horizontal variation at the surface that NUCAPS represents for an FOR that has a diameter between 50 km at nadir to 150 km at the scan edge [23]. Although horizontal variation inhibits a representative profile, steep vertical thermal gradients improve the vertical structure and isothermal vertical profiles are more difficult to retrieve [21]. So, the ability to well-characterize the boundary layer varies depending on atmospheric conditions.  
To address these challenges, the modified NUCAPS product replaces the surface layer temperature and dew point temperature with values from the Real-Time Mesoscale Analysis (RTMA) [24]. The remainder of the boundary layer is assumed to be well mixed in potential temperature and water vapor mixing ratio. The height of the boundary layer is measured using both RTMA and Geostationary Operational Environmental Satellite (GOES) Advanced Baseline Imager (ABI) observations. Since the boundary layer is adjusted with in situ and satellite observations, the modified NUCAPS profiles are model independent.
## 4 Model Construction
### Pipe Diameter Optimization  
Prior to initializing the algorithm, some considerations must be taken into account. In terms of pipe diameter, the optimization of the water network implies finding the smallest diameter that is capable of providing the expected service. This means that at every junction, a minimum head pressure has to be reached, but a maximum value should also be considered according to the local standard regulations. In the case of the study area, the minimum and maximum values for the head pressures are 15.3 m and 71 m, respectively. However, in this study, a lower range of values (between 15 m and 40 m) was considered to achieve an optimal design in which the pressure in the system is reduced to avoid future leakage problems while also reaching a minimum suitable service value.  
In addition to the considerations for the head pressure, a range of minimum and maximum water velocities in the pipes was also regulated for the WDN. The reason for this regulation is to guarantee a stable level of water quality regardless of the demand pattern  
Figure 3: EPANET original model of the Cheongna WDN indicating the current head pressures.  
Figure 2: EPANET original model of the Cheongna WDN indicating the current pipe diameters.  
to avoid different problems related to chlorine decay. In this case, the velocity range was established to be between 0.05 and 3 m/s.  
Both head pressure and velocity limitations were used as the constraints--i.e., the penalties--in the objective function used in the HS algorithm.
## Materials and Methods
### Measurement Equipment  
A summarized overview of the measurement equipment is given in Table 1 and Figure 3. The mass flow rate of the fuel was measured using a Micro Motion ELITE CMFS010M flowmeter, which operates on the Coriolis principle. This type of flowmeter allows the flow rate of various gases to be measured, without the need for calibration each time, unlike thermal flow meters. The airflow rate through the intake manifold was measured with a Sensycon Sensyflow thermal flowmeter. The throttle angle and ignition were controlled by an external EMU Black controller from EcuMaster (Modlnica, Poland). An LSU 4.9 broadband oxygen sensor and LCP80 controller were used to measure the excess air ratio at the engine exhaust. Fast-variable engine parameters, such as the main and prechamber pressure, crankshaft angle, prechamber, and main-chamber injection times, were registered using the 8-channel AVL IndiSmart system. In addition, the NOx content of the exhaust gas was recorded using a Semtech DS analiser (Sensors, USA). The main objective of the present study was to investigate the combustion process of helium-hydrogen fuel, so the analysis of the collected data was carried out over an angular range of \(-10\) to 30 degrees of the crankshaft.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Name** & **Value** \\ \hline Engine & AVL 5804 & 1-cyl; 4 valves; TJI \\ Injectors & PFI (EM) & – \\ Brake & AMKASYN AVL AMK & – \\  & DW13-170 & – \\ Parameter recorder & AVL IndiSmart & 8-channel, 0.5 CA \\ Pressure sensor (MC) & AVL GH14D & 0–25 MPa \\ Pressure sensor (PC) & Kistler M3.5 6081 AQ22 & 0–25 MPa \\ Airflow meter & ABB SensyFlow & 0–720 kg/h, error \(<\pm\)0.8\% \\ Injection controller & Mechatronika & 0–20 ms, \(\pm\)0.1 ms \\ Fuel flowmeter (MC) & Micro Motion ELITE & \\  & CMFS010M Coriolis Meter & 0.1–2 kg/h, accuracy \(\pm\)0.25\% \\ Fuel flowmeter (PC) & Bronkhorst 111B & 0.1–100 g/h; \\  & accuracy 0.1\% FS \\ Oxygen sensor & Bosch LSU 4.9 & 0.7 do 12.5 \\  & Throttle & Bosch ETB 32 mm & \(\pm 1^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test bench equipment.  
Figure 2: Passive (**a**) and active (**b**) prechamber (dashed square) system (based on [15]; blue arrows —intake port, red arrows—exhaust port, DI—direct injection, PFI—port fuel injection).
# Bluetooth-Based Healthcare Information and Medical Resource Management System
## 2 Related Work and Literature Review  
### Remote Health Monitoring Systems  
Remote Health Monitoring Systems (RHMS) [24] based on Wireless Body Area Networks reduce the number of unessential hospitalizations by providing long-term management of health conditions, disease prevention, and detection of emergencies for patients at home. Furthermore, continuous health monitoring using RHMS is a hopeful solution for elderly people suffering from chronic diseases. RHMS is based on the deployment of a Wireless Body Area Network (WBAN) [25], called WBAN-based RHMS (WBAN-RHMS), using wearable and/or implantable sensors in or around the human body. The sensed physiological data are forwarded through a wireless Bluetooth network to a collecting node known as a data collector or a gateway or coordinator (smartphone or PDA: Personal Data Assistant), which is connected to the remote server (for data processing and storage) through the Internet network (via WiFi) or the cellular networks (4G or 5G) as Figure 1.