esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Antifungal Mechanism
### Raman Spectroscopy Analysis
#### 4.2.1 Raman Spectroscopy Analysis of PEI-AuNP@Van and Voriconazole- Treated _A. flavus_ Conidia
flavus_ conidia for 8 h. When compared to the spectrum of the voriconazole-treated conidia (Figure 8(aii)), it was observed that some spectral peaks were slightly shifted towards both lower and higher wave numbers. This shift can be attributed to changes in the chemical bond length. Shifting to a higher wave number denotes a reduction in the chemical bond length and vice versa. Based on the observed spectral bands, the assignments are as follows: the band at 340 cm\({}^{-1}\) in the voriconazole-treated conidia was shifted to 343 cm\({}^{-1}\) in PEI-AuNP@Van treated conidia, indicating a shortening of the OH bond in chitin. Similarly, the peak at 412 cm\({}^{-1}\) shifted slightly to 414 cm\({}^{-1}\), indicating significant structural changes in cell wall carbohydrates. The peak at 481 cm\({}^{-1}\) shifted to 488 cm\({}^{-1}\); this peak can be assigned to the in-plane bending vibration of CO-NH/C-CH\({}_{3}\) in galactomannan and chitin. Again, the peak at 534 cm\({}^{-1}\) shifted to 537 cm\({}^{-1}\); this result can be assigned to glucofuranose ring deformation. A new Raman peak at 588 cm\({}^{-1}\), which was observed in the PEI-AuNP@Van-treated conidia, was tentatively assigned to the in-plane bending of CCO in the fructosyl ring. Some typical peaks (734, 810, and 1040 cm\({}^{-1}\)) were observed in the spectra of the voriconazole- and PEI-AuNP@Van-treated conidia (Figure 8(aii,aiii)). A weak peak at 841 cm\({}^{-1}\), which was linked to an intense peak at 810 cm\({}^{-1}\), can be assigned to CO/CC/CH deformation, suggesting the exposure of DNA/RNA/phospholipids. Furthermore, Raman shifts were observed at 910, 977, 1133, 1207, 1312, 1450, and 1598 cm\({}^{-1}\) (as shown in Figure 8(aii,aiii)). The above-explained Raman spectrum suggests a similar mode of action for PEI-AuNP@Van and voriconazole against conidia. However, detailed molecular mechanisms remain unknown and are currently under investigation.
## 5 Experiments
### Efficiency Test  
An efficient binary clone detection tool can help analysts accelerate the process of binary similarity analysis. In this experiment, we compared GeneDiff with the control group (see Section 5.2) in terms of detection efficiency.  
We compiled _binutils-2.32_ and _OpenSSL-1.0.1f_ into binaries of different ISAs (i.e., AMD64, AArch64, and MIPS64). Then, these binaries were divided into several file pairs according to their file names. We used GeneDiff and the control group to detect file pairs and logged the time taken to detect each function pair. We found that GeneDiff generally completed the task in much less time than did the control group. Figure 9 shows the experimental results of two approaches for detecting the sysinfo file in binutils-2.32 and the libssl file in OpenSSL-1.0.1f; the red line denotes GeneDiff. There are a total of 137 function pairs in the sysinfo file. GeneDiff successfully found 129 of them in 33.02 s, whereas the control group found 93 pairs in 177.69 s. Since the two approaches preprocess the target binaries via IDA Pro [29], the function pairs are not found in either of the approaches in Figure 9 during the initial time period. However, GeneDiff's preprocessing time was shorter than that of the control group, and its detection efficiency was much higher than that of the control group. This is because GeneDiff only converts bytecode into VEX instructions and trims out redundant data during preprocessing, which makes the implementation faster. GeneDiff leverages the lexical semantics based on the intermediate representation rather than on symbolic execution: the former is more scalable and less vulnerable to adverse effects from the differences in architectures and compiler optimization options.  
Figure 8: The receiver operating characteristic (ROC) evaluation results based on the test datasets, where the \(x\)-axis is the true positive rate and the \(y\)-axis is the false positive rate. (**a**) Same instruction set architecture (ISA), cross opts; (**b**) cross ISAs, O0; (**c**) cross ISAs, O1; (**d**) cross ISAs, O2; (**e**) cross ISAs, O3; (**f**) cross ISAs, cross opts.
## 2 Islanding Detection Methods
### Computational Intelligent Based Methods
#### 2.6.6 Support Vector Machine Method (SVM)  
By establishing a decision boundary to divide the necessary training data, the signal and structure are assessed using this method. To ascertain the typical functions of the recorded PCC voltage or current signals, SVM is paired with auto-aggressive processing. SVM has a quick detection rate and good accuracy. However, because of the data training and the method, it is fairly complicated for practical implementation [47; 48].
## 2 Materials and Methods
### Equipment Instrumental Analysis
#### 2.4.5 Liquid Chromatography Coupled to Ion Trap Mass Spectrometry (LC-MS(tm))  
The migration extracts were analyzed by an LC-MS(tm) system that comprised an Agilent Technologies 1200 Series HPLC system (Waldbronn, Germany) equipped with a quaternary pump, a degassing device, an autosampler, a column thermostat system, and a diode array detector (DAD), all of them controlled by the ChemStation for LC 3D systems software, along with the Agilent 6330 Ion Trap mass spectrometry detector controlled by the 6300 Series TrapControl version 6.1 software.  
The chromatographic separation was achieved on a Gemini C18 110A (150 mm \(\times\) 3 mm internal diameter, 5 \(\upmu\)m particle size) column with a pre-column from Phenomenex(r) (Torrance, CA, USA). Mobile phases were 0.1% formic acid in ACN and 0.1% formic acid in water. A gradient elution method was applied, starting at 20% can, and this percentage gradually increased, reaching 50% at minute 25; this composition was held constant until minute 45, then the can concentration was linearly increased, reaching 100% at 60 min, and was held constant until minute 70. The column was kept at 35 \({}^{\circ}\)C and the autosampler was maintained at ambient temperature. The flow rate remained constant at 0.4 mL/min, and the injection volume was 10 \(\mu\)L.  
The conditions of the ion trap mass spectrometer for the identification of unknown polyester oligomers were the following: positive electrospray ionization (ESI) mode, dry temperature was set at 350 \({}^{\circ}\)C, nitrogen was used as the nebulizer gas at a pressure of 15 psi, dry gas at 10 L/min, HV capillary at 3000 V, MS data were acquired in full-scan mode using two ranges (100 to 500 \(m/z\) and 500 to 1000 \(m/z\)), averages of 5 spectra, maximum accumulation time of 200 ms. The fragmentation was carried out in SmartFrag mode with a ramped collision energy applied over a range of 0.3 to 2.0 V. Helium was used as collision gas. The fragmentation spectra were obtained, and the proposed structures were checked through ACD/MS Fragmenter (v. 11.03) software.  
In addition, a targeted analysis was carried out in MS(n) in negative ESI mode for the simultaneous determination of five polyvalent carboxylic acid monomers, including phthalic acid (PA), isophthal acid (IPA), terephthalatic acid (TPA), adipic acid (AA), and trimellitic acid (TMA). The \(m/z\) values selected were \(m/z\) 164.8 for PA, IPA and TPA, \(m/z\) 145.0 for AA, and \(m/z\) 208.8 for TMA.
## 4 Diesel Power System
### Diesel Generator Automatic Control System
#### 4.2.3 Architecture and Hardware  
The stability and reliability of control is the most important index in a long-term unattended harsh environment, so the selection of system architecture and equipment needs special consideration and design.  
Redundant PLC control technology has showed high reliability in the industrial control area, such as the AB ControlLogix PLC redundant control schemes used in extremely cold regions of Russia. In addition, the AB 1794 series I/O modules can work reliably in low-temperature environments as well. Thus, in our project, the AB related products are adopted to realize the SMC control scheme and the field bus technology solutions are used to obtain control effect.  
In the hardware structure, the main control equipment, the special control communication network, and the flex I/O sites are all configured to redundant mode (as is shown in Figure 3).  
Two redundant main controllers of the ControlLogix 5561 low-temperature-type PLC are settled in the instrument module cabin. One is the principal PLC and the other is the subordinate one.  
If the principal PLC breaks down, the subordinate PLC should take over all the work and become the current principal PLC in a very short time (less than 100 ms). The main controller refers to the principal PLC. Some other significant devices are close to the main controller, as follows. The redundant 1757-SRM is the frame of the fiber optic cable communication module between the redundant PLCs, whose CPU is L61. The redundant 1756-EWEB is the Ethernet connection module and 1756-CNBR is the control network connection modules. Through the Ethernet, the main controller PLC connects the (PC monitor) PCM's PC104 machine. The content about PC104 can be referred to in Section 6.4.  
In the power module cabin, many distributed Flex I/O sites (also called data acquisition and control box, or control box) are the wide-temperature type of product. They can work reliably in low temperature and implement the task of data acquisition and terminal control. Each Flex I/O site can communicate with the controller PLC via the help of the 1794-ACNR 15 ControlNet adapter module which connects the Flex I/O with the field control network. Each ACNR module can connect eight I/O modules at most.
# Effect of Waste Engine Oil and Waste Cooking Oil on Performance Improvement of Aged Asphalt
###### Abstract  
In order to explore the applicability of waste engine oil and waste cooking oil used in aged asphalt, the effect of waste engine oil and waste cooking oil on aged asphalt recycling was studied through the analysis of the improvement of its physical, chemical, and rheological properties. Six aged asphalt binders with different aging times were obtained by indoor test simulation using the Thin Film Oven Test at 163 \({}^{\circ}\)C. Then, waste engine oil and waste cooking oil with five different dosages were added to investigate improvement performances. The results clearly demonstrated that waste engine oil and waste cooking oil could soften and recover the work ability of aged asphalt effectively. Furthermore, the physical, chemical, and rheological performances of six aged asphalts could be improved to normal level of virgin asphalt if the content of waste engine oil or waste cooking oil was suitable. The rheological properties of aged asphalt with waste cooking oil had better improvement than that with waste engine oil. Overall, the good applicability would provide waste oil a much wider service range in asphalt pavement recycling field. It also provided a method of developing new rejuvenating agent with the two waste oils to achieve complex synergism effect. Moreover, it realized the waste cyclic utilization and environmental protection.  
Keywords: asphalt binder; aged asphalt; rejuvenating; properties improvement; waste engine oil; waste cooking oil +
Footnote †: journal: Applied Sciences
## 4 Experimental Results and Discussion
### Results of the Estimation Obtained for Constant Current and Variable Current with Aging Effect
The GRU is particularly effective for estimating the SoC prediction. Figure 24: Estimation of the SoC obtained at variable current using the DNN model with the aging effect. Figure 25: Estimation of the SoC obtained at variable current using the GRU and LSTM models with the aging effect. ing the SoC because it can retain relevant information on previous charge and discharge cycles, which is crucial for predicting the actual state of the battery. The LSTM model is particularly suited for sequential prediction tasks due to its ability to store information over long periods and forget irrelevant information. This allows the LSTM model to effectively capture dynamic effects and complex interactions in battery cycles. The models were tested under different aging and temperature conditions to simulate real-life battery use scenarios. The results indicated that the DNN and LSTM model were particularly robust in the face of these variations, thanks to their ability to model complex relationships and store relevant information over long periods. The DNN achieved the best accuracy, with a maximum error of less than 2.5%. This can be attributed to the capacity of the DNN to capture complex features and its flexibility in terms of network structure. The GRU, although slightly less accurate than the DNN, offered a good compromise between performance and computation time, thanks to its simplified structure and its ability to capture temporal dependencies. The LSTM model, while offering robust performance due to its ability to handle long-term dependencies, was slightly slower to train than the GRU due to its increased complexity.
## 6 Worst-Case Latency Analysis of the FWOS Model
### Length of the Contention-Free Interval  
Servicing the arrived traffic in each node is based on the open-window interval for the associated priority queue. Under flexible overlapping between the open windows for TT queues, each open window serves the arrived traffic according to more stringent scheduling constraints. In particular, these windows are divided into three different intervals: block, contention-based, and contention-free intervals. (i) Block intervals represent guard band intervals, where the remaining time is not enough to transmit the whole frame. (ii) Contention-based intervals represent time slots' aggregation, where the related window is overlapped with another TT window and the priority or the non-preemption technique may prevent the frame from being served. Although the targeted traffic may obtain the required service during contention-based intervals, safe latencies are formulated based only on contention-free intervals. (iii) Contention-free intervals represent the guaranteed service interval that has to be specified to determine the worst-case latency bounds. To define contention-free intervals, we have to specify all block and contention-based intervals. Block intervals are located at the end of each open window, but contention-based intervals depend on overlapping with other TT windows. Thus, the overlap must be comprehensively analyzed to specify these intervals.  
As mentioned, three overlapping aspects have to be considered: priority, position, and the OR. Accordingly, the latency of TT traffic is evaluated and discussed below,  
Figure 6: The worst-case latency bound using the network calculus method.  
under all these aspects to give a clearer view of system performance with a variety of relevant parameters.
# An Efficient Distributed Coverage Hole Detection Protocol for Wireless Sensor Networks
## 4 Proposed Hole Detection Protocol
### Determination of CIP  
It is assumed that each sensor knows its own location information. After deployment of the nodes, each sensor exchanges its location information to know the location of its one-hop sensing neighbors. For simplicity, throughout the paper, sensing neighbors are refereed to as the neighbors. Upon receiving the location information from its one-hop neighbors, each sensor has to determine its CIP set (\(P\)) and _sensing neighbors set (\(N\))_. The algorithm for calculating the CIP is given in Algorithm 1. Upon executing the CIP calculation algorithm, each node has to maintain the list of its one-hop neighbor's information and corresponding CIP, as given in Table 1. For example, the neighbors and CIP of different nodes given in Figure 2 can be shown in Table 1.  
It is to be noted that there may be some isolated sensors deployed over the monitoring region \(R\). Since the isolated sensors cannot have any sensing neighbors, they would not execute Algorithm 1, and there must be a coverage hole around that sensor. Suppose any sensor \(S_{i}\
ot\in\) isolate has to calculate its CIP. First, \(S_{i}\) chooses its one neighbor \(S_{j}\) from  
\(P(S_{1})=\{p_{1},p_{10},p_{15},p_{16}\}\). Based on Algorithm 1, after the sensor \(S_{1}\) finds its CIP, its next hop sensing neighbor \(S_{2}\) clockwise finds its one-hop sensing neighbors set, _i.e._, \(N(S_{2})=\{S_{3},S_{1}\}\), and its CIP set can be given as \(P(S_{2})=\{p_{1},p_{2},p_{16},p_{17}\}\). Thus, the process continues for all sensors, and finally, as shown in Figure 2, the set of CIP can be calculated as \(P=\{p_{1},p_{2},...,p_{10},p_{14}\sim p_{28}\}\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Node** & **Neighbors** & **Corresponding CIP** \\ \hline \multirow{3}{*}{\(S_{1}\)} & \(S_{2}\) & \(P_{1}\), \(P_{16}\) \\  & \(S_{10}\) & \(P_{10}\) \\  & \(S_{11}\) & \(P_{15}\) \\ \hline \multirow{3}{*}{\(S_{2}\)} & \(S_{1}\) & \(P_{1}\), \(P_{16}\) \\  & \(S_{3}\) & \(P_{2}\), \(P_{17}\) \\ \hline \multirow{3}{*}{\(S_{3}\)} & \(S_{2}\) & \(P_{2}\), \(P_{17}\) \\  & \(S_{4}\) & \(P_{3}\), \(P_{18}\) \\ \hline \multirow{3}{*}{\(S_{4}\)} & \(S_{3}\) & \(P_{3}\), \(P_{18}\) \\  & \(S_{5}\) & \(P_{4}\) \\ \hline \multirow{3}{*}{\(S_{5}\)} & \(S_{4}\) & \(P_{4}\) \\  & \(S_{6}\) & \(P_{5}\), \(P_{20}\) \\ \hline \multirow{3}{*}{\(S_{6}\)} & \(S_{5}\) & \(P_{5}\), \(P_{20}\) \\  & \(S_{7}\) & \(P_{6}\), \(P_{21}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of neighbors and corresponding critical intersection points (CIP) of a node.
# Panoptic Segmentation Method Based on Feature Fusion and Edge Guidance
## 4 Experiments  
### Specific Realizations  
#### 4.1.1 Datasets and Evaluation Indicators  
This paper selects the widely recognized Cityscapes and MS COCO Panoptic 2017 datasets in the field of panoptic segmentation as the basis for model training and evaluation. The Cityscapes dataset primarily focuses on urban street scenes and contains 5000 high-quality annotated images (2975 for training, 500 for validation, and 1525 for testing), with an image resolution of 1024 \(\times\) 2048 pixels. This dataset contains 19 semantic categories, with instance-level annotations provided for 8 of these categories. The MS COCO Panoptic 2017 dataset contains images from common scenes, with a maximum image resolution of 640 \(\times\) 640 pixels. This dataset contains 133 semantic categories (80 of which are instance categories), and the training, validation, and test sets comprise 118 k, 5 k, and 20 k images, respectively. Both datasets are representative benchmarks in the field of panoptic segmentation and enable effective evaluation of the model's performance across diverse scenes.  
The panoptic segmentation task, at the same time as its proposal, introduces the panoptic quality (PQ) metric to better measure the performance of task execution. PQ provides a comprehensive and accurate measurement of model performance by combining segmentation quality (SQ) and recognition quality (RQ). SQ reflects the model's segmentation accuracy at the pixel level. Specifically, SQ represents the average IoU value of all object masks that are correctly identified and have an intersection over union (IoU) greater than 0.5. RQ is used to measure the accuracy of the model in target semantic category recognition, focusing on the model's ability to correctly classify objects.  
PQ, SQ, and RQ expressions are shown as Equations (5)-(7), where TP represents instances correctly predicted by the model; FP represents instances incorrectly predicted by the model; FN refers to predictions where the category is correct but the instance is identified incorrectly, and IoU(p,g) is the intersection over union between the predicted object p and the ground truth g. PQ ranges from 0 to 1 and is typically expressed as a percentage.  
\[SQ=\frac{\sum_{(p,g)}\in TP^{IoU(p,q)}}{|TP|} \tag{23}\]  
\[RQ=\frac{|TP|}{|TP|+\frac{1}{2}|FP|+\frac{1}{2}|FN|} \tag{24}\]  
\[PQ=SQ\times RQ \tag{25}\]  
PQ not only comprehensively reflects the performance of the model in instance segmentation and semantic segmentation, but also further refines into two sub-indicators: \(PQ^{st}\) and \(PQ^{th}\). Among them, \(PQ^{st}\) focuses on evaluating the segmentation quality of non-instance classes (usually background), while \(PQ^{th}\) is used to measure the segmentation effect of instance classes (foreground objects). This refined evaluation system enables researchers to analyze the performance of the model in different scenarios more comprehensively and accurately.
# A Phase Correction Model for Fourier Transform Spectroscopy
## 3 Phase Correlation Method
The Hilbert difference method involves taking the difference between the absolute values of the cross-correlation function and its Hilbert transform. This process preserves values near the peaks while reducing the correlation of values outside the peaks. As a result, the sharpness of the main peak in the waveform of the received signal's correlation function significantly increases [24]. After using the Hilbert difference method, the correlation peak becomes sharp and steep. Therefore, we assume that the points on the left and right sides near the peak have the same derivative values, but the point on the left is positive, while the one on the right is negative. As shown in Figure 3b, we define the maximum point and its adjacent two data points: \(R(-1)\), \(R(0)\), and \(R(1)\) (supposing that \(R(1)>\)\(R(-1)\)). By geometric considerations, we can derive that  
Figure 2: A schematic representation of the selection of two signals in PCM-APF model. \[\Delta_{t}=\frac{\Delta_{s}}{2}[1-\frac{|R(0)-R(1)|}{|R(0)-R(-1)|}] \tag{4}\]  
where \(\Delta_{t}\) is the distance between the sampled maximum point and the actual peak, and \(\Delta_{s}\) is the sampling interval. So the linear phase correction of this interferogram can be completed using the Mertz method. This involves processing the original interferogram using Equation (3), where the error phase is \(\varphi(\
u)=(N\Delta_{s}+\Delta_{t})\
u\). \(N\Delta_{s}\) represents the distance from the maximum point \(R(0)\) to the \(y\)-axis, and its value is an integer multiple of the sampling interval.