esa-sceva/satcom-chunk-collection huggingface dataset:
# Green Synthesis, Characterization, and Antifungal Efficiency of Biogenic Iron Oxide Nanoparticles
## 3 Results and Discussion
### FTIR Analysis of the Biogenic \(Fe_{2}O_{3}\) Nanoparticles  
Fourier-transform infrared (FT-IR) spectroscopy was used to analyze the surface chemical composition and functional groups of the biogenic IONPs, as well as to investigate any potential surface interactions [48]. The FTIR spectrum of the aqueous leaf extract of \(L\). _nobilis_ demonstrated the presence of six absorption bands at 3427.08, 2927.22, 2847.61, 1632.01, 1030.37, and 540.36 cm\({}^{-1}\), whereas the FTIR spectrum of the biogenic IONPs demonstrated the presence of eight absorption bands at 3434.74, 2921.47, 1621.34, 1444.05, 1352.94, 1176.47, 1075.45, and 574.83 cm\({}^{-1}\) (Figure 5). In this context, the O-H stretching was observed in the spectrum of the aqueous extract of \(L\). _nobilis_ at a wavenumber of 3427.08 cm\({}^{-1}\), providing evidence for the existence of alcoholic and phenolic functional groups. The prominent peak seen at 3434.74 cm\({}^{-1}\) in the FTIR spectrum of the biogenic IONPs might be ascribed to the stretching vibration of the O-H bond in phenolic compounds that are adsorbed onto the nanoparticle surface during the synthesis process [49]. The spectral analysis of the \(L\). _nobilis_ extract revealed the presence of two distinct bands at 2927.22 and 2847.61 cm\({}^{-1}\). These bands may be attributed to the stretching of C-H bonds in alkanes. The functional groups of alkanes present in the extract may have been capped on the surface of the biogenic IONPs, as shown by the detection of a band at a wavenumber of 2921.47 cm\({}^{-1}\)[50; 51]. The absorption bands seen at wavenumbers of 1632.01 and 1621.34 cm\({}^{-1}\) in the spectra of \(L\). _nobilis_ and the biogenic IONPs might be ascribed to the N-H bending of amine functional groups that are capped over the surface of the biogenic IONPs, as suggested by previous research [52]. Moreover, the absorption band located at 1444.05 cm\({}^{-1}\), might be attributed to the C-C stretching of aromatic C=C [53]. Moreover, the band noticed at 1352.94 cm\({}^{-1}\) could be ascribed to the O-H bending of phenolics [54]. In addition, the band observed at 1176.47 cm\({}^{-1}\) might be assigned to the C-N stretching of amines. However, the absorption band detected at 1030.37 cm\({}^{-1}\) of the extract might be ascribed to the COO- carbonyl group [55]. Similarly, the band seen at a wavenumber of 1075.45 cm\({}^{-1}\) in the biogenic IONPs may be attributed to the stretching of C-O bonds present in alcoholic functional groups (Table 1) [56]. Finally, the characteristic transmission bands detected at 540.36 and 574.83 cm\({}^{-1}\) were assigned to the alkyl halides as previously reported [57]. Collectively, FTIR analysis affirmed that the phenols, alcohols, amines, and  
Figure 4: SEM image of the biogenic IONPs.  
alkanes of the plant extract act as reducing, stabilizing, and capping agents for the biogenic IONPs.
## 3 Materials and Methods
### Turbulence Model  
The standard \(k\)-\(\varepsilon\) transport equations for the turbulence kinetic energy, \(k\), and its rate of dissipation, \(\varepsilon\), are:  
\[\frac{\partial}{\partial x_{i}}(\rho ku_{i})=\frac{\partial}{\partial x_{j}} \left[\left(\mu+\frac{u_{t}}{\sigma_{k}}\right)\frac{\partial k}{\partial x_{ j}}\right]+G_{k}-\rho\varepsilon, \tag{5}\]  
and  
\[\frac{\partial}{\partial x_{i}}(\rho\varepsilon u_{i})=\frac{\partial}{ \partial x_{j}}\left[\left(\mu+\frac{u_{t}}{\sigma_{\varepsilon}}\right) \frac{\partial\varepsilon}{\partial x_{j}}\right]+C_{1\varepsilon}\frac{ \varepsilon}{k}G_{k}-C_{2\varepsilon}\rho\frac{\varepsilon^{2}}{k}, \tag{6}\]  
where \(G_{k}\) is the generation of turbulence kinetic energy due to the mean velocity gradients. \(\sigma_{k}\) and \(\sigma_{\varepsilon}\) are the turbulent Prandtl numbers for \(k\) and \(\varepsilon\). The turbulent (or eddy) viscosity, \(\mu_{t}\), is evaluated as:  
\[\mu_{t}=\rho C_{\mu}\frac{k^{2}}{\varepsilon}, \tag{7}\]  
where the empirical model constants are \(C_{1\varepsilon}=1.44\), \(C_{2\varepsilon}=1.92\), \(C_{\mu}=0.09\), \(\sigma_{k}=1.0\), \(\sigma_{\varepsilon}=1.3\)[30].
# Future Industrial Applications: Exploring LPWAN-Driven IoT Protocols
## 5 Industrial Application Perspective
### Asset Tracking and Monitoring  
IoT-based asset tracking and monitoring applications are becoming more common in the industry. Effective and scalable tracking and monitoring are essential for rental assets and industrial bases. Numerous tracking options are available, with uses spanning from monitoring automobiles and bicycles to monitoring company assets, including parcels, service goods, and containers. Technologies like LPWAN tracking are becoming quicker and more accurate [122; 123] and, thus, can help the scalability of monitoring and tracking. LoRaWAN is being utilized as a geolocation tracker, where the differential time of arrival method determines the asset's precise location at the closest gateway [124; 125]. LoRaWAN and Sigfox are suitable options as they can handle an extensive coverage area and a significant user base at the expense of higher collision, latency, and jitter rates [122; 124; 126]. Significant research wasperformed to check the suitability of using LoRaWAN to conduct temperature studies on local government assets in the City of Melville, an Australian metropolitan area, and Curtin University's residential areas. The results found that LoRaWAN is appropriate for this kind of application because of its long range and suitable data rate [127].
## 2 Materials and Methods  
### Chemicals  
The diverse weight ratio of carbonates (and hydroxides), allowing the continuous absorption and electrochemical transformation of CO\({}_{2}\) (and H\({}_{2}\)O) into valuable carbon-based fuels, is contained in a long-life corundum crucible. The electrolyte of lithium carbonate (Li\({}_{2}\)CO\({}_{3}\), 99.99%, battery grade), sodium carbonate (Na\({}_{2}\)CO\({}_{3}\), 99.5%), potassium carbonate (K\({}_{2}\)CO\({}_{3}\), 99%), calcium carbonate (CaCO\({}_{3}\), 99%), barium carbonate (BaCO\({}_{3}\), 99%), and lithium hydroxide (LiOH, 99.9%) were provided by Beijing InnoChem and Macklin. The electrode system of spiral Fe wire cathode (active area, 10 cm\({}^{2}\)) and spiral Ni wire anode (active area, 10 cm\({}^{2}\)) was purchased from Shanghai Makclin Biochemical Co., Ltd. and was polished to avoid the influence of the oxide layer on the metal surface.  
**Method**. The corundum crucible, containing eutectic molten salts, was placed inside a ceramic heater powered by electricity. To ensure that the moisture of the electrolyte was removed completely, the mixed molten salts were kept at operating temperature (50 \({}^{\circ}\)C above the melting point, at least) for 3 h. The electrolysis of the CO\({}_{2}\) (and H\({}_{2}\)O) from CO\({}_{3}^{2-}\) (and H\({}^{+}\)) in the eutectic molten salts occurred at the operating current density for the desired duration. The cathodic solid products in the system of pure carbonates were collected through a series of purification treatments: acid leaching \(\rightarrow\) ultrasound process \(\rightarrow\) filtration \(\rightarrow\)water bath (deionized water) \(\rightarrow\) drying. The gaseous products released from the sealed reactor through a gas-guide tube were collected in the airbag for composition analysis. Even more importantly, a well-sealed stainless-steel reactor was used to ensure the accuracy of the product composition.  
**Characterization**. The micro-structure of the solid carbon on the cathode was observed by a Zeiss SIGMA SEM (scanning electron microscope) with a 15 KV working voltage. With the help of the the EDS (energy dispersive spectroscope) component of the SEM, the element composition of the cathodic solid was detected. Thus, the gaseous products, collected in the air bag, were analysed by gas chromatography (Agilent 7890B). The H\({}_{2}\), O\({}_{2}\), and CH\({}_{4}\) were detected by a thermal conductivity detector (TCD).
# Fracture Assessment of Weld Joints of High-Strength Steel in Pre-Strained Condition
###### Abstract  
Unstable fractures tend to occur after ductile crack initiation or propagation. In most collapsed steel structures, a maximum 15% pre-strain was recorded, at the steel structural connections, during the great earthquake of 1995, in Japan. Almost-unstable fractures were observed in the beam-to-column connections, where geometrical discontinuities existed. Structural collapse and unstable failure occurred after large-scale plastic deformations. Ship structures can also suffer from unstable fractures in the welded joints. The fracture resistance of butt-welded joints subjected to tension in the pre-strained condition was estimated by considering the toughness deterioration, due to pre-strain and toughness correction for constraint loss in a tension specimen. The target specimen for this fracture assessment was a double-edged, through-thickness crack panel, with a crack in the weld joint (heat-affected zone (HAZ)). The critical fracture toughness value (crack tip opening displacement (CTOD)) of a large structure with pre-strain, which was applied to the HAZ region, was estimated from a small-scale, pre-stained, three-point bend specimen. Fracture toughness values, evaluated by a CTOD test, were recently mandated for shipbuilding steel plates. The critical fracture toughness value is a very useful parameter to evaluate the safety of huge ship structures.  
pre-strain; fracture toughness; brittle fracture; fracture assessment crack tip opening displacement (CTOD); great earthquake; dynamic load; huge ship structure Article
## 4 Experimental Evaluation of Performances
### Community Anomaly Detection in Real Datasets  
In this section, we test our model on two real networks: the Disney network and the Book network and compare our model's performance with those of the CODA and ConSub models. Our proposed model has only two parameters:  
* The \(\alpha\) in the Equation (10) that we set at 0.5 to give the same weight to the structure-based anomaly and the attribute-based anomaly.
* The number of attributes to include. We set the second parameter to be the half-number of attributes and a maximum of 10 attributes, as we observed that a higher number of attributes increase the runtime without significant improvement in quality.  
For CODA and ConSub parameter settings, we tested many values. The ones that give the best results are described in Table 1.
# Recent Advancement in Assessment and Control of Structures under Multi-Hazard
## 4 Multi-Hazard Assessment and Control of Bridges  
Bridges are lifelines of modern society. They are vulnerable to different hazards, as evidenced by several failures in the past. On 19 August 2016, a suspension railway bridge in Tolten-Chile collapsed due to train-induced vibrations [56]. On 29 August, during hurricane Katrina, the Twin Spans Bridge connecting New Orleans to Slidell, Louisiana, United States, suffered extensive damage [57]. On 21 July 2003, Kinzua Bridge in Pennsylvania, United States, was hit by a tornado with 100 mph (45 m/s) winds and collapsed [58]. On 14 January 2003, [PERSON] [PERSON] VC Memorial Bridge in Ontario, Canada collapsed [59] due to fatigue-induced failure of the steel hanger rods supporting the deck. On 17 January 1995, a bridge on Hanshin Expressway in Kobe, Japan collapsed during the Kobe Earthquake [60]. During the Loma Prieta earthquake in 1989, two famous bridges (Cypress Street Viaduct and San Francisco--Oakland Bay Bridge) in California, USA were heavily damaged [61; 62]. The collapse of these two bridges killed forty-one persons.  
Safety and reliability of bridges are controlled by a diverse set of factors related to the structural form, function, maintenance, and the hazards they are exposed to. Multi-hazard consideration is, therefore, emerging as an important topic in bridge design and safety assessment. Some of the recent advancements in this field are discussed in the following.
# Virtual Grid-Based Routing for Query-Driven Wireless Sensor Networks
## 3 VGRQ: The Proposed Routing Mechanism
### Phases of VGRQ
#### 3.2.1 Grid Construction  
In the VGRQ approach, the sensor field is divided into square-shaped virtual grid cells with dimensions of \(M\times M\). The determination of the optimal number of cells andcell headers is achieved through the utilization of TEEN [25] and APTEEN [26] algorithms. Specifically, the cell headers are selected as 5% of the total number of sensor nodes. If there are \(n\) sensor nodes in the network, VGRQ constructs a grid with \(M\times M\) dimensions, where the value of \(M\) (representing the number of rows and columns) is obtained using Equation (1).  
\[M=\begin{cases}4,&n\times 0.05\leq 5,\ \ \text{if}\ n=100\text{ {to}}\ 200\\ 16,&10\leq n\times 0.05\leq 20,\ \ \text{if}\ n=201\text{ {to}}\ 500\\ 36,&25\leq n\times 0.05\leq 40,\ \ \text{if}\ n=501\text{ {to}}\ 800\\.&.\\.&.\\ \end{cases} \tag{1}\]  
The construction of the grid for varying numbers of deployed sensor nodes is depicted in Figure 1a-c. Sensor nodes identify their cell ID using their GPS information. Once established, the virtual grid structure remains unchanged and static.  
Let the length of the side of the sensing field be \(L\) and the sides of each grid cell be \(L/M\). Once construction of the virtual grid structure is accomplished, each sensor node identifies the grid cell \((r,c)\), i.e., the \(r\)th row and \(c\)th column, where they belong based on their location \((x,y)\), as depicted in Figure 2.  
\[\begin{split} r&=\left[\frac{x}{L/M}\right]=\left[ \frac{x\times M}{L}\right]\\ c&=\left[\frac{y}{L/M}\right]=\left[\frac{y\times M}{L} \right]\end{split} \tag{2}\]  
Figure 1: Grid construction when number of sensor nodes is (**a**) 100 to 200, (**b**) 201 to 500, (**c**) 501 to 800.
## 4 HFM-Based AUV Localization  
The ultimate solution to decouple localization performance from the MAC delay is to allow all ANs to transmit their packets at the same time. This is the concept of multiple access technique. The most well-known multiple access techniques are time, code and frequency division multiple access, i.e., time division multiple access (TDMA), code division multiple access (CDMA), and frequency division multiple access (FDMA). Unlike other technologies, TDMA is not allowing for several nodes to communicate simultaneously. Meanwhile, CDMA allows all the ANs to share the channel at the same time, but implementation of CDMA in underwater is challenge itself because CDMA need to set the optimal transmit power and code length [19]. Prediction of signal attenuation, which is determined  
Figure 4: The CRLB with respcet to the AUV’s velocity, network size: \(1500\times 1500\times 1500\) m\({}^{3}\), initial position of the AUV: (750, 750, 750).  
Figure 3: The CRLB with respcet to the AUV’s velocity, network size: \(300\times 300\times 300\) m\({}^{3}\), initial position of the AUV: (150, 150, 150).  
by the signal frequency and communication distance, is very difficult, especially in the mobile scenario where the distances are continuously changed. Although CDMA-based MAC protocols have been proposed, but the system performance is affected by the channel state [20]; consequently it is difficult to apply this scheme into underwater localization requiring high reliability. Therefore, we employ FDMA technique, which supports the simultaneous transmission [21]. Each AN uses different frequency to send a packet, and then the multiple signals can be identified in the frequency domain. One notable thing is that the used modulation has to be tolerant to the Doppler effects because the transmitted signal is strongly affected by the Doppler effect, especially in the case of communication between moving platform. For the purpose, we suggest the use of HFM signal that is the most tolerant to the Doppler effects [22; 23]. After describing the multiple access system combined with HFM signal, the localization process is presented.
## Appendix A Computational Complexity
### Conventional Scheme B  
Bear in mind that the conventional scheme B is comprised of four operations: the \(g\)-lag correlation \(\tilde{Y}_{g}(k)\) in (12), the estimation of total frequency offsets \(\hat{\varphi}(k)\) in (13), the CFO estimation in (14) and the SFO estimation in (15). In (12), it can be seen that using \(\tilde{Y}_{g}(k)\) requires \(N_{f}(3N_{b}^{2}/8-N_{b}/4)\) complex multiplications and \(N_{f}(3N_{b}^{2}/8-3N_{b}/4)\) complex additions. For (13), \(\tilde{Y}_{g-1}(k)\) is also calculated, which requires \(N_{f}(3N_{b}^{2}/8-3N_{b}/4)\) complex multiplications and \(N_{f}(3N_{b}^{2}/8-5N_{b}/4+1)\) complex additions. To estimate \(\hat{\varphi}(k)\) in (13), it follows that computing \(\sum_{g=1}^{N_{b}/2}w(g)\triangle\tilde{Y}_{g}(k)\tilde{Y}_{g-1}^{*}(k)\) requires \(N_{f}(N_{b}/2)\) complex multiplications, \(N_{f}(N_{b}/2)\) real multiplications and \(N_{f}(N_{b}/2-1)\) real additions. For \(W(n)\)'s and \(Q(n)\)'s, \(W(n)\)'s (\(n=1,2,3\)) require \(4N_{f}\) real multiplications and \(4N_{f}-3\) real additions, whereas the calculation of \(Q(n)\)'s (\(n=1,2\)) needs \(2N_{f}\) real multiplications and \(2N_{f}-2\) real additions. The calculation of (14) and (15) demands 10 real multiplications and four real additions. As a consequence, the conventional Scheme B requires the number of real flops given by \(N_{f}(6N_{b}^{2}-3N_{b}+13)+9\).
## 4 Data Collection and Engineering
### Dataset of IoT System  
IoT devices can operate in many domains, such as smart cities, healthcare systems, education systems, smart homes, smart grids, and transportation systems [34]. Our research concentrates on a smart home IoT system; therefore, the IoTD20 dataset [35] was used to test and evaluate the performance of our proposed model. The environment used to collect the IoTD20 dataset consists of IoT devices connected through an access point network [35]. The IoT devices comprise a laptop, smartphone, EZVIZ camera, and SKT NUGU speaker. The laptop and the smartphone were used to establish intrusion attacks, and Wireshark monitors the IoT traffic. The security camera and the AI speaker are the victims, as shown in Figure 8. A detail about the experiment can be found in this reference [35].  
Figure 8: IoT device architecture.  
Figure 7: KNN Classifier. (**a**) shows the initial stage of the KNN algorithm. (**b**) shows the calculation stage for computing the distance between neighbors. (**c**) shows the classification stage based on the K value.  
The dataset was collected from a real-time scenario using IoT devices, as shown in Figure 8. The original IoTID20 dataset includes 86 columns and 625,783 rows. Each row in the dataset is labeled with the type of network activity. We preprocessed the dataset to increase the classification accuracy of the label, category, and sub-category features. However, we focused our study on label and category features and will express the reason in the Features Engineering section (Section 4.2). Label features include binary classification, which is normal, and anomaly. Category features have five classifications: normal, Mirai attack, DoS attack, Scan attack, and MITM attack [35].
# Indoor Localization for Augmented Reality Devices Using BIM, Point Clouds, and Template Matching
## 4 Evaluation  
To test the accuracy and reliability of the algorithm, we have recorded point-cloud data and pose data using Microsoft HoloLens. Our reference model is a BIM model of a university building, where we match on a 3000 m\({}^{2}\) floor space. The model has multiple self-similar rooms and hallways to test reliability. Figure 4 shows the floor plan of the BIM model used for localization.
# An Adaptive Routing Algorithm Based on Relation Tree in DTN
## 4 Relation Tree  
Introducing the concept of social networks into routing algorithms is the latest trend in the field of DTN research. In many application scenarios of DTNs, the nodes are mostly mobile devices with communication functions used by people, and thus the nodes in DTNs also exhibit social characteristics similar to those of humans. Despite the relatively unpredictable network topology, human movement patterns are not completely random; for example, closely related nodes have a higher probability of meeting, and these nodes that meet frequently form a social circle. Compared with nodes outside the social circle, nodes within the same social circle are more closely connected, meet more frequently, and have a higher probability of successfully delivering messages to each other. If we can accurately construct the social circles of every node, we can forward messages in a more targeted manner and improve the forwarding efficiency. Based on the above analysis, we propose a method for constructing social circles, which is called a Relation Tree because it is reflected in the storage structure as a tree storage structure.  
Definition (Relation Tree (RT)). Each node in the network maintains a Relation Tree with itself as the root node. the maximum depth of the Relation Tree is 3, the memory cycle of each node is T, and the judgment threshold of the number of encounters is M. A node with depth 2 in the Relation Tree satisfies the condition that the number of encounters with the root node exceeds M in a memory cycle, and a node with depth 3 in the Relation Tree  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Explanation** \\ \hline \(T\) & Memory cycle \\ \(M\) & Relationship threshold \\ \(V\) & Set of nodes in the network \\  & Number of times \(v_{i}\) and \(v_{j}\) meet in a memory \\ \(N\Big{(}v_{i},v_{j}\Big{)}\) & cycle \(T\) \\ \(RT_{i}\) & Relation Tree of node \(v_{i}\) \\ \(L_{i}(m_{a})\) & Number of copies of \(m_{a}\) carried by node \(v_{i}\) \\ \(\beta_{now}\) & Current cache occupancy of node \\ \(\beta_{best}\) & Optimal cache occupancy of node \\ L & The actual number of copies \\ \(L_{init}\) & The initial number of copies \\ \(TTL_{new}\) & The lifetime of the newly generated message \\ \(TTL_{old}\) & The original lifetime of the message copy \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of notations.  
Figure 1: The flowchart of the AR-RT algorithm.  
satisfies the condition that the number of encounters with its parent node exceeds M in a memory cycle.
## 3 Methodological Approach
### Evaluation Metrics
#### 3.2.2 IoU Score  
The intersection over union score, also called the Jaccard Index, is especially beneficial in the cases where one needs to numerically describe the overlapping level of the bounding boxes or segments in the case of pixel-wise classification or object detection in general. The IoU score ranges from 0 to 1, where 0 indicates no overlap and 1 indicates complete overlap in the instances. The higher the IoU score, the better the DNN model classifies the road types. The IoU score is described as  
\[Intersection\ over\ Union=\frac{Area\ of\ Overlap}{Area\ of\ Union} \tag{6}\]
###### Abstract  
Automatic modulation recognition (AMR) provides excellent performance advantages over conventional algorithms and plays a key role in modern communication. However, a general challenge is that the channel errors greatly deteriorate the classification capability, and the computational complexity is extremely high. To reduce the offset error of the signal and ensure fewer parameters to save training resources, we demonstrated an efficient modulation recognition scheme combined predictive correction with double Gate Recurrent Unit (GRU), thus realizing a lightweight neural framework. Predictive correction reduces channel errors, and double GRUs are better at capturing long-term dependencies. The results show that when the signal-to-noise ratio is around 18 dB, the highest recognition accuracy can be achieved, and the computational complexity is significantly reduced. The proposed scheme exhibits a tradeoff between the accuracy and the computational complexity, providing an attractive method for modulation recognition.  
modulation recognition; deep learning; predictive correction +
Footnote †: journal: Applied sciences