esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Llme-SPME Method Optimization  
Many factors could affect the LLME-SPME process. Some of them are optimized and discussed in this study including selection of extraction solvent, volume of extraction solvent, extraction time and temperature, SPME fiber coatings, and effects of dispersive solvent, stirring, and salt. The portable GC/MS system can only perform manual SPME mode. The extraction optimization process was performed on a bench-top GC/MS instrument equipped with an autosampler because the autosampler can reduce the error compared to manual injection. The bench-top instrument is also more stable than the portable instrument, and can enable the liquid injection mode which can be used to evaluate the absolute recoveries of the SPME and LLME-SPME methods. The peak area was calculated by integration in the TCE retention time window of the extracted molecular ion _m/z_ 132 chromatogram. The peak area was used to compare the extraction efficiencies obtained from different extraction conditions.
# Hybrid Multi-Strategy Improved Butterfly Optimization Algorithm
## 4 Simulations and Analyses
### Analysis of Function Optimization Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Function** & **Algorithm** & **Optimal Value** & **Worst Value** & **Average Value** & **Standard Deviation** \\ \hline \multirow{4}{*}{F1} & IBOA & 0 & 0 & 0 \\  & BOA & \(1.4878\times 10^{-7}\) & \(4.4612\times 10^{-7}\) & \(2.4402\times 10^{-7}\) & \(5.5853\times 10^{-8}\) \\  & PSO & \(0.0070901\) & \(0.040186\) & \(0.018441\) & \(0.0065241\) \\  & SIBOA & 0 & 0 & 0 & 0 \\  & PWMBOA & 0 & 0 & 0 & 0 \\  & CFSSBOA & \(9.6278\times 10^{-148}\) & \(7.0809\times 10^{-90}\) & \(1.504\times 10^{-91}\) & \(1.0016\times 10^{-90}\) \\ \hline \multirow{4}{*}{F2} & IBOA & 0 & 0 & 0 \\  & BOA & \(3.0092\times 10^{-15}\) & \(1.0444\times 10^{-10}\) & \(5.515\times 10^{-12}\) & \(1.5167\times 10^{-11}\) \\  & PSO & \(0.30662\) & \(1.3457\) & \(0.53501\) & \(0.18852\) \\  & SIBOA & \(7.2297\times 10^{-170}\) & \(9.0327\times 10^{-157}\) & \(4.7102\times 10^{-158}\) & \(1.7459\times 10^{-157}\) \\  & PWMBOA & 0 & \(1.3399\times 10^{-5}\) & \(2.6798\times 10^{-7}\) & \(1.8949\times 10^{-6}\) \\  & CFSSBOA & \(1.4647\times 10^{-22}\) & \(3.5006\times 10^{-9}\) & \(7.037\times 10^{-11}\) & \(4.95\times 10^{-10}\) \\ \hline \hline \multirow{4}{*}{F3} & IBOA & 0 & 0 & 0 \\  & BOA & \(1.1995\times 10^{-7}\) & \(3.8247\times 10^{-7}\) & \(2.1382\times 10^{-7}\) & \(4.4449\times 10^{-8}\) \\ \cline{1-1}  & PSO & \(0.60127\) & \(9.9668\) & \(1.9745\) & \(1.4246\) \\ \cline{1-1}  & SIBOA & 0 & 0 & 0 \\ \cline{1-1}  & PWMBOA & 0 & 0 & 0 \\ \cline{1-1}  & CFSSBOA & \(1.5661\times 10^{-131}\) & \(1.8499\times 10^{-64}\) & \(3.7058\times 10^{-66}\) & \(2.6161\times 10^{-65}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Algorithm Optimization Results. The optimal value and the worst value can reflect the optimization quality of the algorithm. It can be seen from the data in Table 3 that for functions F1, F2, F3, F4, F8, and F10, the improved algorithm IBOA can achieve the theoretical optimal value when solving them. For functions where the optimal value of IBOA has not reached the theoretical optimal value, such as F5, F6, and F7, IBOA is the best-performing algorithm among multiple algorithms. For the F9 function, the optimal value of IBOA is the same as that of SIBOA, PWMBOA, and CFSSBOA, and is eleven orders of magnitude higher than BOA, which indicates that the introduction of multiple strategies can indeed enhance the performance of the algorithm.
# Deep Learning for COVID-19 Diagnosis from CT Images
## 3 Results
### CT Image Classification via Deep Learning
#### 3.2.3 Two-Class Classification on COVID-CT  
For this experiment, we proceeded in two steps: initially, we used the entire COVID-CT dataset as a test dataset for the top four models obtained from the Section 3.2.1 experiments. Subsequently, it was decided to perform a fine-tuning strategy on the same models. In  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Net** & **Pre** & **Rec** & **Spe** & **Acc** & **F1** & **MAvA** & **MAvG** \\ \hline AlexNet & 91.88\% & 92.50\% & 96.64\% & 95.38\% & 92.17\% & 91.75\% & 91.88\% \\ GoogLeNet & 89.46\% & 88.96\% & 95.13\% & 93.59\% & 80.08\% & 89.46\% & 89.39\% \\ InceptionV3 & 95.48\% & 94.34\% & 97.54\% & 96.97\% & 94.84\% & 95.48\% & 95.48\% \\ VGG16 & 96.65\% & 96.57\% & 98.44\% & 97.93\% & 96.58\% & 96.65\% & 96.63\% \\ VGG19 & 97.85\% & 97.87\% & 99.08\% & 98.87\% & 97.86\% & 97.85\% & 97.84\% \\ ShuffleNet & 95.36\% & 94.92\% & 97.94\% & 97.28\% & 95.13\% & 95.36\% & 95.34\% \\ MobileNetV2 & 94.24\% & 93.05\% & 97.31\% & 96.38\% & 93.38\% & 94.24\% & 94.14\% \\ ResNet18 & 96.41\% & 96.67\% & 98.22\% & 97.71\% & 95.98\% & 96.41\% & 96.40\% \\ ResNet50 & 92.19\% & 89.53\% & 95.61\% & 94.62\% & 90.45\% & 92.19\% & 92.16\% \\ ResNet101 & 94.99\% & 93.06\% & 97.16\% & 96.53\% & 93.08\% & 94.99\% & 94.97\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Macro-average performance for testing on COVID-CT-2A.  
Figure 4: MAVG and classwise accuracy trends with the different classifiers adopted. Cov, Norm, Pne stands for the COVID-19, normal, and pneumonia classes, respectively.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Net** & **COVID-19** & **Normal** & **Pneumonia** & **AVG** \\ \hline AlexNet & 88.89\% & 96.83\% & 94.40\% & 93.97\% \\ GoogLeNet & 73.10\% & 92.06\% & 94.90\% & 86.52\% \\ Inception V3 & 83.63\% & 98.41\% & 98.40\% & 93.48\% \\ VGG16 & 90.64\% & 97.62\% & 100.00\% & 96.09\% \\ VGG19 & 95.91\% & 98.41\% & 97.60\% & 97.31\% \\ ShuffleNet & 85.96\% & 98.41\% & 96.00\% & 93.46\% \\ MobileNet V2 & 77.78\% & 98.41\% & 100.00\% & 92.06\% \\ ResNet18 & 82.46\% & 98.41\% & 98.40\% & 93.09\% \\ ResNet50 & 66.08\% & 99.21\% & 96.00\% & 87.10\% \\ ResNet101 & 71.93\% & 99.21\% & 99.20\% & 90.11\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Patient-oriented testing with COVID-CT-2A.  
particular, we chose VGG19, given its results in both previous experiments, MobileNetV2, one of the most superficial networks with good results in classifying patients of the _normal_ and _pneumonia_ classes, and, finally, VGG16 and ResNet18, being the two networks with the best results after VGG19. The dataset was then divided into training, validation, and testing, according to the percentages provided by the authors. Table 4 shows the results on the whole dataset, while Table 5 shows the results after the fine-tuning strategy.
## 3 Results
### Experiment Verification and Analysis
#### 3.3.3 Verifying and Analyzing the Elastic Bands
The force generated by this elastic band was equivalent to the force produced by spring partitioning (segmentation), ensuring that each diamond-shaped unit experienced the same force from the band. To install the TPU elastic bands onto the exterior of the original-sized stent, a small stretching machine was constructed using 2 mm-diameter screws, nuts, and two 3D-printed discs. The ends of the band were affixed to the grooves of the discs, and by rotating the nut, the band was stretched to 6.9 mm before being loaded onto the stent (Figure 19a). At this stage, the recovery force of the TPU elastic band was 0.126 N (Figure 18), which is greater than the 0.123 N compression force required by the single structure when the stent undergoes a 0.2 mm compression displacement. As a result, the recovery force from the band caused the stent to expand. When the TPU elastic band contracted to 6.7 mm, its recovery force reached approximately 0.123 N, equal to the compression force of the stent, resulting in equilibrium. At this point, the stent remained in its expanded state with a measured diameter of 6.4 mm (Figure 19b), which is less than 1% larger than the 6.354 mm diameter predicted by finite element simulations. Figure 19c shows a partial enlarged view of the self-expanding stent. To restore the stent to its original diameter, the TPU elastic band simply needs to be stretched again to 6.9 mm. These experimental results confirm the feasibility of the self-expanding stent developed in this study.
# Equipment Health Assessment: Time Series Analysis for Wind Turbine Performance
## 2 Data and Methods  
### Data  
A SCADA dataset for wind turbine power generation collects real-time operational, environmental, anomalous, electrical, and communication data from individual turbines or across an entire wind farm [21; 22]. It encompasses turbine status (on/off, power output, rotor speed), environmental conditions (wind speed, direction, temperature), alarms for irregularities, electrical parameters (voltage, current), and communication details. The initial SCADA dataset measures approximately 2000 different features. We have limited our scope of interest, however, before downloading the data from a data collection server [7], to keep the data size small. Therefore, we have excluded all features that are metadata like timestamps or Boolean values related to wind turbine status. For our study, we are interested in the power generation performance of each individual wind turbine. Therefore, we mainly focus on the measurements of actual power output and any related factors. Wehave chosen relevant parameters based on the following turbine components: e.g., rotor, pitch, gearbox, nacelle, power, and environmental information like wind [23].  
After downloading an initial dataset with 76 features, we have conducted an exploratory data and correlation analysis on one wind turbine (no. 5) to further downsize the dataset of interest. The wind turbine was selected based on domain information that it has active pitch alignment and is considered one of the better performing wind turbines. We were able to decrease the feature set to 24 features, omitting 9 features because of rare changes in values and 43 features because of very high correlation (larger than 0.9) to at least one other feature. The correlation heatmap of the remaining features of interest is shown in Figure 1. Here, we highlighted the row and column for the actual power output (\"WTUR.W.mag.f\") which is our target feature of interest to evaluate the power generation performance of a wind turbine. We also added an additional feature to the heatmap which is a calculation of the sinus radius of the wind direction, where the original wind direction values are given in degrees.  
Figure 2 shows the actual power output of the 13 wind turbines over the available time frame from end of October 2022 to early June 2023. The timelines colored in orange are considered \"good\" based on initial information received from the data provider. We also got informed that the whole wind farm is known to show \"bad\" performance between mid of November to mid of December 2022 due to dust collection. Furthermore, we can observe in Figure 2 that there are several periods of missing data mostly caused by temporary shutdowns of the SCADA data collection system.  
Figure 1: Correlation Heatmap between Features of Interest (the Power output is denoted using the Black box).
# A Robust and Effective Two-Factor Authentication (2 FA) Protocol Based on ECC for Mobile Computing
### Motivations and Contribution  
In practical terms, a 2 FA protocol (password + smart card as the two authentication factors) is supposed to offer comprehensive security and various desired characteristics. Table 1 shows the four essential security goals [34] that a 2 FA protocol must meet.  
However, current state-of-the-art 2 FA protocols do not meet at least one of the four presented security goals. For instance, according to the acknowledged criteria and heuristic analysis of this paper, the protocol in [35] cannot offer user un-traceability, the protocols in [36; 37] cannot resist password-guessing attacks or provide session-key security, and the protocol in [38] is vulnerable to counterfeiting attacks. Similarly, using a heuristic analysis with detailed attack steps, [PERSON] et al. [39] found that the static key and the client's password in the protocols in [40; 41] can be obtained by any attacker. To enhance security and maintain high efficiency, we developed a robust 2 FA protocol for mobile computing. The key contributions of this paper are as follows:  
1. Design of a 2 FA protocol for mobile computing The \"Fuzzy-Verifiers\" [42] and \"Honeywords\" [42] techniques, which can be used to construct a fuzzy password verifier and effectively resist password-guessing attacks, were applied in our protocol. Further, based on the hash function and ECC, we designed a 2 FA protocol that supports user registration, mutual authentication, and user password updating.
2. The semantic security of the 2 FA protocol The semantic security of the session key was proved with a security proof in our protocol. Additionally, through heuristic analysis, we demonstrated that the proposed protocol meets the ten security evaluation criteria. Furthermore, our protocol's entity authentication, message confidentiality, and session key security were confirmed using the ProVerif [43] tool.
3. Performance analysis of the 2 FA protocol A comparative analysis of the functionality, communication, and computation cost of the proposed protocol was conducted with six common related protocols; i.e., those of [PERSON] et al. [37] (IEEE IoTJ'18), [PERSON] et al. [5] (IEEE IoTJ'18), and so on. A better balance between security and availability was achieved in the proposed protocol according to the comparison results.
# Designing Automated Logistics Warehouse Stackable Bidirectional Infinite-Loop Modules
## 5 Results  
### Experimental Results of Balanced Inventory Logistics  
The experimental results of 250 simulation runs regarding the time required for the selection, retrieval, and delivery of ordered goods from the warehouse to the worker, until the task was completed, are summarized in Table 4.  
The to-be stacker crane model took 2.03 h to process 100 PLT, 6.09 h for 300 PLT, 12.15 h for 600 PLT, 20.28 h for 1000 PLT, and 36.53 h for 1800 PLT. These results indicate that the to-be stacker crane model can efficiently utilize time, with improvements ranging from approximately 37.54 to 73.22% when compared to the as-is fork lift and as-is stacker crane  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Logistics Shipment Quantity (PLT)**} & \multicolumn{2}{c}{**Average Time (s)**} & \multicolumn{2}{c}{**Average Time (h)**} \\ \hline \multirow{4}{*}{100} & As-is fork lift & 27,284.63 & 7.58 \\ \cline{2-4}  & As-is stacker crane & 11,704.66 & 3.25 \\ \cline{2-4}  & To-be stacker crane & 7330.62 & 2.03 \\ \hline \multirow{4}{*}{300} & As-is fork lift & 82,156.09 & 22.82 \\ \cline{2-4}  & As-is stacker crane & 35,029.83 & 9.73 \\ \cline{2-4}  & To-be stacker crane & 21,935.43 & 6.09 \\ \hline \multirow{4}{*}{600} & As-is fork lift & 164,489.20 & 45.69 \\ \cline{2-4}  & As-is stacker crane & 69,972.45 & 19.43 \\ \cline{2-4}  & To-be stacker crane & 43,752.13 & 12.15 \\ \hline \multirow{4}{*}{1000} & As-is fork lift & 274,748.71 & 76.32 \\ \cline{2-4}  & As-is stacker crane & 119,241.88 & 33.12 \\ \cline{2-4}  & To-be stacker crane & 73,041.91 & 20.28 \\ \hline \multirow{4}{*}{1800} & As-is fork lift & 494,373.59 & 137.32 \\ \cline{2-4}  & As-is stacker crane & 214,393.35 & 59.55 \\ \cline{2-4}  & To-be stacker crane & 131,518.45 & 36.53 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average time taken for calls of a specified amount of logistics.  
Figure 6: (**a**) Visual representation of the to-be stacker crane constructed using Unreal Engine 5; (**b**) static meshes used in Unreal Engine 5.  
models. Thus, we demonstrated that time optimization is achievable using our proposed warehouse design.  
Figure 7 visually depicts the time differences among the models based on call quantity, further validating the developed model's time efficiency.
## 4 Conclusions  
This study proposed a methodology for manufacturing smart slabs with embedded magnetoelastic sensors aiming at providing damage diagnosis capabilities with conventional, affordable equipment. Successful application of the proposed damage diagnosis concept requires that the smart slab must  
Figure 11: **(a)**: Pole-zero maps in the complex Im-Re plane of CC-AR(80) models with red crosses associated to poles corresponding to -C1 slabs and green crosses associated to poles corresponding to -C2 slabs. **(b)**: Autocorrelation plots indicating uncorrelated CC-AR(80) residuals (values mostly in-between the dashed limits) for one signal from -C1 and another from -C2 slabs. be under continuous monitoring, with the latter starting for a slab in perfect operational condition. Comparisons of experimental data over time will then detect and evaluate any occurring damage. The concept considered magnetoelastic strips integrated via 3D printing procedures in CFRP slabs. During the two testing procedures proposed, the smart slabs emitted (in a passive manner) electrical signals under vibration loading. These signals were received in a contact-less manner by conventional low-cost reception coils. The analysis of the power spectra of these signals validated the sensing concept, since they allow for computing the slab eigenfrequencies at high frequency regions. Such frequencies may thus be monitorable by combining conventional oscilloscopes, low-cost coils along with extensive analysis of the signal involved. Furthermore, it was shown that damage occurrence in slabs caused spikes in specific (high) frequency areas of the signals, and that following a specific signal modeling procedure, quantitative indications for assessing damage severity could also be found. Future work is underway to extend the concept from the current basic state (of a cantilever setup suffering from notches) to damage diagnosis cases of actual structural components. These include specimens of larger dimensions, implemented in representative to real-life structures, suffering from various damage profiles such as cracks, delaminations, and so on. Conceptualization D.E.M. ; methodology D.E.M. and D.G.D. ; software D.G.D. and P.J.C. ; validation D.G.D. and P.J.C. ; formal analysis D.G.D. ; investigation D.E.M. and D.G.D. ; resources D.E.M. and D.G.D. ; data curation D.E.M. and D.G.D. ; writing--original draft preparation, D.G.D. ; writing--review and editing D.G.D. and D.E.M. ; visualization D.G.D. and D.E.M. ; supervision, project administration, D.E.M. and D.G.D. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors would like to thank Professors [PERSON] for kindly providing access to experimental facilities and [PERSON] for the useful discussions and suggestions on this work. Both Professors are from the National Technical University of Athens (NTUA). The authors declare no conflict of interest. The following abbreviations are used in this manuscript:
# Resilience Quantification of Smart Distribution Networks--A Bird's Eye View Perspective
## 5 Literature Review
### Results and Insights
#### 5.4.7 Economical Cost  
DSOs do not just scrutiny costs due to phenomenal disasters and attacks, but also audit their investment strategies to find the best balance between resilience and minimal spending. Cost is inserted in resilience studies at different levels, most of the time directly on the metric [123; 127; 128; 133; 143], but can also be incorporated in objective functions of cost-benefit analyzes [134; 134; 138; 141] that search the optimal tradeoff between resilience and associated investment costs.
## 3 Materials
### Feature Extraction
#### 3.2.5 Fourier Transform  
Fourier transform is a mathematical technique used to analyze signals and data in the frequency domain. It transforms a signal from the time domain where it is represented as a sequence of amplitude values over time, into the frequency domain, where it is represented as a combination of sinusoidal waves with different frequencies.
## 2 Problem Description and Formulation  
Figure 1a depicts the cavity and coordinate system along with the boundary conditions considered in this study. It concerns a 2D square cavity differentially heated with a width \(L\) and a height \(H\). In the present study, we set \(L=H\). The confined fluid in the cavity is assumed incompressible and Newtonian, and the flow occurring inside is laminar. The Cartesian coordinate system is labeled \((x,y)\) of which the \(x\)-axis is horizontal and the \(y\)-axis upwardly directed in the direction opposite gravity. During the numerical simulation process, the lower and upper walls are assumed perfectly insulated (adiabatic), and the two vertical walls are isothermal and held at uniform temperatures \(T_{h}\) and \(T_{c}\) (\(T_{h}>T_{c}\)).  
Recall that the temperature difference between the vertical sides produces a temperature gradient in the cavity and a consequent density difference that induces a fluid motion, that is the convection. In  
Figure 1: Schematic view of the differentially-heated square cavity with initial and boundary conditions (**a**) Natural convection (no melting); (**b**) melting with convection.  
addition, all thermo-physical properties of the fluid are assumed constant, except the density, which varies in so far as the [PERSON]'s approximation is respected. When dealing with convection melting, Figure 1b shows the solid-liquid phase change problem, also called the melting by convection problem. The left vertical side is kept at \(T_{h}\) with \(T_{h}>T_{m}\), \(T_{m}\) being the melting temperature. As for the right side, it is kept at \(T_{c}\) (= \(T_{m}\)). Meanwhile, the top and bottom sides remain adiabatic. Initially, the medium is in a solid state at \(T_{m}\).
## 4 Experiments and Results
### Datasets  
To validate the effectiveness of the proposed RL-Fusion model fusion method, we conducted performance evaluations on the Yayi [21], MMLU [19], and C-Eval [20] datasets. Importantly, the training set used for model fusion does not overlap with the test sets from these datasets, ensuring an unbiased evaluation. The Yayi dataset is a large-scale corpus containing millions of samples, with 54% in Chinese and 46% in English. It covers data from 12 domains, including finance, society, biology, business, industrial manufacturing, chemistry, vehicles, science, medical diseases, personal life, safety, and general knowledge, spanning a wide range of scenarios. In our experiments, named entity recognition (NER) was specifically chosen as the task to evaluate the model's performance before and after fusion, as it is a critical area where we aim to achieve performance enhancement through the RL-Fusion approach. To this end, we utilized the NER data from Yayi, which includes 28 Chinese entity types (e.g., person, geopolitics, organization, body parts, drugs) and 130 English entity types (e.g., animal, weapon, conference, book). NER was selected as the key task for performance evaluation in our experiments, and model performance was evaluated using the SOTA LLM evaluation module.  
MMLU (Massive Multitask Language Understanding) is a multi-task, multi-language evaluation dataset proposed by OpenAI, designed to assess the performance of large language models across a variety of fields and tasks. This dataset aims to assess the model's general knowledge and reasoning abilities, encompassing 57 tasks across various disciplines, including history, mathematics, computer science, economics, medicine, and law. The difficulty of the tasks range from high school to undergraduate and graduate levels, with all questions presented in a multiple-choice format with four options. Additionally, it supports multiple languages, enabling the evaluation of the model's cross-lingual capabilities.  
C-Eval is a comprehensive Chinese evaluation suite designed to assess the advanced knowledge and reasoning abilities of base models within the Chinese context. It is the first evaluation suite to comprehensively cover multiple difficulty levels (middle school, high school, university, and professional levels) in Chinese, with multiple-choice questions across 52 distinct subjects, spanning a broad spectrum of fields from the humanities to science and engineering.