esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Evaluation Metrics
OA (sometimes abbreviated oAcc) measures the overall effectiveness of a classifier by computing the ratio between the number of truly classified samples and the total number of samples. \[\text{OA}=\frac{\sum_{i=0}^{K}p_{ii}}{\sum_{i=0}^{K}\sum_{j=0}^{K}p_{ij}} \tag{4}\]  
mAcc measures the average per-class effectiveness by computing the OA per class and averaging by the total amount of \(K\)[115]. \[\text{mAcc}=\frac{1}{K+1}\sum_{i=0}^{K}\frac{p_{ii}}{\sum_{j=0}^{K}p_{ij}} \tag{5}\]  
OA and mAcc are commonly used as performance metrics for point cloud classification [17; 100; 101]. However, for reasons we already explained in the last section (Section 3.3), both are hardly meaningful in the presence of imbalanced classes and to evaluate segmentation results. Accounting for the area of segment overlap improves the informative value. The formula to calculate the mean Intersection over Union (mIoU) is reported in Equation (6). mIoU, as shown in Figure 11, computes the intersection ratio between ground truth and predicted values per class and averages the sum over the total number of classes \(K\)[116]. \[\text{mIoU}=\frac{1}{K+1}\sum_{i=0}^{K}\frac{p_{ii}}{(\sum_{j=0}^{K}p_{ij}+p_ {ji})-p_{ii}} \tag{6}\]  
mIoU and mAcc are the most frequently used performance metrics for 3D point cloud semantic segmentation [40; 59; 74], assuming \(L_{i},I\in[0,K]\) is the number of instances in every class and \(c_{ij}\) is the number of points of instance \(i\) inferred to belong to instance \(j\) (\(i=j\) represents correct and \(i\
eq j\) represents incorrect segmentation) [26]. The formulas to calculate the average precision (AP) and the mean class average precision (mAP) are reported in Equations (7) and (8), respectively. Figure 11: Visualization of the Intersection over Union (IoU). AP is often used as the metric for 3D object detection [117]. It is calculated class-wise as the area under the precision-recall curve. Figure 10.0  
mAP is frequently used for 3D object detection and 3D instance segmentation if discriminating between multiple instances within one class. It is an extension of AP by averaging the per-class precision over the total number of \(K\)[26; 109]. Figure 10.1  
Apart from these commonly used metrics in machine learning, other problem-specific metrics are worth mentioning: Overall average category Intersection over Union (Cat.mIoU) and overall average instance Intersection over Union (Ins.mIoU) can be used for 3D part segmentation [26]. Precision and success are commonly used to evaluate the overall performance of 3D single-object tracking. Average multi-object tracking accuracy (AMOTA) and average multi-object tracking precision are the most frequently used metrics for 3D multi-object tracking [25; 37].
# Water Body Mapping Using Long Time Series Sentinel-1 SAR Data in Poyang Lake
## 3 Results  
### The Variation in \(\sigma^{0}\) for Different Land Cover  
After pre-processing all the Sentinel-1 SAR data, the series SAR images were utilized to investigate the multi-temporal backscatter coefficient properties (e.g., mean and standard deviation) for persistent water area (PW), permanent non-water area (NW), and seasonal water area (SW). Here, we selected all the Sentinel-1 SAR data for the years 2016 and 2017. The statistical results of \(\sigma^{0}\) for each class are shown in Figure 9.  
From Figure 9, we can see that the backscatter for the permanent water area is weaker but more stable than that for the non-water area, and the backscatter coefficients under VH polarization (around \(-29\) dB) are lower than those under VV polarization (around \(-23\) dB)  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & & **Predicted** \\ \cline{3-4}  & & **Non-Water** & **Water** \\ \hline \multirow{3}{*}{Actual} & Non-water & TN & FP \\ \cline{3-4}  & Water & FN & TP \\ \hline \hline \end{tabular}
\end{table}
Table 1: Confusion matrix used for binary classification.  
and much more concentrated, with a smaller variance (Figure 9b). For the seasonal water area, the backscatter is lower when the area is submerged by water. However, when the water body retreats, the backscatter becomes stronger. Therefore, there are two periods of strong backscatter in these two years, especially from August 2016 to April 2017. For the non-water area, the backscatter is strong and much more concentrated, with a smaller variance (Figure 9b). Similarly, the backscatter coefficients under VH polarization (around \(-15\) dB) for the non-water area are lower than those under VV polarization (around \(-9\) dB). We can also see that there is no overlap between the backscatter coefficients under different land covers and different levels of polarization, especially between water bodies and other types of land cover. Therefore, long-time-series Sentinel-1 SAR data can be used for water body extraction [31].  
Figure 9: The temporal variations in radar backscatter coefficient from Sentinel-1 SAR data for different land cover types and polarization. PW is for persistent water area, SW is for seasonal water area, and NW is for non-water area.
## 2 Materials and Methods
### The Engine Test Bench, Equipment, and Specifications  
The SI engine was connected directly through the shaft to the Eddy-current brake dynamometer AMX200/100 (Automex Group sp. z o.o., Gdansk, Poland) and controlled by the MOTEC M800 (MoTeC, Melbourne, Australia) electronic control unit (ECU). For experimental tests, gas injectors were installed in the intake manifold and adapted for using CNG. The in-cylinder pressure was measured by a piezoelectric sensor AVL ZI31_Y7S (AVL, Graz, Austria) (sensitivity 11.62 pC/bar), which is integrated into the spark plug. Pressure signal amplifier with dynamic pressure measurement system AVL DiTEST DPM 800 (AVL DiTEST GmbH, Graz, Austria) was used for data acquisition. The position of the crankshaft was determined with a built-in angle encoder A58M-F on the crankshaft, which transmits the signal to the LabVIEW Real Time module every 0.1758 crankshaft angle degree (CAD), synchronously with the signal from the in-cylinder pressure sensor. After collecting data from 100 engine operation cycles, they were saved and visualized in LabVIEW software (version 14.0.1f3). The amount of airflow and fuel mixture into the engine required for the intended load and engine speed was regulated by the throttle. The amount of NG consumed was fixed using a Coriolis flow meter RHEONIK RHM 015 (Rheonik Messtechnik GmbH, Odelzhausen, Germany) (accuracy \(\pm\) 0.10%), and a fuel mass meter was used to measure gasoline consumption. The layout of the engine test setup is shown in Figure 1, while specifications of the engine are presented in Table 1.  
The intake air mass was quantified using a Bosch HFM 5 (Robert Bosch GmbH, Gerlingen, Germany) hot-film sensor (measurement error \(\pm\)2%), while pressure in the intake manifold was monitored with the manifold absolute pressure sensor Bosch DSS3 TF (Robert Bosch GmbH, Gerlingen, Germany). The temperature of intake air and exhaust gas were fixed with a K-type thermosensor (precise to \(\pm\) 0.5%). The composition of the gas was analyzed before the catalyst converter using emission measurement system MRU MGAprime (MRU Messgerate fur Rauchgase und Umweltschutz GmbH, Neckarsulm-Obereisesheim, Germany). The engine was warmed up to operating temperature before each test to ensure data stability and repeatability. Exhaust gas concentrations were measured at 3 s intervals over a 1 min period. The characteristics of exhaust gas measurement system are presented (Table 2).
## 2 The Basic Structure and Operation Principle of DLSRG  
### Basic Structure  
The structure of DLSRG has the advantages of simple structure, easy opening of die, winding by suit, and low requirement for installation and maintenance, and DLSRG operates independently, and the mutual inductance between phase and phase is relatively small, which can be ignored, so the difficulty of control is greatly reduced.  
The basic structure of DLSRG is shown in Figure 1. The generator has been preliminarily designed following the approach presented in the reference [18]. The optimization has been performed by letting some geometrical parameters be varied in order to achieve maximum transverse magnetic pull and ensure self-generation capability. The stator and the rotor are made of silicon steel sheet (DW465-50), which is thick in 0.5 mm and has a stacking coefficient of 0.9. The excitation windings are arranged on the stator teeth in turn and are fixed by epoxy resin slot wedge. The precision of slot wedge thickness is low, which is easy to fix and install. The stator slot adopts open slot, which requires lower precision of slot wedge thickness, this kind of structure is easy to fix and install. The rotor is fixed on the shell of generator through linear bearing and base, so reciprocating motion can be realized. The DLSRG cross-section diagram is shown in Figure 2. The letter A, B, C represents the three-phasewinding, and the three-phase winding adopts the centralized winding. According to the symmetrical structure, each phase winding is divided into two groups and four coils, and the magnetic loop is formed through the excitation. The three-phase excitation is different from the traditional three-phase excitation. Traditional three-phase excitation, phase difference is 120 degrees. In DLSRG winding, the three phase excitation is equal, the phase difference is 0, the structural parameters of DLSRG are shown in Table 1.
## 2 Materials and Methods  
ITO and AZO were deposited on top of an n-Si substrate to fabricate ITO/AZO/Si heterojunction devices. Firstly, AZO was deposited on chemically cleaned n-type Si substrate by a dual beam co-sputtering method. Two separated 4-inch targets of ZnO (99.999%) and Al (99.999%) were used in the same chamber. ZnO with a RF magnetron and Al with a DC magnetron were applied simultaneously for AZO co-sputtering process. A constant RF power of 300 W was used for the ZnO target. Various values of DC power (0 \(\sim\) 45 W) were applied to Al target to achieve different Al doping concentrations (0 \(\sim\) 6.89 w.t.%) in AZO thin films. Al doping concentration (wt%) in ZnO was calculated based on the material density of Al (2.7 g-cm\({}^{-3}\)) and ZnO (5.606 g-cm\({}^{-3}\)) and characterized deposited volume of Al and ZnO with controlled deposition power of Al and ZnO. The co-sputtering was performed at a substrate temperature of 300 \({}^{\circ}\)C with 5 mTorr working pressure in Ar gas ambient. During the deposition process, the sample was rotated (7 rpm) for uniform Al doping and thin film thickness. The deposition time was controlled to be 5 min, and the thickness of the AZO film was observed to be \(\sim\)40 nm with a SEM side view image. Then, a 250-nm-thick ITO layer was deposited on top of the AZO/Si structure without breaking the vacuum. The ITO layer was deposited with an ITO target (In\({}_{2}\)O\({}_{2}\) and 10 wt.% SnO\({}_{2}\)) with 300 W DC power for 15 min in 5 mTorr Ar/O\({}_{2}\) (50/1) gas ambient at 300 \({}^{\circ}\)C.  
For further investigation of the impact of AZO inter-layers, two different device structures (i.e., ITO/n-Si and ITO/AZO/n-Si) were prepared for comparison. For the photovoltaic devices, front and back Al contacts were deposited by thermal evaporation. The fabricated devices had a 0.5 cm by 0.5 cm active area with a front grid shadowing area of 13.9%. With the same fabrication process, 1-mm-diameter circular diodes of AZO/n-Si were also fabricated to investigate the AZO/Si heterojunction properties. The thicknesses of each layer were measured by a field emission scanning electron microscope (FESEM, FEI Sirion). Secondary ion mass spectrometer (SIMS, CAMECA IMS 7f magnetic sector) characterizations were carried out to investigate the chemical depth profile for each layer in the ITO/AZO/Si structures. The crystal structure was studied by using an X-ray diffractometer (XRD, Rigaku Ultima IV). The AZO/n-Si circular diodes were analyzed by capacitance-voltage (C-V) measurement (HP 4280A) to understand the junction characteristics. Photovoltaic properties were measured using a Keithley2400 source meter under dark and illumination conditions (100 mW/cm\({}^{2}\) of AM1.5G solar spectrum, Newport, Xenon 300W).
## 1 Introduction  
Haepyoyijin-tang (HPYJT) is an herbal medicine prescription that consists of 12 medicinal herbs (Perillae Folium, Citri Unshius Pericarpium, Poria Sclerotium, Glycyrrhizae Radix et Rhizoma, Perillae Folium, Ephedrae Herba, Armeniaceae Semen, Mori Radicis Cortex, Asteris Radix et Rhizoma, Fritillariae Thunbergii Bulbus, Platycodonis Radix, and Zingiberis Rhizoma Recens) that is used in Korea to treat coughs and asthma caused by the common cold [1].  
HPYJT has been demonstrated to exhibit biological action, such as protective effects on pulmonary injury caused by SO2, and to affect airway mucin production and gene expression control in respiratory diseases that are accompanied by mucus hypersecretion [2; 3]. The major components that make up HPYJT are phenolic compounds (e.g., homogentisic acid and 3,4-dihydroxybenzaldehyde) from Pinelliae Tuber [4], flavonoids (e.g., narirutin, hesperidin, and tamgeretom) from Citri Unshius Pericarpium [5], steroids (e.g., ergosterol peroxide), sesquiterpenoids (e.g., (S)-(+)-turmerone), and triterpenes (e.g., pachymic acid and polyporenic acid C) from Poria Sclerotium [6], flavonoids (e.g., liquiritin and liquiritin apioside) and triterpenes (e.g., glycyrrhizinic acid and 18\(\beta\)-glycyrrhizinic acid) from Glycyrrhizae Radix et Rhizoma [7], phenolic compounds (e.g., roscamy acid and elemicin), monoterpenes (e.g., perilladehyde) from Perillae Folium [8], alkaloids (e.g., ephedrine and pseudoephedrine) form Ephedrae Herba [9], phenolic compounds (e.g., amygdala and neoamygdalin) from Armeniaceae Semen [10], phenolic compounds (e.g., mulberroside A) and flavonoids (e.g., kuwanon G and morusin) from Mori Radicis Cortex [11; 12], phenylpropanoids (e.g., chlorogenic acid and 3,4-dicaffeoylquinic acid), flavonoids (e.g., quercetin and kampferol), and triterpenes (e.g., shionone) from Asteris Radix et Rhizoma [13; 14], alkaloids (e.g., peimine and peiminine) from Fritillariae Thunbergii bulbus [15], triterpenes (e.g., platycodin D and platycocoside E) from PlatycodonisRadix [16], and phenolic compounds (e.g., 6-gingerol and 6-shogaol) from Zingiberis Rhizo Recens [17].  
To establish an efficient quality control protocol for HPYJT, we developed and validated the first simultaneous quantitative analysis using high-performance liquid chromatography equipped with photodiode array detector (HPLC-PDA) system on the eight main constituents of HPYJT. Marker components for quality control of HPYJT were selected as follows: mulberroside A (MULA) from Mori Radici s Cortex, amygdalin (AMY) from Armeniaceae Semen, liquiritin apioside (LIQA), liquiritin (LIQ), and glycyrrhizinic acid (GA) from Glycyrrhizae Radix et Rhiz, hesperidin (HES) and narirutin (NAR) from Citri Unshius Pericarpium, and rosmarinic acid (RA) from Perillae Folium.
# A Secure and Efficient Group Key Management Scheme for Clusters of String Inverters
## 4 The Group Key Management Scheme
### The Key Refresh Operation  
Let us consider the situation in which \(K_{G}\) is the current group key, which has to be refreshed. The refresh operation consists of the following steps.  
1. The KDS selects a KDC and securely sends it a <<Refresh> message.
2. Upon receiving the <<Refresh> message, the KDC reads a timestamp \(\tau\) from its clock, randomly generates a new random group key \(K^{{}^{\prime}}_{G}\), and securely multicast a message <<SetKey>, IV, \(\left\{t,K^{\prime}_{G}\right\}_{K_{G}},H_{K_{G}}\left(\left\{t,K^{\prime}_{ G}\right\}_{K_{G}}\right)\); in its encrypted and authenticated form under the current group key \(K_{G}\) to every inverter in the cluster. The notation \(H_{K_{G}}(\cdot)\) indicates a keyed hash function, namely a Message Authentication Code (MAC) whereas IV denotes an initialization vector, i.e., a nonce. Upon receiving the <<SetKey>, IV, \(\left\{t,K^{\prime}_{G}\right\}_{K_{G}},H_{K_{G}}\left(\left\{t,K^{\prime}_{ G}\right\}_{K_{G}}\right)\) multicast message from the KDC, an inverter \(i\) takes the following actions: 1. Compute the MAC applying \(H_{K_{G}}(\cdot)\) on the encrypted part of the message, \(\left\{t,K^{\prime}_{G}\right\}_{K_{G}}\), and compare it with the MAC contained in the message (if the MACs do not match, the protocol is aborted); 2. Decrypt the encrypted part of the message, \(\left\{t,K^{\prime}_{G}\right\}_{K_{G}}\), by means of the current group key \(K_{G}\) and the nonce IV; 3. Check the freshness of timestamp \(\tau\) (if the timestamp is not fresh, the protocol is aborted); 4. Store key \(K^{{}^{\prime}}_{G}\) in the hardware security module \(\text{HSM}_{i}\); 5. Install key \(K^{{}^{\prime}}_{G}\) as the current group key.
3. Finally, the KDC replies to the KDS that the refresh operation has completed. At the end of the protocol, all inverters in the group \(G\) can communicate using the new group key \(K^{{}^{\prime}}_{G}\).
# A Tweak-Cube Color Image Encryption Scheme Jointly Manipulated by Chaos and Hyper-Chaos
## 2 Preliminaries
### The Logistic-Fraction Hybrid Chaotic Map
#### 2.1.1 Bifurcation Graph  
The bifurcation graph can objectively reflect the region and state of the chaotic behavior of a map. Figure 1 exhibits the bifurcation graphs of the LFHCM, Logistic map, and Fraction map. It can be found that the Logistic map shows chaotic characteristics only when \(\mathrm{b}\in(3.6,4]\). Although the Fraction map has more complex dynamic properties than the Logistic map, its chaotic interval length is also less than 1. The interval in which the LFHCM presents chaotic state is \(\mathrm{a}\in[5.9,11.5]\). It is obvious that the chaotic region of the new chaotic map is increased, which means that the key space is also improved. Besides, in contrast to the intervals of some 1D chaotic maps in Table 1, the LFHCM has the largest region.
# A Literature Review of Adhesive Systems in Density: Key Components and Their Clinical Applications
## 5 Resin-Dentin Bonding  
Following [PERSON]'s groundbreaking work, [PERSON] emerged as a prominent advocate for adhesive technology in dentistry. [PERSON], a chemist at D-Trey/Amalgamated Dental Company in London, UK, introduced Sevriton Cavity Seal in 1949, an acidic adhesive that interacted with the tooth surface on a molecular level. This innovative product earned [PERSON] the title of the true \"Father of Modern Dental Adhesives\", as his concept laid the foundation for subsequent generations of dental adhesives. [PERSON] further advanced this field with his seminal research in 1955, demonstrating that enamel could be acid-etched to create an ideal surface for resin bonding. His work established enamel etching as a fundamental step in bonding procedures, revolutionizing dental practice and paving the way for the development of various adhesive systems that are now essential in restorative dentistry [30; 33; 95; 96].  
Dental adhesives have the following characteristics: they reduce marginal discoloration, minimize microleakage, reinforce the dentin or the enamel, preserve the tooth structure, and may even decrease postoperative sensitivity [97]. Indeed, years after [PERSON]'s work, significant progress has been made in the area of adhesives in restorative dentistry. The development and evolution of reliable enamel and dentin bonding agents is one such example [9; 33; 97].  
Adhesive systems have progressed from the largely ineffective systems of the 1970s and early 1980s to the relatively successful ER and SE systems of today. The latest players in the adhesive marketplace are the so-called UAs [3; 9; 33].  
This is the \"cosmetic revolution\" in dentistry, blossomed in large part due to dramatic advances in adhesive technology. In fact, the longevity and predictability of many if not most current restorative procedures are wholly predicated on the dentist's ability to bond various materials to tooth tissues [33]. Recent advances in the development of new bonding systems have resulted in higher bond strengths (BSs). Each adhesive system must be handled with careful attention to the details of application for maximal success [33; 98].
# Disguised Full-Duplex Covert Communications
## 6 Discussion  
### Performance  
The numerical results from Figures 3-7 confirmed that the optimized transmit power at the disguised FD destination node has a significant impact on the covert rate performance. Furthermore, Figure 3 revealed an interesting relationship between the source and destination transmit power, which is that applying more (less) destination transmit power achieves a higher covert rate when the source transmit power is low (high). Figure 7 also showed that the optimal destination transmits power exploits the best trade-off between the covert rate and minimum DEP threshold.  
Figure 6: The average covert rate \(r_{c,k}\) versus the minimum DEP threshold \(\varepsilon\).  
Figure 7: The average DEP versus the minimum DEP threshold \(\varepsilon\): The optimal scheme benefits from the best trade-off between the covert rate and DEP for a given \(\varepsilon\).
# A Millimeter-Wave Radar Tunnel Obstacle Detection Method Based on Invalid Target Filtering
## 3 Test and Result Analysis
### Performance Test of Filtering Algorithm for Invalid Targets
#### 3.1.2 The Vehicle Was Running in a Straight Line under the Smooth Road Surface  
The vehicle was designed to travel at speeds of \(5,7\) and \(10\) km/h, respectively, and as a control variable, the three speeds were tested in the smooth driving state as well. During the tests, the positions of the three obstacles and the angle of the millimeter-wave radar were kept constant, and three tests were conducted at each speed to verify the effect of speed on the filtering effect. The results before and after processing in the driving condition are shown in Figure 8.  
Some pseudo-targets were generated during the driving process due to vehicle vibration and ground inequality. After nine trials, the invalid target filtering algorithm effectively filtered out pseudo targets, empty targets and non-threatening targets, with an average filtering rate of over 86%.
# Joint SAR Image Time Series and PSInSAR Data Analytics: An LDA Based Approach
## 5 Results and Discussion
### 2 nd Experiment--Multi Look Approach  
For the multi-looking approach, the ML window dimensions were chosen, in case of both datasets, in order to obtain a square ground pixel projection (5 pixels in azimuth and 1 pixel in range).  
For the case of ERS data set, a number of 13,610 PS candidates were identified on the ML dataset, covering 7.48% of scene's surface. A number of 100,967 PS candidates were detected for the Sentinel-1 SITS, representing 11.55% of the total surface of the analyzed scene. The increase of coherent structures presence is a consequence of noise mitigation caused by pixel's spatial averaging. Therefore, the percentage of detected PS grouped in a single category of evolution determined by the LDA modeling signifies 94.45% for the ERS SITS (Figure 12) and 93.3% for the Sentinel-1 SITS  
Figure 11: Single-Look Sentinel-1 image amplitude, Constanta (**A**); Scene classification map presenting categories of evolution (**B**); spatial distribution of detected Persistent Scatterers (**C**).  
Figure 10: Single-Look ERS image amplitude, Buzau (**A**); Scene classification map presenting categories of evolution (**B**); spatial distribution of detected Persistent Scatterers (**C**).  
(Figure 13). The slightly decrease of this percentage (as compared to the case of SL dataset) can be related to the fact that the spectral coherence study could not be included in PS detection algorithm because of the absence of phase information, caused by ML.