esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experimental Results and Simulations
### Comparison of EWOA with Other Well-Known Algorithms  
After performing extensive experiments to prove the efficiency of EWOA over the conventional WOA, we validate its performance by comparing it with a set of well-regarded algorithms, namely Binary Harris Hawks Optimization (BHHO) [97], Binary Gravitational Search Algorithm (BGSA) [98], Binary Grasshopper Optimisation Algorithm (BGOA) [99], Binary Particle Swarm Optimization (BPSO) [100], Binary Grey Wolf Optimizer (BGWO) [101], Binary Bat Algorithm (BBA) [102], Binary Ant Lion Optimizer (BALO) [103], and Genetic Algorithm (GA) [104]. We adopted these competitors because they are categorized into different groups of meta-heuristic techniques. For instance, GA is evolutionary-based, GSA is physics-based, while the others are swarm-based. Hence each algorithm has its exploratory and exploitative potentials. Moreover, these algorithms have been successfully applied as wrapper FS approaches in different domains. To make a fair comparison, ADASYN was used with all competing approaches.  
Table 17 presents a deep comparison between all approaches in terms of average AUC, number of features, and fitness values with STD values and the F-test ranking. The reported results clarify that the proposed EWOA-S2 and EWOA-V4 exceed the other algorithms in achieving higher ACU rates with fewer features on the utilized datasets. Accordingly, the proposed EWOA efficiently keeps the most informative features that offer better classification performance in dealing with student performance prediction. Based on the overall ranking, the EWOA-V4 outperforms all other methods with the rank of 1.33. It is ranked as the best performing method in terms of the considered metrics. Moreover, EWOA-S2 comes in second place with a mean rank of 1.67. In contrast, the performance of BBA is the worst one (rank of 8.83).  
Figure 16 illustrates the convergence curves of the developed EWOA-V4 versus other methods. Obviously, EWOA-V4 achieves a better acceleration trend in dealing with both datasets. The diverse exploratory and exploitative behaviors in the developed EWOA-V4 improve its ability to explore the targeted space and converge faster toward better solutions.
# In Vivo Efficacy of Contact Lens Drug-Delivery Systems in Glaucoma Management. A Systematic Review
## 3 Results
### Molecularly Imprinted CL  
Another type of CL drug-delivery system was molecular imprinting [55]. During CL polymerisation, a template drug and functional monomers created multiple macromolecular memory sites, similar to drug receptors [56]. The CL was then washed and reloaded with the active drug, which was entrapped in the CL memory site (Figure 4) [56]. This technique increased the CL drug loading capacity [56]. It has been applied to different ophthalmic drugs--hyaluronic acid, prednisolone, diclofenac, ketotifen fumarate, norfloxacin, and acyclovir [57; 58; 59; 60; 61; 62]. However, to date, only timolol has been investigated in vivo among hypotensive drugs.  
Figure 4: Drug release by molecularly imprinted CL. Reproduced with permission from [53], ©Copyright 2015, Elsevier.  
[PERSON] et al. compared a molecularly imprinted CL to a timolol-soaked CL and timolol eyedrops [40]. In particular, timolol concentration and bioavailability in rabbits' tears were evaluated. The molecularly imprinted CL (30 \(\upmu\)g of drug dose) delivered timolol at a measurable concentration for 180 min, longer than the soaked CL (21 \(\upmu\)g of drug dose) (90 min) and eyedrops (34 and 125 \(\upmu\)g of drug dose) (60 min). Furthermore, molecularly imprinted CL bioavailability was 3.3- and 8.7-fold superior to the soaked CL and eyedrops, respectively [40].  
However, further studies are warranted to evaluate the molecularly imprinted CL as an extended drug-delivery system.
## 2 Models and Assumptions
### Different Antenna Patterns Assumptions Analyzed
#### 2.3.2 L5 Antenna Patterns  
In this study three different options for the L5 pattern were used, approximated from the L1 patterns following the principle explained in [16]. We carried out a signal characteristics analysis for three different assumptions on the L5 antenna gain patterns:  
1. L5 antenna pattern 1. In the first case we modeled a different L5 antenna gain pattern for each satellite of IIR and IIR-M Blocks and only one L5 pattern for all the satellites of GPS IIF Block.  
For each IIR satellite and IIR-M satellite we derived the L5 patterns from the GPS L1 assumed patterns as follows:  
1. First, for each satellite, we computed a 2D pattern from the L1 detailed 3D pattern, where the gain for each elevation is the mean value of the gains for L1 for the same elevation and for all azimuths.
2. Second, maintaining the gain unchanged, we multiplied each elevation angle by a 1.1 factor. Thus, essentially the 2D gain pattern of L5 differs from the one of L1 only in elevation. To form the single L5 pattern used for all the IIF satellites from the L1 pattern, the same procedure was adopted.
2. L5 antenna pattern 2. In the second case we used the same pattern as the one defined for IIF satellites in the first case, for all GPS L5 satellites.
3. L5 antenna pattern 3. In this case we used the pattern provided in [5].  
In Figure 5 is shown in blue the assumed L5 antenna gain pattern for the IIF satellites (in the first case) approximated from the L1 antenna gain pattern as explained above, also used in the second case for all GPS satellites. In red we can see the L5 antenna pattern 3.  
Figure 4: Block IIF GPS L1 antenna gain pattern.
## 3 Antibiotic Resistance in Hospital and Municipal Wastewater
### Regional Variation of ARGs in HWWs  
The diversity of ARGs in HWW varies significantly across the globe. The variation in ARGs depends on the antibiotic usage patterns and the effectiveness of WWT in that region. Based on the research conducted on the number of AMR gens from the wastewater samples in Asia (China, India and Iran) [11; 48; 62; 63; 65], Europe (Spain and Sweeden) [66; 22; 67] and South America (Brazil) [68; 22], the developing countries such as China, India and Brazil showed the highest AMR gene copy numbers (10\({}^{5}\)-10\({}^{7}\) ARG copies/mL) in HWW samples [63; 22; 48; 65]. While the HWW samples from Spain and Iran exhibited moderate AMR genes (10\({}^{3}\)-10\({}^{5}\)copies/mL) [11; 22]. The ARGs survey conducted in Sweden HWWshowed the least (10\({}^{1}\)-10\({}^{3}\) copies) among the above countries [67]. Of these countries, studies carried out in the Chinese cities of Zhejiang, Shantou and Linan have reported the predominance of _bla\({}_{OXA}\), aac(6\({}^{\prime}\))-lb-cr_, _su11_, _su2_, _tetW_ and _tetA_ carried by pathogens such as _E. coli_, _P. aeruginosa_, _Aeronomas_ sp. and _Acinetobacter_ sp. across HWW samples [62; 63; 65]. On the other hand, ARGs such as _blaNDM-1_, _su11_, _su2_ and _mcr-5.1_ were found to be the most prevalent ARGs in Indian hospitals. _K. pneumoniae_ and _E. coli_ were two of the prominent hosts of ARGs in Indian HWWs [48]. Similarly, HWWs of Brazil are primarily composed of ARGs such as _bla\({}_{XPC}\)_ and _bla\({}_{TEM}\)_ carried by _K. pneumoniae_, _P. aeruginosa_ and _Enterobacter_ sp. Apart from resistance towards a single antibiotic, these regions comprise the highest relative number of MDR genes and MDR pathogens. It was found that the prominent ARGs in Iranian HWW were _bla\({}_{XPC}\)_, _qnrS2_, _tetW_ and _bla\({}_{TEM}\)_; most of these ARGs were associated with _E. coli_ and _P. aeruginosa_. In addition, MDR genes found in _Proteus mirabilis_ and _Klebsiella pneumoniae_ raise concerns about their level of resistance [11]. Whereas in Spain, the HWW resistance is dominated by fluoroquinolone resistance genes such as _qnrS2_ carried by _Aeronomas hydrophila_ and _Klebsiella pneumoniae_. Even though these regions are not as critical as in China or India, there is a need for continuous monitoring of ARGs, to prevent ARG spikes in the environment. Lastly, the study by [PERSON] et al. [67] revealed ARGs (the most predominant such as _mcr-1_ and _optrA_) with concentration ranges between 10\({}^{1}\) and 10\({}^{2}\)copies/mL in HWW. It was identified that pathogens such as _Enterococcus_ species and _Staphylococcus_ species were the major carriers of such ARGs.
## 3 Performance of the Optical Wireless Link
### Average Bit Error Rate Estimation  
Another significant metric quantity that measures the reliability of the system is the frequency of bit errors occurring per time unit, known as the bit error rate (BER). Supposing that the link supports the NRZ-OOK modulation scheme, the respective instantaneous BER is expressed as follows [15; 44; 45]:  
\[P_{e}(I_{j})=\frac{1}{2}erfc\left(\frac{\sqrt{2}\eta I_{j}}{4\sqrt{N_{0}}} \right)\Leftrightarrow P_{e}(\gamma_{j})=\frac{1}{2}erfc\left(\frac{\sqrt{2 \gamma_{j}}}{4}\right), \tag{15}\]  
where erf(.) denotes the complementary error function [46].  
However, the average BER (ABER) of the link is more preferable than the instantaneous one, since the former provides a clear, durable picture of the operating FSO system. The ABER is computed as the expectation of the instantaneous irradiance between \(I_{j,min}\) and \(B\) or, equivalently, of the instantaneous BER between the minimum, \(\gamma_{j,min}\), and the maximum SNR, \(\gamma_{j,\max}\equiv\gamma_{j}(B)=B^{2}\overline{\gamma}_{j}/\left(E\big{[} I_{j}\big{]}\right)^{2}\), as follows [15; 44; 45]:  
\[\overline{P}_{e}\big{(}I_{j}\big{)}=\int\limits_{I_{j,\min}}^{B}P\big{(}I_{j} \big{)}f_{I_{j}}(I_{j})dI_{j}\Leftrightarrow\overline{P}_{e}\Big{(}\overline{ \gamma}_{j}\Big{)}=\frac{1}{2}\int\limits_{\gamma_{j,0}}^{\gamma_{j,\max}} erfc\left(\frac{\sqrt{2\gamma_{j}}}{4}\right)f_{\gamma_{j}}(\gamma_{j})d \gamma_{j}. \tag{16}\]  
Then, by substituting _erfc_ with \(erfc(x)=2Q\Big{(}\sqrt{2}x\Big{)}\) along with the \(Q\)-function approximation, \(Q(x)\approx\left[\exp\left(-x^{2}/2\right)+3\exp\left(-2x^{2}/3\right)\right]/12\), presented in [47], Equation (16) is approximated by  
\[\overline{P}\Big{(}\overline{\gamma}_{j}\Big{)}\approx\frac{1}{6\sqrt{2\pi} \sigma_{T}Z}\int\limits_{0}^{\overline{\gamma}_{0}}\left\{\exp\left[-\frac{ \overline{\gamma}_{j}B^{2}}{8\left(E\big{[}I_{j}\big{]}\right)^{2}}\exp\left( -\frac{2y^{2}}{T_{pw}^{2}}\right)\right]+3\exp\left[-\frac{\overline{\gamma} _{j}B^{2}}{6\left(E\big{[}I_{j}\big{]}\right)^{2}}\exp\left(-\frac{2y^{2}}{T _{pw}}\right)\right]\right\}\exp\left(-\frac{y^{2}}{2\sigma_{T}^{2}}\right)dy. \tag{17}\]Finally, using for the exponential function the power series of \(\exp(x)=\sum\limits_{k=0}^{\infty}x^{k}/k!\), the following mathematical expression is obtained from ABER's integral of Equation (17):  
\[P_{e}\Big{(}\gamma_{j}\Big{)}\approx\frac{T_{pw}}{12Z}\sum\limits_{k=0}^{+ \infty}\frac{1}{k!\Theta_{k}}\Bigg{[}-\frac{\gamma_{j}B^{2}}{\big{(}E\big{[}I_{ j}\big{)}^{2}\big{)}^{k}}\Bigg{(}\frac{1}{8^{k}}+\frac{3}{6^{k}}\bigg{)}erf \Bigg{[}\frac{\Theta_{k}T_{sl}}{2\sqrt{2}T_{pw}\sigma_{T}}\Bigg{]}, \tag{18}\]  
where \(\Theta_{k}=\sqrt{T_{pw}^{2}+4k\sigma_{T}^{2}}\).
# Role-Based Access Control Model for Inter-System Cross-Domain in Multi-Domain Environment
## 4 RBAC-IC Execution
### Authorization and Access Control
#### 4.2.1 Authorization Management  
Users who want to access the objects in the system need to have corresponding permissions, which are obtained by authorizing specific roles. For inner-domain authorization, the user applies for a specific role to the domain server where he belongs. If the domain administrator agrees to authorize, the domain server will check the specific role and authenticate. After the authentication is passed, the domain server will check whether the user meets the constraint required by the specific role. After the inspection is correct, the domain server will authorize the specific role to the user. The sequence diagram of applying for inner-domain authorization is shown in Figure 6.  
Figure 5: Authorization and access control framework of RBAC-IC.  
Figure 6: Sequence diagram of applying for inner-domain authorization.  
When users need to cross-domain accesses objects, it is necessary to apply for cross-domain authorization, and the domain administrator of the requested domain authorizes the specific role. The user applies for cross-domain authorization to the local domain server. After the authentication is passed and the local domain administrator agrees, the local domain server sends a request to the corresponding domain server. If the administrator of the requested domain also agrees to authorize, the requested domain server checks the specific role and determines whether the user meets the constraint. After the inspection is correct, the domain administrator authorizes the specific role to the user. In addition, domain administrators can set a valid time of the specific role. When valid time is reached, the specific role will become invalid, and the user will not be able to continue to access corresponding resources in the domain. The sequence diagram of applying for cross-domain authorization is shown in Figure 7.
# Analysis of _In Vivo_ Plant Volatiles Using Active Sampling and TD-GC\(\times\)GC-TOFMS
## 2 Materials and Methods
### Thermal Desorption Tube Preparation  
Standard 3\({}^{\prime\prime}\)\(\times\) 0.25\({}^{\prime\prime}\) stainless steel tubes with a Tenax(r) TA/Carbograph 1/Carbosieve sorbent bed (Markes International Ltd., Bridgend, UK) were pre-cleaned at 350 \({}^{\circ}\)C with a flow of 100 mL/min of ultrahigh-purity N\({}_{2}\) (boil-off from liquid nitrogen Dewar) for 30 min using a TC-20 Tube Conditioning Unit (Markes International Ltd., Bridgend UK). Three-bed sorbent tubes were used for collection to maximize the chemical classes and volatile range of compounds that can be collected and analyzed. Prior to sampling, a 4 \(\mu\)L aliquot of internal standard solution (IS) containing 10 ng/\(\mu\)L of each of n-nonane-d\({}_{20}\) (CDN Isotopes, Pointe-Claire, QC, Canada) and naphthalene-d\({}_{8}\) (Isotce, Canton, GA, USA) in methanol (Fisher Scientific, Ottawa, ON, Canada) was spiked onto the sampling end of each TD tube using a 10 \(\mu\)L glass syringe (Hamilton, Reno, NV, USA). Once the internal standards were spiked, tubes were conditioned in the TC-20 at 60 \({}^{\circ}\)C with a flow of 50 mL/min of N\({}_{2}\) for 20 min.
# A Simhash-Based Integrative Features Extraction Algorithm for Malware Detection
## 3 Methods  
The purpose of the algorithm is to use simhash to integrate the results of static analysis and dynamic analysis of malicious samples, to form a uniquely identified hash value (i.e., the integrative feature). The process of the algorithm is shown in Figure 1.  
As shown in Figure 1, the algorithm includes three sub-portions, which are a static analysis, a dynamic analysis, and a dynamic and integrative feature extraction.  
In the static analysis, the first step is to judge whether the sample has a shell and, if so, use the relevant unpack tools to remove it. Next, the API call functions are obtained through IDA Pro disassembly. In the dynamic analysis, the process is to configure and debug the sample analysis environment, run the samples in the environment, and then use Process Monitor, Wireshark and other tools to capture the file, registry, process and network behaviors. In the integrative feature extraction, we use the simhash algorithm to correlate the API functions with the file, process, registry and network behaviors respectively, and finally integrate a hash binary value. The three processes are detailed below.
# Digital Twins Generated by Artificial Intelligence in Personalized Healthcare
## 4 Discussion
### Directions for Further Research  
Future research should focus on developing methods for the seamless (automatic) integration of different data sources, including standard formats and protocols, to improve the interoperability and consistency of DTs across healthcare systems. Exploring advanced AI techniques for real-time data processing and dynamically updating DTs (reflecting the patient's current health status) will be critical to ensuring that these models accurately reflect the patient's current health status. Exploring ways to increase the accuracy of digital twins through improved data quality, more comprehensive datasets, and improved AI algorithms will be essential for reliable health predictions and interventions. Research should prioritize the development of robust privacy-preserving technologies such as federated learning, blockchain, and advanced encryption methods to protect sensitive patient data while enabling the generation of effective, reliable DTs. Exploring scalable, cost-effective computational methods, including the use of cloud computing and edge computing, will be critical to making DTs more accessible and feasible at all levels of healthcare. Research should delve into the ethical implications of digital twins, including establishing clear guidelines for accountability, transparency, and fairness in the use of AI-generated models in healthcare. Developing methodologies to scale digital twins to serve diverse and large patient populations, accounting for differences in genetics, lifestyle, and environmental factors, is a key area for future research. There is a need for research into explainable AI (XAI) approaches that can make digital twin models more understandable to healthcare providers, enabling them to trust and effectively use these tools in clinical decision-making. Conducting long-term studies to confirm the efficacy and safety of DTs in various clinical scenarios will be important for regulatory approval and widespread adoption. The ultimate goal is to explore how to effectively integrate DTs into existing clinical processes, including developing user-friendly interfaces and decision support systems that streamline rather than disrupt healthcare delivery [67; 68; 69; 70; 71].  
The future of AI-based DTs is linked to emerging technologies such as ML, 6G, IoT, edge computing and augmented reality (AR), and the growing awareness of AI-based DTs among healthcare professionals and their patients. The development of legal medicine (health medicine) can have a major impact here. This future means even greater precision, speed, and accessibility of DTs, making it an integral part of eHealth and smart city and smart territory environments.
# Improving Freight Traffic Efficiency at Urban Intersections Using Heavy Vehicle Platooning
## 3 Methodology
### Step 1--Platoon Identification
#### 3.1.2 Moving Platoon  
The moving platoon detection algorithm is critical to adaptive traffic signal systems as it allows real-time adjustment of signal timings to optimize traffic flow and reduce delays. The algorithm detects moving HCV platoons by grouping heavy vehicles based on the assigned headway threshold, which is the distance between one vehicle's leading edge and the following vehicle's leading edge. The algorithm considers factors such as lane changes, turns, and heavy vehicles leaving or joining a platoon to accurately detect and track moving HCV platoons on the road. The algorithm is applicable to all approaches of the intersection and is designed to operate effectively even in scenarios where a stopped queue of heavy vehicles exists at the intersection or where no vehicles are currently waiting.  
The moving platoon detection algorithm has been shown to be particularly effective in improving traffic conditions when connected HCVs on the road have a high penetration rate. However, the presence of conventional heavy vehicles within a platoon can present challenges for accurate platoon detection, as their behavior may not be as predictable as  
Figure 4: The developed queue length-estimation flow chart.  
that of connected HCVs. A more comprehensive approach that considers the behavior of both connected and conventional heavy vehicles within platoons is required to accurately detect and track moving HCV platoons for adaptive traffic signal control.
## 2 Materials and Methods
### EMFs from 5G Base Stations Relative to Distance
The method for exploring whether the electric field in the 5G band decreases with increasing distance can be summarized in the following steps:  
* Perform measurements (27 MHz-6 GHz) without generating data traffic;
* Record the coordinates of the measurement spots;
* Isolate measurements in the 3.5 GHz band (FR1, 5G band);
* Retrieve the locations of BS operating in the same band;
* Calculate the average distance between the measurement spots and the nth closest 5G BS using QGIS software (where \(n\) ranges from 1 to 5);
* Since the electric field is inversely proportional to the distance, \(E\sim r^{-m}\Rightarrow log_{10}E\sim-m\cdot log_{10}r\), plot the log10(distance) vs. log10(E-field) and draw the linear regression line, anticipating a decrease in electric field strength as the distance increases;
* Compute Spearman's correlation coefficient [20] between the electric field and the distance to the nth closest BS (where \(n\) ranges from 1 to 5).
## 5 Review of Mask-Face Detection Hardware Systems
### Recent Mask-Face Detection Systems
#### 5.1.3 Model Compression  
Aiming at compatibility with embedding systems and real-time implementation, various methods were proposed to compress the deep learning model. The studies in [75; 86; 87; 88; 90] all applied quantization to the deep learning models in order to fit the model into embedded systems and achieve faster inference performance. Among them, [PERSON]. [90] provides several ablation studies on both pruning and quantization procedures. It provides a guideline for object detection model compression with the strategy of a 0.6 pruning rate and static quantization. In addition, [PERSON] [87] raised that since mask-face detection is a binary classification problem (i.e., wearing mask and without mask), the process of class probability calculation could be eliminated to lessen the inference time. They treated the output confidence scores as class probabilities to avoid additional forward calculation.  
[PERSON] et al. [89] presented a special quantization method named BinaryCoP to reduce the model memory footprint. The proposed model was based on the Binary Neural Network (BNN) founded by [95]. BNN can be considered as an extreme case of quantization. It quantizes all parameters of weights and activations to one-bit precision as \(-1\) and \(1\). Consequently, most complicated calculations in the neural network can be replaced by binary operations such as XOR during the forward pass of the neural network. As such, a unique structure could be accelerated by performing deployment on a delicately designed hardware; the authors implemented their BinaryCoP models on embedded FPGAs based on the Xilinx FINN framework [96], which was designed for FPGA accelerations of BNNs. When pre-processing on the dataset, they subdivided the MaskedFace-Net dataset's [84] two classes of Correctly Masked Face Dataset (CMFD) and Incorrectly Masked Face Dataset (IMFD) into four classes of CMFD, IMFD Nose, IMFD Chin, and IMFD Nose and Mouth. From the experiments, three proposed BinaryCoP models of CNV, n-CNV and u-CNV achieved a latency of 1.58 ms, 0.31 ms, 0.81 ms and accuracy of 98.10%, 93.94%, 93.78%, respectively. The comparable results proved the effectiveness of BinaryCoP and its requirement of a low inference power of 2 W.  
Besides employing quantization to generate light-weight models, [83] proposed a hybrid approach to perform the mask-face detection task. The architecture was divided into two stages. The first stage exploited the Haar Cascade algorithm [78], also called the Voila-Jones algorithm to detect faces. It is a traditional machine learning algorithm used to carry out object detection and therefore requires fewer computational resources. The authors used pre-trained face detection Haar Cascade models in OpenCV in their method. In the second stage, they trained a MobileNetV2 [32] deep learning model from scratch as the classifier of two classes (mask-wearing and no mask/incorrect mask-wearing). From their experiments, the hybrid model free of a deep learning based detector could achieve a comparable performance.