esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Adsorption of Methylene Blue
#### 2.3.3 Adsorption Kinetics  
Based on the previous tests, the adsorption kinetics were carried out for H-180. The hydrochar and methylene blue solutions (100 mg\(\cdot\)L\({}^{-1}\)) were mixed in Erlenmeyer flasks at a solid-to-liquid load of 3 g\(\cdot\)L\({}^{-1}\). The mixture was stirred (100 rpm) at 30 \({}^{\circ}\)C, and sampled at 10, 15, 30, 45, 60, 90, 120, 180, 300, 480, 720, and 1440 min. After vacuum filtration, the concentration of the remaining methylene blue in the solution was determined. The experiments were carried out in triplicate for each point in addition to blank tests (without hydrochar), and the mean, minimum, and maximum values were reported. The suitability of the non-linear pseudo-first-order (PFO--Equation (4)), pseudo-second-order (PSO--Equation (5)), and Elovich (Equation (6)) models were tested [29; 30]. The adjustment was performed in the software Origin(r) 2017 using the function Nonlinear Curve Fit and assessed according to \(R^{2}\) and mean relative deviation (MRD) [27; 28]:  
\[q_{t}=q_{e}\cdot\left(1-e^{(-K_{1}\cdot t)}\right) \tag{4}\]  
\[q_{t}=\frac{K_{2}\cdot q_{e}^{2}\cdot t}{1+(K_{2}\cdot q_{e}\cdot t)} \tag{5}\]  
\[q_{t}=\frac{1}{\beta}\cdot\ln(1+\alpha\cdot\beta\cdot t) \tag{6}\]  
In Equations (4)-(6), \(q_{t}\) and \(q_{e}\) are the adsorption capacity at time \(t\) and equilibrium, respectively (mg\(\cdot\)g\({}^{-1}\)); \(t\) is the reaction time (min); \(K_{1}\) (min\({}^{-1}\)) and \(K_{2}\) (g\(\cdot\)mg\({}^{-1}\cdot\)min\({}^{-1}\)) are the rate constants of PFO and PSO models, respectively; \(\alpha\) is the initial adsorption rate (mg\(\cdot\)g\({}^{-1}\cdot\)min\({}^{-1}\)); and \(\beta\) is the desorption constant (g\(\cdot\)mg\({}^{-1}\)).
# Detecting Out-of-Distribution Samples in Complex IoT Traffic Based on Distance Loss
###### Abstract
Next, we optimize a feature distance loss function to ensure that samples within each group converge toward the group center, increasing the minimum feature distance between groups and enhancing the separability of in-distribution samples. Clustering is performed in the penultimate feature layer, allowing the model to focus on local information within each group, improving the discriminability of feature representations while maintaining classification accuracy. For test samples, the Mahalanobis distance is used to calculate the distance to each group, serving as the OOD score. During real-world deployment, we introduce a KDE-based adaptive threshold selection strategy, which smooths the probability density distribution of sample scores to identify potential inflection points in the distribution, which are used as a reasonable threshold for OOD detection. Experimental results show that our method significantly outperforms existing methods on multiple public datasets and real-world IoT traffic, especially in scenarios with class imbalance, where it demonstrates greater robustness. Our contributions are as follows:  
* A Novel Group-Based OOD Detection Framework: We propose an OOD detection framework tailored for complex IoT environments, which integrates class mean feature clustering and group-level partitioning at the penultimate layer to enhance the semantic organization of the feature space. * Innovative Distance Loss and Adaptive Thresholding: We introduce a group-level distance loss to enhance intra-group compactness and inter-group separability, and employ KDE to adaptively select detection thresholds, significantly improving OOD discrimination under varying traffic distributions and device types. * Improved Performance in Imbalanced IoT Traffic: Experiments on public and real-world IoT datasets show that our method outperforms state-of-the-art baselines, achieving at least a 10% increase in AUROC and a 33% reduction in FPR95, particularly excelling in class-imbalanced conditions.
## 3 Results
### P-R Curves  
A Precision-Recall (P-R) curve comprehensively evaluates an object detector and provides insights into precision-recall tradeoffs for different use cases. It is commonly used along with metrics like mean average precision to compare object detectors [29].  
Here are some key properties of a P-R curve:  
* The higher the curve, the better the detector's accuracy at different recall levels.
* The closer the curve is to the top right corner, the better the detector's overall precision and recall.
* The area under the curve (AUC) summarizes the detector's expected precision over all recall levels. A higher AUC indicates a better detection performance.  
Figure 8a,b show the P-R curves for the original YOLOv8 and our modified YOLOv8, respectively. Our modified YOLOv8 P-R curve is higher and closer to the top right corner than that of the initial YOLOv8. Moreover, our model has a higher AUC than the initial YOLOv8, indicating better performance of our model.  
The AUC is the mean Average Precision (mAP) in a P-R curve. The mAP of the original YOLOv8 is 0.912, while ours is 0.971, an improvement of 0.059.
## 2 Materials and Methods
### Spectral Signal Processing  
#### 2.3.1 Data Pre-Processing  
Suppression of the base noise plays an essential role in the spectral processing of CWML to accurately estimate the radial wind speed. If the base noise is ideally white and has a known average level, it is easy to reduce. However, the base noise is colored due to the frequency gain characteristics of the electrical receiver circuit. In addition, the average noise level varies according to the scattered noise level. Empirically, the scattered noise level constantly fluctuates, as the base noise can quickly fluctuate depending on the ambient temperature. In our processing, the CWML recorded the noise spectrum without any received signal when the laser was stopped and subtracted the noise spectrum from the received signal spectrum. For each range gate signal, the base noise signal was subtracted (counterpoint subtraction) and the mean value of the base noise was added, and the resulting spectrum is a flat-base spectrum, as shown in Figure 8. This process whitens the base noise and helps the correct signal peak to be detected. Subsequently, the spectra were smoothed according to the method proposed by [PERSON] et al. for estimating radial wind from smoothed accumulated spectra [39]. Examples of intermediate results are shown in Figure 9.  
Figure 8: Schematic of base noise reduction.  
Figure 7: Flowchart of speed compensation.
## 4 Discussion  
### S. triloba Acetone Extract Significantly Reduces U2 OS and SKOV3 Cell Viability  
In this study, we investigated the effects of crude _S. triloba_ ethanol and acetone extracts on viability, migratory ability, and gene expression of various cancer cell lines. The effect of crude _S. triloba_ extracts on the viability of U2 OS, SKOV3, and HepG2 cells was variable. Although U2 OS and SKOV3 cell viability declined significantly after treatment with _S. triloba_ acetone extract, HepG2 viability was hardly affected by the _S. triloba_ ethanol extract. As a result, HepG2 cells were excluded from further analyses. Although no prior studies have examined the cytotoxicity of _S. triloba_ acetone extracts, bioactivities of both ethanolic, and methanolic extracts were previously reported [23; 25; 56; 57]. _S. triloba_ ethanolic extracts inhibited the proliferation of MCF7 and TD47 breast cancer cell lines with IC\({}_{50}\) values of less than 30 \(\upmu\)g/mL [57]. When tested on PC-3 and DU-145 prostate cancer cell lines, IC\({}_{50}\) values of 287 \(\upmu\)g/mL, and 456 \(\upmu\)g/mL were respectively reported [23]. Our recorded IC\({}_{50}\) values for _S. triloba_ acetone extract against U2 OS (30.210 \(\upmu\)g/mL) and SKOV3 (75.960 \(\upmu\)g/mL)  
Figure 11: FTIR spectra of _S. triloba_ leaves (**Top panel**) and its crude acetone extract (**Bottom panel**).  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Distance Traveled by** **Fraction (cm)** & **Distance Traveled by Solvent Front (cm)** & **Retention Factor** \\ \hline
2.54 & 11.43 & 0.2222 \\ \hline
4.445 & 11.43 & 0.3889 \\ \hline
6.5024 & 11.43 & 0.5689 \\ \hline
9.2202 & 11.43 & 0.8067 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Retention factors of _S. triloba_ acetone extract fractionations.  
cells were within the range of previously reported values (17.43 \(\upmu\)g/mL to 456 \(\upmu\)g/mL) for various cancer cell lines [23; 25; 56; 57]. The observed variability in _S. triloba_ IC\({}_{50}\) can be attributed to differences in the type of cell line used per study. _S. triloba_ further exhibited negligible cytotoxicity against the non-tumorigenic mammary cell line, MCF 10A, and such selective cytotoxicity against cancer cell lines supports its potential use in cancer therapy [23]. Treatment of SKOV3 cells with _S. triloba_ yielded the greatest Hill Slope (6.678 \(\pm\) 3.928) while U2 OS cells displayed the least Hill Slope (0.8665 \(\pm\) 0.137). The Hill Slope is equivalent to the Hill coefficient, which gives an idea of binding affinity between target macromolecules in U2 OS and SKOV3 cells, and the ligands present in _S. triloba_ extract [58]. A Hill coefficient equal to 1 indicates non-cooperative binding while a Hill coefficient either above or below 1, respectively, suggests positive and negative cooperative binding [59]. Our Hill coefficient results thus suggest that SKOV3 cells displayed a greater degree of cooperativity than U2 OS cells when treated with _S. triloba_. This distinction could be attributed to differences in target macromolecules between U2 OS and SKOV3 cells.
## 4 Methodology
### Crop Yield Parameters
#### 4.1.2 Indicators of Environmental Conditions of Plant Growth  
To obtain the factors of environmental conditions of plant growth, the daily weather data collected by 11 synoptic stations of the Iran Meteorological Organization in the study area over an 8-year period were gathered. This included eight types of data, namely minimum temperature, maximum temperature, average temperature, sunny hours, maximum humidity, minimum humidity, average humidity, and dry temperature in growing, germination, and flowering seasons. Environmental factors also included slope and altitude, which were determined using the DEM of Kermanshah province. After giving a score to each 100 m\({}^{2}\) parcel of land, this map was transformed into the raster format. Since multiple layers of data had to be combined, the data of the layers were standardized before overlay. As the slope map illustrated in Figure 2(a) shows, a large portion of the province has a slope of 0-10 degrees. The northwestern and northeastern parts of the province generally have slopes of more than 10 degrees. In slopes of over 10 degrees, the shallowness of the soil and poor plowing conditions make the land more susceptible to environmental degradation. Between the mountains of the province, there are several plains with very gentle slopes from northwest to southeast. Figure 2(b) shows the altitude map of the study area. As can be seen, in Kermanshah province, altitude decreases from east to west. The conditions are more suitable in the central plains of the province, where the average altitude ranges from 800 to 1500 m. Some east and northeastern parts of the province, such as Sonqor County, have an average altitude of over 1500 m. In contrast, the western parts of the  
Figure 2: Satellite images of the indices obtained from the remote sensing data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data** & **Spatial Resolution** & **Temporal Resolution** & **Source** & **Product** \\ \hline Normalized difference vegetation index & 250 m & 16-Day & NASA & MOD13Q1.006 \\ Enhanced vegetation index & 250 m & 16-Day & NASA & MOD13Q1.006 \\ Leaf area index & 500 m & 4-Day & NASA & MCD15A3H.006 \\ Fraction of photosynthetically Active radiation & 500 m & 4-Day & NASA & MCD15A3H.006 \\ Gross primary production & 500 m & 8-Day & NASA & MOD17A2H.006 \\ Evapotranspiration & 500 m & 8-Day & NASA & MOD16A2.006 \\ Rainfed croplands & 1000 m & 2010 & NASA & USGS/GFSAD1000_V1 \\ Surface soil moisture & 0.25\({}^{\circ}\) & 31-days & NASA & NASA,USDA/HIS/SMAP \\ Subsurface soil moisture & 0.25\({}^{\circ}\) & 31-days & NASA & NASA,USDA/HIS/SMAP \\ Precipitation & 0.05\({}^{\circ}\) & Daily & CHIRPS & chirps-v2.0.1981-2019.39 yrs.tif.gz \\ \hline \hline \end{tabular}
\end{table}
Table 3: Specifications of remote sensing data used in the study.  
province--such as Qasr-e Shirin, Sarpol-e Zahab, and Gilan-e Gharb counties have an average altitude of less than 800 m.
# Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
## 2 Highly Adaptive Reconfigurable Receiver Front-End (HARRF) for 5G and Satellite Applications
### Design Process Flow  
The overall design process of the HARRF involves multiple stages, including requirement specification, circuit design, optimisation, and validation. Figure 1 illustrates the design process flow.
# Simplified Welch Algorithm for Spectrum Monitoring
## 2 Fourier Transform and Welch Algorithm  
### DFT & FFT  
Discrete time signals result from sampling of continuous-time analog signals that are converted into digital values for future analysis. The interval between consecutive measurements is specified by the sampling period. Thus, discrete time signals are not defined between sampling intervals, which means that the highest possible observable frequency cannot exceed the Nyquist limit [16] that is equal to half of the sampling frequency. Even with this known trade-off, the DFT is a powerful tool for analyzing these types of signals since it can reveal the periodicity in the input signal as well as the relative power for each frequency component. The DFT is defined mathematically as:  
\[X[k]=\ \sum_{n=0}^{N-1}x[n]\times exp\bigg{(}\frac{-j2\pi mk}{N}\bigg{)}\ 0\ \leq k\ \leq N-1 \tag{1}\]  
where \(N\) is the number of input samples, \(k\) is an integer index, \(2\pi/N\) is the fundamental frequency resolution, \(x=\ \{x[0],x[1],\ldots x[n]\}\) is the window of input samples, and \(X=\ \{X[0],X[1],\ldots X[n]\}\) is the vector of DFT samples. The DFT can also be written in a matrix form:  
\[\left[\begin{array}{c}X[0]\\ X[1]\\ X[2]\\ \vdots\\ X[N-1]\end{array}\right]=\ \left[\begin{array}{ccc}W_{0}^{0}&\cdots&W_{N-1}^{0}\\ \vdots&\ddots&\vdots\\ W_{0}^{N-1}&\cdots&W_{N-1}^{N-1}\end{array}\right]\left[\begin{array}{c}x[0]\\ x[1]\\ x[2]\\ \vdots\\ x[N-1]\end{array}\right] \tag{2}\]  
\[\text{where }W=\left[\begin{array}{ccc}W_{0}^{0}&\cdots&W_{N-1}^{0}\\ \vdots&\ddots&\vdots\\ W_{0}^{N-1}&\cdots&W_{N-1}^{N-1}\end{array}\right]\ \text{is DFT coefficient matrix. DFT calculation requires many mathematical operations such as \(4N^{2}\) real multiplications and \(N\) (\(4N-2\)) real additions. The FFT is a computational implementation of the DFT and it reduces the complexity of computing the DFT from \(O\big{(}N^{2}\big{)}\) to \(O\big{(}Nlog^{N}\big{)}\) that is more numerically efficient than the DFT [17].
# Green Synthesis, Characterization, and Antifungal Efficiency of Biogenic Iron Oxide Nanoparticles
## 2 Materials and Methods
### Antifungal Efficiency of Standard Fungicide against the Tested Strains  
Two pathogenic strains of _Alternaria alternata_, namely _A. alternata_ OR236467 and _A. alternata_ OR236468 were provided from the Botany and Microbiology Department, College of Science, King Saud University. The food poisoning method was employed to assess the antifungal effectiveness of a commercially available fungicide, specifically (Metalaxyl + Mancozeb), against the fungal strains under investigation. Various concentrations of the fungicide (50, 100, 200, 400, and 800 ppm) were introduced into the PDAmedium subsequent to the process of sterilization. Subsequently, the plates were inoculated with a 6 mm disc containing fungal growth and subjected to incubation at a temperature of 25 \(\pm\) 2 \({}^{\circ}\)C for a duration of 7 days. In contrast, control PDA plates were utilized, which were inoculated solely with a 6 mm disc of fungal growth. The measurement of the growth diameter of the pathogenic fungal strain was conducted utilizing a Vernier caliper. The growth inhibition ratio (%) was determined by applying the following formula:  
\[\%\ inhibition=(A-B)/A\times 100\]  
where A represents the growth diameter observed in the control plates, and B represents the growth diameter observed in the treated plates.
# Use of Smart Technology to Improve Management of Utility Tunnels
## 3 Smart System Design
### Data Analysis  
Data analysis concerns the transformation of real-time and historical data into operational information, to improve the efficiency and security of the tunnel and hosted utilities.  
Security analysis aims at improving the early detection of abnormal events such as water or gas leaks, water infiltration, unusual increases in the temperature, and illegal intrusions, as well as faults in the tunnel equipment related to access control, ventilation, smoke extraction, and firefighting. It aims also at conducting rapid security procedures including automatic actions such as turning off/on tunnel equipment, closing/opening access, closing/opening compartmentalization doors, turning on firefighting systems, smoke evacuation, alerting staff inside the tunnel, and sending alerts to staff and authorities concerned by the incident.  
Early incident detection is based on the comparison of real-time data with references values or baseline values, which could be completed and enhanced by cross-data analysis and artificial intelligence. According to the degree of violation of the reference values, a risk score is attributed to the event, and an appropriate procedure is activated. This procedure can include additional verification, alerts, turning off/on equipment, or emergency service intervention.  
Post-incident analysis is crucial. It concerns analysis of data collected during the incident, as well as pre- and post-incident data. It includes also the staff response to the incident and the interaction with the tunnel and utilities managers. This analysis allows understanding the incident response of systems and staff. It allows also checking the efficiency of incident management procedures and improving these procedures, including incident early detection and response strategies.
## 4 Results and Discussion
### Information Visualization  
While the number of samples collected does not provide enough variation to be statistically significant, the goal of only having 2 experts performing the qualitative evaluation was to validate the design choices by argument from authority, so glaring errors that can negatively impact worker performance, and thus skew the results, could be corrected before proceeding with the test on the factory floor. Only minor text corrections were requested, but the qualitative evaluation of each criteria the questions belong to provided useful insight on the workers' priorities, needs and capabilities that inform future design decisions. One of the questions in the Perceived Ease of Use criteria was excluded due to a semantic misinterpretation resulting in a missing value.  
As seen in Table 5, all criteria were rated above 3.50 (1 to 5 Likert scale). Both workers show a great degree of internal consistency, with the reason for Inspector 2 giving a  
Figure 7: Examples of detection results on real test images with different improvements upon the Baseline, including fine-tuning with CycleGAN and CUT converted images and fine-tuning the latter model with pseudo labeling (PL) of real images. The last column represents the results of the simulation of a fixed camera setup (FCS).  
lower average score being different standards of evaluation, as confirmed by the feedback provided in the open questions. Inspector 1 put particular emphasis on how the support tool would help reduce fatigue, the skill gap between workers and factory costs, while Inspector 2 paid more attention to design flaws and pointed out some improvements (larger screen and button image resolution and removal of the 3D visual representation). Both stated how useful this tool would be to their line of work, particularly at how easy and fast it makes the identification and reporting of non-conformities, and how much the existence of an integrated camera and the notes options facilitate the documentation process.  
A set of clarification questions were made outside of the questionnaire, which provided additional insight into the answers provided:  
* The 3D vehicle representation was considered unnecessary because of their level of expertise no longer requiring a visual aid to know where each part should be located in the vehicle. The label description suffices;
* In the paper version the reporting of a non-conformity requires a lengthy written description of the occurrence, which the support tool partially eliminates just from having each button associated with the respective part;
* In the paper version the photographic and additional written details of the non-conformity are optional, due to the additional hassle of uploading the information in the physical terminal under tight time constraints.
## 2 Overview of Blockchain Technology
### How Blockchain Technology Works
#### 2.2.1 Consensus Algorithms
The Byzantine generals then engage in combined action by coordinating several army units to attack a citadel simultaneously [21]. Regarding blockchains, this involves obtaining consensus on whether to verify a block or set of transactions. The difficulty lies in the fact that messages sent between the generals must traverse enemy territory and might potentially be lost without alerting the sender or the receiver, akin to traveling via an unstable, decentralized network. Additionally, some of the generals might be traitors who want to disrupt the combat strategy by transmitting misleading or altered communications, or by ignoring messages altogether. The problem is to guarantee that faithful generals can come to an agreement on the offensive strategy, without a few disloyal individuals leading them to choose an ineffective plan. In blockchain terminology, a limited number of untrustworthy or possibly malicious nodes should not have the ability to influence the confirmation of a fraudulent block or set of transactions. These transactions are verified individually by well-known and trusted validators, making these algorithms more suitable for implementation in trusted environments [22]. Figure 6 depicts the way practical Byzantine fault tolerance (PBFT) works. Figure 5: Delegated proof-of-stake (DPoS). Proof-of-authority is another consensus algorithm used in blockchain networks. In this system, approved accounts act as validators and are the ones responsible for validating blocks and transactions. Proof-of-authority (PoA) algorithms are based on the reputation and identity of their validators. They are chosen based on their reliability and overall commitment to the network [23]. Usually, the identity of these validators is public and easily verifiable, thus adding an extra layer of accountability to the whole proof-of-authority (PoA) system. Since those validators put their identity and reputation at stake, they are quite motivated to maintain and enhance the integrity of the network. Generally, proof-of-authority (PoA) processes of transaction validation are much faster than proof-of-work's (PoW) or proof-of-stake's (PoS), since validators are limited and well known in this mechanism, making it ideal for organizations requiring more scalable blockchain solutions [24]. Figure 7 shows us how proof-of-authority (PoA) really works. Figure 6: Practical Byzantine fault tolerance (PBFT). Figure 7: Proof-of-authority (PoA). In Table 1 presents a comparison between the most common of blockchain consensus mechanisms.