esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Cross-Reactivity of the MIPs in Water Samples  
The cross-reactivity of MIPs prepared for PG (MIPs-PG) and BPA (MIPs-BPA) was tested against structurally similar compounds and other endocrine disruptors rather than target molecules. To test  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**298 K**} & \multicolumn{2}{c}{**303 K**} & \multicolumn{2}{c}{**308 K**} & \multicolumn{2}{c}{**313 K**} \\ \hline
**Polymer** & **Qmax (mg/g)** & **b (L/mg)** & **Qmax (mg/g)** & **b (L/mg)** & **Qmax (mg/g)** & **b (L/mg)** & **Qmax (mg/g)** & **b (L/mg)** \\ \hline MIP 2 & 0.636 & 2.167 & 0.462 & 1.046 & 1.618 & 10.72  
the cross-reactivity of MIPs-PG, diosgenin was used as a structural analogue and BPA was used as an endocrine disruptor. In the case of MIPs-BPA, diphenylamine was used as a structurally similar compound and PG was used as an endocrine disruptor. All experiments were performed in triplicate. The cross-reactivity of MIPs was analysed in ultrapure water against one analyte at a time, at two different concentrations (1 mg/L and 3 mg/L). In all cases, MIPs showed no significant cross-reactivity with a rather high recovery of the tested analytes, more than 85%. The lowest cross-reactivity was observed for MIPs-PG tested against BPA (with a recovery of 88.2 \(\pm\) 2.3%) and MIPs-BPA tested against PG (87.2 \(\pm\) 5.7%). Additionally, the slightly lower recovery of 85.5 \(\pm\) 4.2% (MIPs-PG against diosgenin) and 82.9 \(\pm\) 3.5% (MIPs-BPA against diphenylamine) suggested a higher cross-reactivity in the presence of structural analogue molecules. This certain degree of cross-reactivity can be related to the fact that MIPs do not generally show absolute specificity for target molecules and can also exhibit certain interactions with other structurally related molecules or cross-reactants with a similar distribution of functional groups [47].
# Repellency of Veratraldehyde (3,4-Dimethoxy Benzaldehyde) against Mosquito Females and Tick Nymphs
## 4 Discussion
In this study, there was a difference in the repellency and repellency duration of veratraldehyde and formulations according to application dose and tested arthropod species. Veratraldehyde and its pump spray formulations exerted better repellency and complete protection time to the mosquito females than to the tick (_H. longicornis_) nymphs. In earlier studies, the improved duration of repellent agents (DEET or plant essential oils) against mosquitoes might be attributed to the lower evaporation rate and better skin persistence [31; 36]. According to the pharmacokinetic results in our study, it was predicted that the veratraldehyde was not absorbed into dermal tissues via skin, or even if it was absorbed, its amount was very small. In a previous report, the veratraldehyde was not detected in the blood when applied on the skin of rats, whereas it was detected in the blood when administered orally [29]. However, veratric acid, a metabolite of veratraldehyde, was detected for up to 24 h after skin administration, although the amount was very small compared to oral administration [29]. This suggests that the main natural loss route for veratraldehyde applied on the skin is inherent evaporation, and if a small amount is absorbed through the skin, it can be converted immediately into metabolites such as veratric acid. Most insect repellents are categorized as vapor or olfactory repellents because they are active in the vapor phase and have an impact on the olfaction of target arthropods, preventing the insects from locating a treated area [39]. Thus, insect repellents must have a minimal vapor pressure to be effective, and repellents with a high vapor pressure will have a repellent effect at low concentrations, but lower volatility of a repellent will result in protection for longer time periods [40]. Veratraldehyde has a low vapor pressure, which is lower (0.0009 hPa at 24 \({}^{\circ}\)C) than that of the above-described repellent agents: DEET 0.0024 hPa at 25\({}^{\circ}\)C, IR3535 0.0015 hPa at 20 \({}^{\circ}\)C and citronella oil 0.22 hPa at 25 \({}^{\circ}\)C [41]. Although the mode of action of veratraldehyde is not known, its repellency against mosquitoes and ticks may be due to both its low volatility and its low skin absorption.
## 3 Materials and Methods
### GC-MS Headspace-Solid Phase Micro Extraction (HS-SPME)  
Gas chromatography-mass spectrometry (GC-MS) was used to investigate the two fractions obtained--the polar (amino and organic acids, carbohydrates) and non-polar (saturated and unsaturated fatty acids) fractions.  
Briefly, 50.0 mg lyophilized peach material from each sample was subjected to the procedure described by [PERSON] et al. [55]. The GC-MS analysis was carried out using a 7890A gas chromatograph (Agilent, Santa Clara, CA, USA) coupled to a 5975C mass-selective detector (Agilent) and an HP-5 ms silica-fused capillary column coated with a 0.25 \(\upmu\)m film of poly(dimethylsiloxane) as the stationary phase (Agilent), with dimensions of 30 m \(\times\) 0.25 mm (i.d.). The oven temperature program used was as follows: initial temperature 100 \({}^{\circ}\)C for 2 min, then 15 \({}^{\circ}\)C/min to 180 \({}^{\circ}\)C for 2 min, then 5 \({}^{\circ}\)C/min to 300 \({}^{\circ}\)C for 10 min, with a run time of 42 min. The flow rate of the carrier gas (helium) was maintained at 1.2 mL/min. The injector and the transfer line temperature were kept at  
Figure 3: Clustering results for eight peach cultivars, shown as a heatmap: (**A**)—WF, (**B**)—FP. The values were normalized by log\({}_{10}\) transformation.  
250 \({}^{\circ}\)C, with EI energy at 70 eV and a mass range of 50 to 550 m/z at 1.0 s/decade. The temperature of the MS source was 230 \({}^{\circ}\)C. The injections were carried out in a split mode at a 10:1 ratio; the injection volume was 1 \(\mu\)L.  
For the purposes of headspace sampling, 2 cm of SPME fiber assembly divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS, Supelco, Bellefonte, PA, USA) was used. The HS-SPME technique was used for the analyses of the peach volatile compounds according to the procedure described by [PERSON] et al. [56].  
An Agilent 7890A GC unit coupled to an Agilent 5975C MSD and a DB-5 ms (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m) column were used to analyze the volatile compounds in all investigated samples. The oven temperature program was as follows: from 40 \({}^{\circ}\)C (hold 1 min) to 250 \({}^{\circ}\)C (hold 5 min) at 2 \({}^{\circ}\)C /min; carrier gas: helium with a flow rate of 1.2 mL/min; transfer line temperature: 270 \({}^{\circ}\)C; ion source temperature: 200 \({}^{\circ}\)C, EI energy: 70 eV; and mass range: 50 to 550 m/z at 1.0 s/decade.  
The AMDIS software, version 2.64 (Automated Mass Spectral Deconvolution and Identification System, NIST, Gaithersburg, MD, USA), aided in the reading of the obtained mass spectra and the identification of the metabolites. AMDIS recorded the RIs of the compounds with a standard n-hydrocarbon calibration mixture (C8-C36, Restek, Teknokroma, Barcelona, Spain). For identification, the separated compounds were compared to their GC-MS spectra and Kovats retention indexes (RI) using reference compounds in the NIST'08 database (NIST Mass Spectral Database, PC-Version 5.0, 2008 from National Institute of Standards and Technology, Gaithersburg, MD, USA).
# Evaluation of 5G Positioning Performance Based on UTDoA, AoA and Base-Station Selective Exclusion
## 4 Simulation Results and Evaluation
### Scenario Definition  
The scenario considered in this work is based on the simulation methodology for outdoor environments within the 5G positioning study in 3 GPP Release 16 [7]. It is a predefined Urban Macro-cell (UMa) and Urban Micro-cell (UMi) network scenario based on a deep urban deployment map with BSs deployed above the building rooftops, i.e., BS heights between 20 and 40 m. The deployment consists of a hexagonal grid with seven macro sites, where each has three sectors with an Inter-Site Distance (ISD) around 1732 m in rural areas, 500 m and 200 m in dense urban areas. The method was tested through simulations based on a deep urban deployment map, which comes with an experimental data file of the user's position. The UEs are randomly placed within the coverage area of the predefined deployment scenario. For reference, see Figure 3. The deep urban area is widely characterized with 75% of the position, while the low density scenario is only shown in 25% of the position. The distance between the BS and UE is used to determine the propagation conditions based on the distance-dependent LoS probability and path-loss models. The multipath channel is stochastically generated following the tabulated channel parameters in 3 GPP standard. For each receiver position, the positioning performance is evaluated over 96 iterations of Monte Carlo. The ranging observables are calculated for specific propagation conditions, channel model, system bandwidth (BW), SNR levels and time-delay estimator, and the angle observables are calculated with mathematical models for specific system BW, SNR levels and antenna elements. A 5G PRS and SRS signal with a BW of 20 MHz and 50 MHz at a carrier frequency of 4 GHz, within the cmWave Frequency Range 1 (FR1) of 5G NR and antenna arrays of \(\mathrm{M}=\mathrm{N}=11\) elements have been considered. The orientation of the BS arrays is set to three typical sectors \((0^{\circ},120^{\circ},240^{\circ})\). The network is supposed to have synchronization error among BSs. Table 1 summarizes the simulation parameters used in this work.
## 3 Preliminaries
### Explicit Time Stability Theory
 \tag{35}\]  
where \(G(\sigma_{e})=(1-\sigma_{e}^{\text{T}}\sigma_{e})\mathbf{E}_{3}+2\sigma_{e}^{ \times}+2\sigma_{e}\sigma_{e}^{\text{T}}\), where \({}^{\times}\) represents the cross product operation, and \(\mathbf{R}_{E}\in\mathbf{R}^{3\times 3}\) is the rotation matrix representing the orientation of the end effector coordinate system \(\sum_{E}\) relative to the inertial coordinate system \(\sum_{I}\)[15]; the expression for \(\mathbf{J}_{ed}\) is similar. The position error of the end effector is given by  
\[\mathbf{e}_{e}=\mathbf{p}_{e}-\mathbf{p}_{ed} \tag{36}\]  
where \(\mathbf{p}_{e}\) is the actual position of the end effector and \(\mathbf{p}_{ed}\) is the desired position. Taking the derivative of \(\mathbf{e}_{e}\) with respect to time, it follows that  
\[\mathbf{\dot{e}}_{e}=\mathbf{v}_{e}-\mathbf{v}_{ed} \tag{37}\]  
The attitude error of the end effector can be represented using unit quaternions as  
\[ \begin{split}\mathbf{q}_{ee}&=\begin{bmatrix}q_{e0}&q_{e1}&q_{e2}&q_{e3} \end{bmatrix}^{\text{T}}\\ &=\begin{bmatrix}q_{e0}&\mathbf{q}_{ee}^{\text{T}}\end{bmatrix}^{\text{T}}\\ &=\mathbf{q}_{ed}^{-1}\mathbf{q}_{e}\end{split}  \tag{38}\]  
where \(\mathbf{q}_{e}\) is the actual quaternion representing the attitude of the end effector, \(\mathbf{q}_{ed}\) is the quaternion representing the desired attitude, \(q_{e0}\) is the scalar part of the quaternion, and \(\mathbf{q}_{e0}\) is the vector part of the quaternion. The attitude error can be expressed using MRPs as  
\[\mathbf{\sigma}_{e}=\frac{\mathbf{q}_{e0}}{1+q_{e0}} \tag{39}\]Both MRPs vectors represent the same attitude error. The kinematics of the relative attitude error can be expressed as  
\[\dot{\mathbf{\sigma}}_{e} =\frac{1}{4}G(\mathbf{\sigma}_{e})\mathbf{\epsilon}_{\omega}^{E} \tag{40}\] \[=\frac{1}{4}G(\mathbf{\sigma}_{e})(\mathbf{\omega}_{e}^{E}-\mathbf{\omega}_{ed} ^{E})\] \[=\frac{1}{4}G(\mathbf{\sigma}_{e})\mathbf{R}_{E}^{\mathsf{T}}(\mathbf{\omega}_ {e}-\mathbf{\omega}_{ed})\]  
where the superscript \({}^{E}\) indicates that the quantities are expressed in the end effector coordinate system \(\sum_{E}\). Substituting (33) into (34), the general form of the pose error kinematics can be obtained as  
\[\dot{\mathbf{e}}_{p}=\mathbb{J}\mathbf{\dot{q}}+\mathbf{\xi} \tag{41}\]  
where \(\mathbb{J}=J_{e}J_{g}\mathbf{\xi}=\mathbf{J}_{e}J_{0}\mathbf{H}_{0}^{-1}\mathbb{C}-J_{ed} \mathbf{V}_{ed}\).
# Finite-Key Analysis of 1-Decoy Method Quantum Key Distribution with Intensity Fluctuation
Most importantly, for the 1-decoy state method, the security bound with a small data size is more sensitive to intensity fluctuations than that with a large data size. This paper is organized as follows. Section 2 conducts a finite-key analysis for the 1-decoy state method with intensity fluctuations and dependent events. In Section 3, the simulation results are shown, and a conclusion is obtained in Section 4.
# AUTOSAR Runnable Periods Optimization for DAG-Based Complex Automobile Applications
## 3 Background and Problem Description
### Problem Description  
With the system and control performance models and the schedulability constraint presented above, our problem can be defined as follows: With a given AUTOSAR control system composed of DAG-structured runnables \(\{r_{1},r_{2},\cdots,r_{n}\}\) and a linear control cost function \(J(T,\Delta)\) regarding the control target plant, find the optimal runnable periods \((p_{1},p_{2},\cdots,p_{n})\) that minimize the control cost while satisfying the system's schedulability constraint. More formally, our problem is as follows:  
\[\begin{split}\underset{p_{1},p_{2},\cdots,p_{n}}{\text{minimize}}& J(T,\Delta)\\ \text{subject to}& U(p_{1},p_{1},\cdots,p_{n})\leq 1. \end{split} \tag{12}\]  
In this paper, we try to find the theoretically optimal real numbered runnable periods, without explicitly considering neither the grouping of runnables into predefined periodic tasks nor the scheduling granularity (e.g., integer constraints) of a specific RTOS. However, our solution can be used as a baseline foundation for further practical applications after considering the implementation details imposed by a specific RTOS.
# Steel Surface Defect Detection Based on YOLOv8-TLC
## 5 Conclusions  
To address the low recognition rate and efficiency of traditional image processing algorithms in detecting surface defects on steel, this paper proposes the YOLOv8-TLC algorithm. By introducing detection heads of different sizes, the algorithm reduces missed detections, while incorporating the LSK-Attention mechanism enhances the network's ability to recognize surface defects on steel. Additionally, the TSP module improves the extraction of redundant features, and the C2f-DS module replaces the traditional C2f module, accelerating the model's detection speed. Testing results on the NEU-DET dataset show that YOLOv8-TLC not only surpasses YOLOv8n in detection accuracy but also significantly improves detection speed. The algorithm meets industrial production requirements for high detection accuracy and real-time performance. However, the detection performance for crack-like defects with similar background colors and textures is still not ideal. Although this paper has made some improvements in this area, the issue remains unresolved. Future research will focus on enhancing crack defect detection and further lightening the model.  
Writing--review and editing, H.C.; supervision, C.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Liaoning Provincial Department of Education's General Fund Project (LJKZ1184) under the number 296.  
Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: [[https://drive.google.com/file/d/1](https://drive.google.com/file/d/1) qrdZlaDi272 eA79b0 uCwwqPrm2Q_WI3k/view]([https://drive.google.com/file/d/1](https://drive.google.com/file/d/1) qrdZlaDi272 eA79b0 uCwwqPrm2Q_WI3k/view) (accessed on 8 September 2021).  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**AP/\%**} & \multirow{2}{*}{**mAP/\%**} \\ \cline{2-2} \cline{4-7}  & **Cr** & **In** & **Pa** & **Ps** & **Rs** & **Sc** \\ \hline YOLOv3 & 45.0 & 77.0 & 93.6 & 71.7 & 73.9 & 86.9 & 74.7 \\ YOLOv5n & 37.8 & 79.5 & 95.6 & 77.6 & 78.6 & 88.2 & 76.2 \\ YOLOv5s & 38.2 & 79.9 & 95.8 & 79.5 & 76.3 & 89.6 & 76.6 \\ YOLOv6n & 42.0 & 76.6 & 96.2 & 77.1 & 73.5 & 92.4 & 76.3 \\ YOLOv6s & 44.2 & 76.6 & 93.8 & 76.3 & 78.7 & 92.6 & 77.0 \\ Algorithm1 [24] & 34.5 & 76.8 & 89.8 & 82.5 & 61.6 & 86.8 & 72.0 \\ Algorithm2 [30] & 43.4 & **89.3** & 84.3 & 76.6 & 74.2 & 95.3 & 77.2 \\ YOLOv8n & 42.7 & 80.3 & 90.3 & **93.7** & 65.4 & 90.6 & 77.1 \\ YOLOv9s & 49.1 & 77.9 & 95.8 & 74.4 & 77.0 & 87.5 & 77.0 \\ YOLOv10n & 42.4 & 78.4 & 93.5 & 75.0 & 77.4 & 88.6 & 75.9 \\ YOLOv11n & 44.2 & 76.3 & 95.9 & 75.7 & **81.6** & 90.6 & 77.4 \\
**YOLOv8-TLC (ours)** & **50.1** & 82.6 & 93.4 & 84.8 & 72.3 & **95.4** & **79.8** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison experiments of detection algorithms.
# Droop Control Strategy of Utility-Scale Photovoltaic Systems Using Adaptive Dead Band
## 4 Conclusions  
This paper has analyzed a novel droop control to operate transmission system with utility-scale PV more flexibly. The adaptive dead band scheme, which operates by changing the dead band for each time period, addresses the voltage problem of the system, including RESs. Furthermore, the proposed method was simulated and analyzed for the historical daily load curve, daily PV generation, and disturbance on the bus to which the RESs were connected.  
In the proposed method, various schemes used in the centralized and decentralized methods were used. The system topology and the result data of the power flow were used as the input data, and the voltage sensitivity method was used to derive a formula for calculating the required reactive power output according to the voltage change. The voltage change and reactive power output were expressed as a three-dimensional graph over time through the characteristic curve equation of the reactive power-voltage control scheme and the required reactive power data. A TSO was used to calculate the voltage change in the bus for two disturbances and transmit the appropriate dead band value for each time period to the plant controller through the values used in the proposed method. The simulation results showed that operating the system through an adaptive dead band rather than using a fixed dead band enables flexible system operation against disturbance.  
The main idea was proposed by W.K. and G.J.; the experimental results were collected and analyzed by W.K. and S.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This work was supported by Korea Institute of Energy Technology Evaluation and Planning(KETEP) grant funded by the Korea government(MOTIE) (No. 20191210301890) and \"Human Resources Program in Energy Technology\" of the Korea Institute of Energy Technology Evaluation and Planning (KETEP), granted financial resource from the Ministry of Trade, Industry and Energy, Korea. (No.20194030202420).  
The authors declare no conflict of interest.  
Figure 14: Net load curve and dead band result by time.
# Machine Learning Techniques to Detect a DDoS Attack in SDN: A Systematic Review
## 5 Available Benchmarked DDoS Datasets and Classes of Attacks in Datasets
The performance indicators are the most popular measures defined in this section. For binary classification, the typical performance measurements are accuracy, recall, precision, F1-score, AUC, etc. The confusion matrix is described as the overview of outcomes foreseen by the categorization model. It includes (True Positive TP), True Negative (TN), False Positive (FP), and False Negative (FN) [89]. The true positive rate (TPR) is determined following Equation (1) Additionally, it may be known as the recall or sensitivity [90], and ought to be as high as possible. \[\text{TPR}=\frac{TP}{(TP+FN)} \tag{1}\]  
Precision is determined following Equation (2) by checking how many of the positive classes that the model adequately predicted are really positive [91]. \[\text{Precision}=\frac{TP}{(TP+FP)} \tag{2}\]  
Following Equation (3), accuracy is defined as the percentage of true predictions made by the model across all classes. The highest level is preferable. Its formula is as follows [91]:  
\[\text{Accuracy}=\frac{TP+TN}{(Total)} \tag{3}\]  
The FPR or False Positive Rate is shown in Equation (4) [90]; it measures the percentage of negative occurrences that the model incorrectly forecast as positive. \[\text{FPR}=\frac{FP}{(TN+FP)} \tag{4}\]  
The percentage of positive cases incorrectly anticipated as negative cases is known as the false negative rate (FNR), and is determined as shown in Equation (5) [90]  
\[\text{FNR}=\frac{FN}{(TP+FN)} \tag{5}\]  
The TNR or True Negative Rate is shown in Equation (6); Specificity is another name for it. It is described as the percentage of adverse events accurately foreseen as adverse [90]. \[\text{TNR}=\frac{TN}{(TN+FP)} \tag{6}\]  
It is challenging to compare two models if one has great recall and low accuracy or vice versa. The F1-score is therefore used to compare them. It is employed to simultaneously assess memory and precision [92]. Equation (7) is used to compute the F1-score:  
\[\text{F1-Score}=\frac{2*Recall*Precision}{(Recall+Precision)} \tag{7}\]  
Efficiency at different threshold levels for classification problems is known as the AUC-ROC curve. A model makes more accurate predictions if the AUC is near 1 [93].
## 4 Experiments
### Data Normalization  
From each frame in a video, 2D image-plane coordinates of 25 keypoints with the confidence of individual keypoints were extracted by OpenPose toolkit. From each detected person in a frame the given points were the \(x,y\) coordinates, in pixels, of the centers of the torso, nose, and pelvis, and centers of the left and right shoulders, elbows, hands, hips, knees, ankles, heels, first and fifth toes, ears, and eyes [12]. The toolkit missed detecting few people from the frame. We removed 1443 such cases from using for training. For some cases only few of the skeleton joints are missed. For such cases, we used linear interpolation to fill the missed points.  
Some of the input data were noisy, so they might not give an expected result. For example, the x-coordinate of the left ear and few other time-series data were noisy and contributed undesired results. To mitigate the effects of these noisy data, we normalized the image-plane coordinates of knees, ankles, hips, big toes, projected angles of the ankle and knee flexion, the distance between the first toe and ankle, and the distance between the left ankle and right ankle [13]. In addition, using window slicing more time-series data were generated, and using an augmented dataset enabled avoiding variation in each starting frame.