esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Discussion
### Experimental Results and Comparative Evaluation
#### 4.3.5 Computational Efficiency Analysis  
The task of small target detection in infrared images places high demands on the accuracy and real-time performance of algorithms, making the selection of an appropriate model particularly important. To this end, this paper systematically analyzes the performance of various YOLOv8 variant models, focusing on the number of layers, parameter count, inference speed (FPS), and GFLOPs, to evaluate the ability of these models to achieve high-precision detection while meeting real-time requirements. This analysis not only provides a theoretical foundation for validating the effectiveness of the proposed models but also offers a valuable reference for their deployment in different practical application scenarios.  
Table 3 provides a comparison of the computational efficiency of various YOLOv8 variant models. From the data, it can be observed that YOLOv8n, with the lowest parameter count of 2.92 M and a relatively low computational complexity of 12.4, achieves an inference speed of 175.4 FPS, demonstrating a significant advantage in speed-sensitive tasks. Next, YOLOv8_FasterNet further reduces the parameter count to 1.72M and the computational complexity to 9.2, exhibiting extreme lightweight characteristics, but its inference speed is limited to 137.0 FPS, making it suitable for resource-constrained scenarios. YOLOv8_Gsconv achieves the highest inference speed of 192.3 FPS with a moderate parameter count of 2.64M and a relatively low computational complexity of 11.6, striking an ideal balance between real-time performance and computational efficiency. Similarly, YOLOv8_fastC2f, with a slightly lower parameter count of 2.56M and computational complexity of 11.1, achieves an inference speed of 181.8 FPS, exhibiting efficiency comparable to YOLOv8_Gsconv. In contrast, although YOLOv8_Biformer has the highest parameter count of 2.93M and computational complexity of 12.7, its inference speed is 172.4 FPS, making it more suitable for precision-focused complex scenarios.  
The proposed YOLOv8_Adv model achieves a good balance in three aspects: 344 layers, 2.34 M parameters, and a computational complexity of 10.1. Although its inference speed of 163.9 FPS is slightly lower than that of some variants, it significantly enhances the feature extraction capability through a complex network structure, performing exceptionally well in small target detection tasks in infrared images. Especially when dealing with complex backgrounds and weak target features, this model can significantly improve the detection accuracy while fully meeting real-time requirements in practical applications. This indicates that the YOLOv8_Adv model strikes an ideal trade-off between accuracy and speed, providing an efficient and reliable solution for small target detection in infrared images. In conclusion, the various models exhibit different optimization orientations in terms of parameter count, computational complexity, and inference speed, providing a range of options to meet diverse practical needs.
## 3 Methodology
### Labeling the Clash Types  
Human experts still inevitably have different subjective judgments on the same cases, and therefore the research team hired two experts who both have more than five years of experience on clash coordination and resolution to label the clash types from the same clash detection report. Those clashes with different labels by the two experts will be excluded from the training process during the machine learning phase. Once obtaining the HTML-based clash detection report, we transform it into a spreadsheet, as shown in Figure 5, for human experts to label the clash types. Besides all the information on the clash detection report shown in Figure 3, the spreadsheet also contains a column \"Clash Type\" with a drop-down list to facilitate labeling clash types by the experts. The drop-down list consists of four options: serious clashes, negligible clashes, legal interventions, and unknown. These four options use a more intuitive vocabulary; at the time of subsequent analysis, these options will correspond to the four categories suggested by the literature as errors, pseudo clashes, deliberate clashes, and unknown, respectively. Serious clashes or errors are those relevant and crucial clashes that need to be carefully examined and resolved if necessary. Except for the spreadsheet containing information derived from the clash detection report, the researcher did not provide the labeling experts with other information about the building, such as CAD drawings or 3D building models. The only information for them to determine the clash types is the clash detection report mentioned in Section 3.1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Clashing Item Types** & **Pipes** & **Fittings** & **Total** \\ \hline Framings & 153 & 25 & 178 \\ Walls & 155 & 68 & 223 \\ Columns & 1 & 1 & 2 \\ Slabs & 12 & 0 & 12 \\ \hline Total & 321 & 94 & 415 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of the clash detection report.  
Figure 4: Snapshot of a clash produced by Autodesk Navisworks Manage 2017.
# QESIF: A Lightweight Quantum-Enhanced IoT Security Framework for Smart Cities
## 3 System Design and Components
### Quantum-Aware Intrusion Detection System (Q-IDS)  
Conventional intrusion detection systems (IDSs) in IoT environments analyze traffic features such as packet rate, size, or known attack signatures. However, in a QKD-based IoT system, threat detection must incorporate anomalies in quantum transmission behavior. To address this, a novel Q-IDS is introduced that utilizes quantum-layeranomalies, channel noise characteristics, and quantum-bit integrity to identify stealthy or quantum-enabled attacks.  
The Q-IDS continuously monitors entropy deviation, photon loss rate, QBER fluctuations, and key generation inconsistencies across sessions. Anomalous behavior is quantified using a composite quantum anomaly score \(\mathcal{A}_{q}(t)\), defined as  
\[\mathcal{A}_{q}(t)=\omega_{1}\cdot\Delta H_{q}(t)+\omega_{2}\cdot\frac{QBER(t) }{QBER_{\text{ref}}}+\omega_{3}\cdot\Lambda_{K}(t) \tag{3}\]  
where  
* \(\Delta H_{q}(t)\) is the deviation of quantum entropy from the expected baseline: \(\Delta H_{q}(t)=|H_{q}(t)-\bar{H}_{q}|\).
* \(QBER(t)\) is the current quantum bit error rate; \(QBER_{\text{ref}}\) is a calibrated reference value from noise-free trials.
* \(\Lambda_{K}(t)\) is the temporal key drop ratio: \(\Lambda_{K}(t)=\frac{\text{Keys dropped}}{\text{Keys attempted}}\) during \([t-\Delta t,t]\).
* \(\omega_{1}\), \(\omega_{2}\), and \(\omega_{3}\) are dynamic trust-weighted coefficients satisfying \(\sum\omega_{i}=1\).  
If \(\mathcal{A}_{q}(t)\) exceeds a detection threshold \(\alpha_{\text{intrude}}\), the Q-IDS flags the session as compromised. The threshold is dynamically adjusted using a Bayesian posterior entropy update rule:  
\[\alpha_{\text{intrude}}^{t+1}=\alpha_{\text{intrude}}^{t}+\eta\cdot\left( \frac{\partial\mathcal{A}_{q}(t)}{\partial t}\right)\cdot\left(1-\mathcal{P} _{n}(t)\right) \tag{4}\]  
where  
* \(\eta\) is the learning rate for intrusion sensitivity.
* \(\mathcal{P}_{n}(t)\) is the normalized probability of benign quantum noise (learned from historical QKD baselines).  
In contrast to classical anomaly detectors, the Q-IDS integrates quantum entropy, photonic error patterns, and key failure metrics into a single detection index. It can detect eavesdropping, side-channel attacks, and quantum channel interference before cryptographic exploitation occurs. The model is repeatedly adjusted based on environmental noise levels, device type, and photon emission profiles to cater to heterogeneous smart city networks utilizing QKD-based IoT networks. Algorithm 2 provides the Q-IDS algorithm for the identification of quantum-layer intrusions based on entropy deviation and inspection of QBER.
## 2 Materials and Methods
### Removal of RhB  
In order to investigate the effects of different pyrolysis temperatures and ball-milling times on the RhB removal efficiency of the biochar samples, 50 mg samples of the biochar were mixed with 50 mL of RhB for 10 min. An RhB solution (10 \(\upmu\)M) was obtained by solubilizing RhB in deionized water. The effect of the biochar doses (10, 20, 30, 40, 50, 60, 70, 80, 90, and 100 mg) on the RhB solution (10 \(\upmu\)M) was studied using a sample with a pyrolysis temperature of 600 \({}^{\circ}\)C and a ball-milling time of 180 min. The contact reaction time was 10 min. The effect of contact reaction times (0, 4, 8, 12, 16, and 20 min) on the RhB solution (10 \(\upmu\)M) was investigated using a sample with a pyrolysis temperature of 600 \({}^{\circ}\)C and a ball-milling time of 180 min. The biochar dose was 50 mg. In all of the experiments, a mechanical shaker at room temperature was used to shake the mixtures at a constant rate of 100 rpm. After the removal test, the withdrawn samples were separated twice by centrifugation at 8000 rpm for 5 min. The final concentration of the residual RhB solution was determined using a fluorescence spectrometer (Fluormax-4; Horiba, Japan) at a maximum wavelength of 576 nm. The removal percentage (R%) was calculated as follows [31]:  
\[\mathrm{R\%}=\frac{(C_{0}-C_{e})}{C_{0}}\times 100 \tag{1}\]  
where \(C_{0}\) (mg L\({}^{-1}\)) and \(C_{\mathrm{e}}\) (mg L\({}^{-1}\)) are the initial and equilibrium RhB concentrations, respectively.
## 2 Numerical Analysis
### Mesh Independence Analysis  
The accuracy of the final CFD numerical results is greatly affected by the mesh quality (or mesh resolutions). On another aspect, the mesh resolutions significantly affect the calculation time. In general, a finer mesh may produce a better mesh quality resulting in a higher accuracy for the CFD results. However, it also always prolongs the computation time. Therefore, to ensure the accuracy of the final CFD simulation results and the reasonableness of the computation time, a mesh independence analysis was carried out. Three simulations with various mesh resolutions, including a coarse, medium, and fine mesh were performed and the simulation results were analyzed and compared. The simulations using the coarse, medium, and fine-resolution meshes took place in 24, 36, and 92 h, respectively, by using a  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & \multicolumn{2}{c}{**Description**} \\ \hline Turbulence & \multicolumn{2}{c}{k-\(\zeta\)-f} \\ \hline Combustion & \multicolumn{2}{c}{Extended coherent flame models (ECFM)} \\ \hline \multirow{2}{*}{Emissions} & Thermal NO & Extended Zeldovich mechanism \\ \cline{2-3}  & \multicolumn{1}{c}{Soot} & Kinetic soot mechanism \\ \hline \multirow{2}{*}{Pilot ignition} & Diesel mode & Auto-ignition \\ \cline{2-3}  & DF mode & Diesel-ignition-gas-engine \\ \hline \multirow{3}{*}{Pilot fuel atomizations} & Breakup & WAVE model \\ \cline{2-3}  & \multirow{2}{*}{Evaporation} & Dukowicz (Diesel mode) \\ \cline{2-3}  & & Multi-component (DF mode) \\ \hline Droplets–Walls interactions & \multicolumn{2}{c}{Walljet1} \\ \hline \hline \end{tabular}
\end{table}
Table 4: CFD models.  
32 GB RAM workstation with a 12-core Intel Xeon processor. The mesh metrics and the corresponding computation time of these three mesh resolutions are listed in Table 5.  
Figure 5 presents the final CFD numerical results for the three various mesh resolutions. As can be observed, the final results were no longer dependent on the mesh resolution. Therefore, all three of these mesh resolutions can technically be utilized for simulations, to achieve both highly accurate and mesh-independent CFD numerical results. The medium mesh, however, was selected for the simulations in this study since it not only gave accurate results but also in a more reasonable time. Additionally, it also has an appropriate resolution for good contour presentations in the post-processing step. In this study, as only the injector SA had been adjusted, all of the simulations using the medium-resolution mesh took place in approximately 36 h.
# Datamator: An Authoring Tool for Creating Datamations via Data Query Decomposition
## 7 Evaluation
### Quantitative Evaluation  
We evaluated the performance of the decomposition network based on the corpus introduced in Section 4.1. In particular, we computed the rate of exact match between the decomposition results and ground truth, and calculated the averaged SARI scores [59] of the decomposition results. The SARI score is commonly used in text simplification tasks; it evaluates the goodness of words that are added, deleted, and kept in the simplified sentences. Two baseline models introduced in [46] were used for comparison. Specifically, we employed two different baselines for our study. The first baseline, denoted as \(\mathcal{B}_{1}\), utilized a sequence-to-sequence neural network featuring a five-layer LSTM encoder and cross-attention. The second baseline, referred to as \(\mathcal{B}_{2}\), employed a distinct sequence-to-sequence model that integrated a copy mechanism [60] in order to handle queries that were previously unseen. We trained these models based on the entire corpus and tested their performance via a validation set containing 30% of data that were randomly selected from the corpus, with the data queries being replaced by similar but different questions. The evaluation results are summarized in Table 3.  
We measured the effectiveness of the calibration network using the _success rate_ and the _retain rate_. Here, the _success rate_ was defined as the percentage of the incorrect decomposition results \(S_{d}\) that could be successfully amended by the calibration network via the feedback \(S_{c}\) among all the incorrect results. The _retain rate_ was defined as the percentage of correct decomposition results that remain correct after updating the model's parameters via calibrations. We performed the experiment based on the above decomposition network \(\mathcal{D}\) and calculated the _success rate_ and the _retain rate_ based on the dataset introduced in Section 4.2. In particular, we used 80% of the data to train the calibration network and used the remaining 20% for validation. As a result, the success rate was 76.61% and the retain rate was 91.79%.
## 4 Discussion  
### Advantages of Mixed Elements over Nodal Elements  
For a given mixed nodal and edge-based element, the distribution of \(\mathbf{A}\) and \(\phi\) is shown in Figure 1, namely one \(\phi\) on each node and one \(\mathbf{A}\) on each edge. In contrast, for a given nodal element, as shown in Figure 12, there are three components of \(\mathbf{A}\) (\(Ax\), \(Ay\), \(Az\)) and one \(\phi\) at each node. It is evident that the degrees of freedom (DoF) for one nodal element is 32, while it is only 20 for one mixed nodal and edge-based cell.  
As shown in Table 2, we compared the DoF and the number of non-zero elements for the two methods with three different grid sizes. The data indicate that the mixed nodal and edge-based element method offers significant advantages, which will become even greater as the grid size increases. For 50 \(\times\) 50 \(\times\) 50 grids in the x-, y-, and z-directions, the number of non-zero elements in the mixed element method is only 72.44% that of the nodal element method.
## 1 Introduction
Additionally, some authors have reported that plasmid stability is dependent on the nutritional composition, with complex nitrogen sources such as yeast extract improving plasmid stability [40; 45]. Thus, it is essential to carefully select the composition of the growth medium to avoid substrate inhibition and/or the formation of inhibiting by-products [46]. This study aims to evaluate the combined effect of different surface materials and nutrient media on biofilm formation and eGFP production by _E. coli_ JM109 (DE3) sessile cells transformed with the pFM23 plasmid for eGFP expression. For that, the different combinations of surface materials (stainless steel (SST), polyvinyl chloride (PVC), and silicone rubber (SIL)) and culture media (Lysogeny broth (LB), Terrific broth (TB), and M9 ZB medium) were tested in agitated 12-well microtiter plates for 9 days. Biofilm formation, eGFP expression, and plasmid stability were monitored every two days by epifluorescence microscopy and plate counting, fluorimetry, and high-performance liquid chromatography (HPLC), respectively, while biofilm structure was assessed by confocal laser scanning microscopy (CLSM) on day 9. Previous studies have addressed the influence of different culture media on biofilm formation and RP expression [17; 24]; however, this is the first report evaluating the effect of surface properties and nutrient medium composition on both processes. This study's hypothesis was that improving crucial operational parameters, such as surface material and medium composition, could boost the efficiency of producing recombinant proteins and other high-value compounds in large-scale biofilm systems for potential industrial use.
# Intelligent Transportation System Technologies, Challenges and Security
## 2 Intelligent Transportation Systems
### Intelligent Transportation Systems Advantages  
ITS, especially in smart cities, integrates systems, bringing many conveniences. These conveniences add comfort features to the life of society. In addition, the advantages provided are spreading to all smart city systems. The benefits of ITS applications, which have become widespread with the developing technology, are detailed below.  
* Low CO2 mission,
* Reduced traffic accident,
* Improving the quality of life of people,
* Use of roads following the capacity,
* Reduced transportation time,
* Creation of infrastructure for increasing mobility,
* Reduced traffic jams,
* Elimination of infrastructure problems,
* Development of environmentally friendly systems,
* Increasing the orientation to public transport,
* Improving driver safety and accident management,
* Determination of traffic density,
* Data integration of density,
* Data integration between different systems,
* Superiority in transit, and
* Ensuring improved traffic flow and safety.
## Non-Isoprenoid Secondary Metabolite Production from Methane Using Methanotrophs  
### Vitamin B2 (Riboflavin)  
Riboflavin, a water-soluble vitamin, is biosynthesized by various microorganisms and most plants. However, it is not biosynthesized in vertebrates, including humans, and should be replenished by external food. It is a precursor to flavin adenine dinucleotide (FAD) and flavin mononucleotide (FMN), the coenzyme necessary for the oxidation-reduction in all organisms and an essential nutrient for humans [59]. Riboflavin is produced in an annual capacity of 9000 tons and can be produced by chemical synthesis and biological synthesis. The chemical synthesis method is accomplished through a complicated process from glucose via ribose, rivamine, etc. Also, the production cost is high since the ribose is used as a precursor. Biological synthesis methods have been developed to replace the chemical method. The biological synthesis method involving the isolation of the microorganisms that naturally produce riboflavin is promising. There is also a method of producing riboflavin that involves improving the microorganism by a genetic engineering method or a chemical-physical method. The microorganisms that produce riboflavin include yeasts belonging to the genus _Saccharomyces_ sp. and _Candida_ sp., bacteria belonging to the genus _Clostridium_, _Bacillus_, and _Corynebacterium_, and fungi belonging to the genus _Eremothecium_ and _Ashbya_[59; 60].  
Methanotrophs also biosynthesize riboflavin and release it in the form of riboflavin and flavin mononucleotide (FMN) (Figure 3). Both Type I and II methanotrophs can release the flavin to the medium. _Methylocystis_ sp. strain M releases the flavin to the medium at 0.05 uM in the exponential phase, 0.056 uM in the late exponential phase, and 0.102 uM in the stationary phase. In the case of iron deficiency, up to 0.145 uM of flavin is released into the medium (Table 2). It is predicted that the flavin mononucleotide (FMN) will chelate the iron and act as an iron shuttle for cell use and respiration [48].  
Figure 3: Biosynthetic pathway of riboflavin and ectoine in methanotrophs.
# FirmHunter: State-Aware and Introspection-Driven Grey-Box Fuzzing towards IoT Firmware
## 3 Our Approaches
### Introspection-Driven Scheduling  
In order to detect vulnerabilities faster, we need to give priority to those valuable test cases, which can generate valuable inputs. In order to rank the queues after the construction of message-state queues mentioned above, we implement a scheduler to score these queues.  
We send a message-state queue to the target emulated device, wait for its execution, collecting execution information using system introspection, and score them using the collected information. The system introspection seeks two kinds of execution information: coarse-grained system calls and fine-grained coverage.  
For system calls, the six system calls listed in Table 1 are preferred for analysis for the following reasons. When valuable inputs, e.g., a series of network messages causing buffer-overflow are sent to the target device, this device will parse these messages and invoke several processes to handle them. Then some operations, like file creations, file deletions, binary executions, memory comparisons, and memory copies, are carried out. Thus, test cases triggering more system calls in Table 1 are more valuable. To capture the system calls triggered by a test case, we modify the Linux kernel to apply the kernel dynamic probes (kprobes) framework, which can intercept the system calls of the kernel.  
For coverage, the more program logics are executed by a test case, the more likely this test case is to contain vulnerabilities. Therefore test cases improving more program coverage are more valuable. In order to obtain coverage information, we implement a QEMU plugin to collect the ids of executed basic blocks (BBs).  
For each message queue \(q_{i}\) in \(\{q_{1},q_{2},\ldots,q_{n}\}\), we send it to the emulated device, wait for several seconds and count the triggered system calls \(S(q_{i})\) and the ids of executed BBs \(C(q_{i})\). After sending all of the message queues we can score each queue using the following algorithm:  
\[Score=C(q_{i})^{k} \tag{1}\]  
\[k=\frac{S(q_{i})-minS}{maxS-minS} \tag{2}\]  
where \(minS\) selects the minimum in \(S(q_{1})\), \(S(q_{2})\),..., \(S(q_{n})\) and \(maxS\) selects the maximum.
# Classification Performance of Thresholding Methods in the Mahalanobis-Taguchi System
## 5 Results and Discussion
### Optimum Thresholds  
With the optimized variables obtained via the MTS, the optimum threshold value (MDT) for each optimized dataset was computed using the four threshold methods mentioned in Section 3 previously. Table 8 tabulates the threshold values (MDT) suggested by each method of which the cut-off value to classify the testing samples (either normal or abnormal) in the testing sets will be used. Note that, the optimum \(\lambda_{opt}\) and the MDT in Box-Cox transformed terms are also included in the table since they are part of the items required in obtaining the optimum threshold values via Box-Cox transformation process. In this study, an MD value of a testing sample having less than or equal to MDT is denoted as normal, otherwise, it is considered abnormal.