esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Analysis
### Time Complexity Analysis  
#### 4.5.1 Proposed Method Time Complexity Analysis  
The time consumption of our proposed algorithm includes three parts: first, the original image is processed by the hybrid spatial filter, and the time complexity is O(L\({}^{2}\)*W) level (W is the filter window size). Second, the multi-thresholding based on region growing is performed, and the time complexity is O(L) level. Finally, the time complexity of multi-threshold segmentation based on interval iteration is O(L) level. In sum, the time complexity of this algorithm is O(L\({}^{2}\)*W) level. When the number of thresholds \(K\) = 2, 3, 4, 5, respectively, the average running time of the algorithm on 10 test images is shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Number of** **Thresholds (K)**} & \multicolumn{4}{c}{**Average Uniformity Measure (\(U\))**} \\ \cline{2-5}  & **Non-F-RGM** & **F-RGM** & **Non-F-RGIIM** & **Proposed** \\ \hline
2 & 0.9696 & 0.9729 & 0.9795 & **0.9825** \\
3 & 0.9723 & 0.9775 & 0.9838 & **0.9840** \\
4 & 0.9677 & 0.9820 & 0.9864 & **0.9866** \\
5 & 0.9791 & 0.9850 & 0.9877 & **0.9884** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation experimental results.  
Figure 9: The average running time of the proposed algorithm under different thresholds.  
In Figure 9, it can be noted that the running time of the proposed algorithm gradually decreases with the increase in the number of thresholds. The overall running time of the algorithm is about 0.6 s, which can be fully applied in real-time systems.
## 2 State of the Art
### Internet of Things and Smart Classroom
[18] described an approach for smart health based on biometrics and IoT. Authors have developed a new standard for applying biometrics for developing smart healthcare solutions based on IoT. The approach was tailored to allow high data access capacity while being easy to use. The use of biometric sensors adds an additional layer of security to user authentication.
## 3 Results and Discussion  
### Flux Behaviour  
Figure 1 presents the permeate flux depending on the VRR during ultrafiltration of sweet buttermilk at different transmembrane pressures. The permeate flux decreased with the decrease in the transmembrane pressure (\(p<0.05\)) except at VRR 2 where the values were statistically equal (\(p>0.05\)). Similar results were obtained in [22] where the authors found that the operational pressure has a great effect on the ultrafiltration performance. They established the highest permeate flux at 6 bar, and the lowest at 3 bar during ultrafiltration of a mixture of cow's and buffalo's kinds of milk (1:1). These authors found a similar trend for the retentate flux. Our results showed a decrease in the rise in VRR (\(p<0.05\)). The rise of the concentration of the solution causes an increase in the dynamic viscosity, thus reducing the mass transfer coefficient and the flux decreases.  
The permeate flux decrease is also due to the concentration polarization and membrane fouling. During membrane filtration, an accumulation of retained colloidal particles at the membrane surface is observed leading to a rise of a concentration gradient of compounds perpendicular to the membrane, called concentration polarization [10]. The membrane fouling is provoked by the direct interaction of the colloidal particles with the membrane and is a different phenomenon from concentration polarization. The fouling depends on the milk components (casein micelles, whey proteins, minerals, etc.) [20]. The authors in [19] found a significant decrease in the permeate flux from the beginning to VRR 2 during ultrafiltration of whey buttermilk with membranes of 30, 50, 100, and 300 kDa, after that the decrease was not so pronounced. The researchers in [21] established that the permeate flux increased significantly from 0.25 to 1 bar, then remained relatively constant. The authors in [18] reported that the temperature (10 and 50 \({}^{\circ}\)C) of the skim milk had a significant effect on the permeate flux when an ultrafiltration membrane of 10 kDa was used. The same authors found that the pressure adjustment did not affect the permeate flux at a temperature of 10 \({}^{\circ}\)C. According to [36], the rate of the permeate flux decline was more rapid during ultrafiltration at 50 \({}^{\circ}\)C in comparison with 15 \({}^{\circ}\)C. These authors attributed this phenomenon to the rise in calcium and protein deposition on the membrane surface at 50 \({}^{\circ}\)C.
# A Look at the Importance of Chirality in Drug Activity: Some Significative Examples
## 10 Anti-Histamine Drugs (H\({}_{1}\) Receptor Antagonists)
### Repositioned Chiral Drugs  
Drug repositioning or drug repurposing represents an effective strategy to find new indications for existing drugs and is highly efficient, low-cost, and quite riskless. It shortens the traditional drug development strategies that generally include five long and intricate steps: discovery and preclinical, safety review, clinical research, FDA review, and FDA post-market safety monitoring [141]. As an example, thalidomide (**56**) is a chiral drug, once used to relieve morning sickness, and then unfortunately renowned as teratogen and neurotoxic. It is now a repositioned drug that received the approval by FDA for the treatment of erythema nodosum leprosum and multiple myeloma under strict control. It has also been found to be effective in the treatment of advanced renal, esophageal, chemotherapy refractory endometrial and pancreatic cancer and is used in the treatment of inflammatory skin, autoimmune disorders and inflammatory bowel disease [38; 142]. Many repositioning drugs have been used for the treatment of COVID-19 as well and many others are undergoing extensive clinical research. Among them, several heterocyclic-based approved chiral drugs have been recently reported for the treatment of COVID-19, such as cidofovir (**57**), ribavirin (**58**), and ruxolitinib (**59**) [143]. Cidofovir is a broad-spectrum antiviral agent, which acts as a nucleoside phosphonate analogue in vitro against multiple viruses, including all serotypes of human adenovirus [144]. Ribavirin is a common antiviral drug that inhibits the replication and spread of multiple viruses, including severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and Lassa fever [145]. Ruxolitinib is a potent and selective oral inhibitor of Janus kinases JAK1 and JAK2. It has been approved for the treatment of myelofibrosis by the US FDA in 2011 and by the European Medicines Agency (EMA) in 2012. In 2014, it received the approval for the treatment of hydroxyurea-resistant or -intolerant polycrystalline vera [146].
# EDC-Net: Edge Detection Capsule Network for 3D Point Clouds
## 3 Proposed Method
### Network Architecture
#### 3.1.1 Input Data  
The source input of EDC-Net is a raw point cloud of dimensions \(N\times 3\) where \(N\) is the number of points in the point cloud. We consider a \(S\)-dimensional point cloud with \(N\) points, denoted by \(\mathcal{X}=\{x_{1},...,x_{N}\}\subset\mathbb{R}^{S}\). Each point of a point cloud commonly contains three coordinates \(x_{i}=(x_{i}^{1},x_{i}^{2},x_{i}^{3})\), which means that \(S=3\). Furthermore, it is possible to include further coordinates, such as those representing color information and normal vectors. However, in this work we just consider the coordinates.  
Two formats of labels are defined to be considered during the training process of EDC-Net. First, segmentation labels of the shape \(N\times 2\), where \(N\) is the number of points in a point cloud, and for each indexed point there is a corresponding value for each class of edge and non-edge. Second, edge labels are of the shape \(n_{g}\times 3\), where \(n_{g}\) is the number of points that are labeled as sharp edge regions in the ground-truth of the given point cloud and each point is represented by its coordinates in three dimensions. The number of edge points is less than the number of points in a point cloud (\(n_{g}<N\)). Segmentation labels and edge labels are applied during the training process for computing the loss. The details of loss calculation are explained in Section 3.2.  
Figure 1: Overall overview of the proposed EDC-Net architecture. The input of this architecture is a raw point cloud of dimension \(N\times 3\). The ground-truths are edge labels and segmentation labels which distinguish edges and non-edges points as two classes. The features graph is an aggregation neighborhood graph based on a concatenation of features from euclidean and eigenvalues spaces. EdgeCaps module proceeds with point-wise segmentation to classify edge and non-edge points. \(\oplus\) stands for concatenation.
# Meta-Learning Approach for Adaptive Anomaly Detection from Multi-Scenario Video Surveillance
## 1 Introduction
Previous studies on anomaly detection across multiple scenarios have faced significant challenges in generalization. Traditional models struggle to adapt to different scenarios and viewpoints, and while few-shot learning (FSL) [1] methods have been introduced to address this issue, they remain limited to specific environments and fail to generalize effectively. To overcome these limitations, a meta-learning framework, particularly MAML, can be employed. MAML optimizes model parameters to enable rapid adaptation to new tasks with only a few examples through minimal gradient updates. The main contributions of this paper can be summarized as follows:  
* The MAML technique is designed to enable anomaly detection in 14 different scenarios, including highways, shops, front doors, offices, parking lots, pedestrian streets, street highviews, warehouses, roads, parks, malls, trains, restaurants, and sidewalks by using a small number of normal and anomalous data, where data collection is limited. * Our method leverages metadata and swin transformers to extract spatial features from frames of video data from different targeted scenes, thereby enhancing adaptability to different scenarios. * By leveraging anomaly detector models using MAML to acquire the capability to classify between normal and abnormal data, better accuracy in anomaly detection from different scenarios with limited data is realized from the targeted domain.
# Tempo and Metrical Analysis by Tracking Multiple Metrical Levels Using Autocorrelation
## 2 Related Work
### Acetuation Curve
#### 2.1.2 Localized Methods  
To detect significant energy bursts on highly localized frequency ranges but still filter out the artifacts due to the possible frequency fluctuation along time of such localized events, it is necessary to add some tracking capability. The approach presented by [9] can be considered to be an answer to this problem. From a spectrogram of frame length 46.44 ms (frequency resolution 21.53 Hz) and hop 11.61 ms, rapid increases of amplitude are searched for on individual frequency components. For each detected onset is evaluated its \"degree of onset\", defined as the rapidity of increase in amplitude.  
More precisely the method searches for particular frequency components \(f\) at a particular time frame \(t\), where the two following conditions are reached:  
1. The power of the spectral component at frequency \(f\) and time \(t\), denoted \(p(t,f)\), is higher than the power around the previous time frame at similar frequency: \[p(t,f)>pp(t,f)\] (1) where \(pp\) is defined as follows: \[pp(t,f)=\max\big{(}\max_{f^{\prime}\in[f-1,f+1]}p(t-1,f^{\prime}),p(t-2,f) \big{)}\] (2) We propose to call this time-frequency region associated with \(pp(t,f)\)_contextual background_ at time \(t\) and frequency \(f\). This is illustrated in Figure 3.
2. The power at next time frame and similar frequency \(np\) is higher than the power in the contextual background: \[np(t,f)>pp(t,f)\] (3) where \(np\) is given by the following definition: \[np(t,f)=\min_{f^{\prime}\in[f-1,f+1]}p(t+1,f^{\prime})\] (4)  
If p is an onset, its degree of onset is given by  
\[do(t,f)=\max(p(t,f),p(t+1,f))-pp(t,f) \tag{5}\]  
For a given instant \(t\), the degrees of onset are summed over the frequency components, resulting in an onset curve.  
Figure 3: Comparison between the method in [9] (**left**) and our proposed method (**right**), for the comparison of a given spectrogram amplitude \(p(t,f)\) at time \(t\) and frequency \(f\) with amplitudes from previous (and next) frames.
## 2 The Working Principle of the Single-Axis Scanning System with Two Galvanometers
The working principle of the 1D scanning system using two galvanometers is shown in Figure 3a,b. In Figure 3a, the galvanometer Gx deflects at an appropriate angle to guide the beam to the desired position. For this system, telecentricity still exists and varies depending on the deflected angle of the incident beam and the distance to the f-theta lens. Figure 3b shows a single-axis scanning system using two galvanometers, working by shifting the laser beam in a direction parallel to the initial beam in the scanning system using 1 Galvo, and keeping the scan angle constant to ensure the correct positioning in the direction of the laser beam in Equation (1). The new position to minimize the telecentricity value depends on the construction of the f-theta lens. With different f-theta structures, the galvanometers will need to be tilted at different angles. Optimization of the tilt angle of the galvanometers will be performed by the optical design software ZEMAX. A position coefficient \(\mathrm{C_{f-theta}}\) is defined as the coefficient to determine the new position one of the f-theta lens \(\mathrm{P_{1n}}\) from position one \(\mathrm{P_{1}}\), and it depends on the structure of the f-theta lens. \[\mathrm{C_{f-theta}}=\frac{\mathrm{P_{1n}}}{\mathrm{P_{1}}} \tag{2}\]  
The beam deflection angle \(\theta\) describes the relationship between the tilt angle of mirror one \(\theta_{\mathrm{m1}}\) and mirror two \(\theta_{\mathrm{m2}}\)  
\[\theta=2.(|\theta_{\mathrm{m1}}|+|\theta_{\mathrm{m2}}|) \tag{3}\]  
New position one is considered as the position relative to the angle theta to mitigate telecentricity, which can be determined by an equivalent equation as follows:  
\[\mathrm{P_{1n}}=\mathrm{d}(\theta).\tan(2.(|\theta_{\mathrm{m1}}|+|\theta_{ \mathrm{m2}}|)).\mathrm{C_{f-theta}} \tag{4}\]  
The \(\mathrm{C_{f-theta}}\) coefficient in this simulation has a value that varies from 1.17 to 1.058 depending on the beam deflection angle from 1 degree to 16.2 degrees, respectively. We can also choose constant factor \(\mathrm{C_{f-theta}}\) = 1.11 as the constant factor for the approximative calculation. Figure 3: Illustration of the working principle of a 1D scanning system using two mirrors (**a**) before and (**b**) after telecentricity compensation with mirror Gx2 in orange, simulated by Zemax.
## 3 Results and Discussion
### Void Distribution Characteristics
#### 3.3.3 The Study on Void Shape Distribution  
Regardless of whether specimens are based on the vibration or static pressure molding, the shape of voids in the mixture is often complex and irregular, which can be refactored by the three-dimensional space of mixture by VG Studio Max 2.2 software. Then the parameters of the shape description, sphericity, is obtained, which is defined as the same as the voids volume of the sphere surface area and the void between the actual surface area ratio [20].  
\[F=S_{\text{D}}/S, \tag{2}\]  
where \(S_{\text{D}}\) is the surface area of a sphere equal in volume to the void of the mixture, and \(S\) is the actual surface area of the void, as well as \(F\) is the sphericity of void. If \(V\) is used to represent the volume of the void, the sphericity of void can be shown as:  
\[F=\ \ \frac{4\pi}{S}\Big{(}\frac{3V}{4\pi}\Big{)}^{\frac{2}{3}}. \tag{3}\]  
According to the different parameters of two-dimensional void shape, the void is divided into three categories to determine whether void shape is regular [21; 22]. Three-dimensional void can similarly be divided into three categories according to the difference of parameter sphericity \(F\), which represents three-dimensional void shape: Regular void (\(0.6<F<1.0\)), irregular void (\(0.45<F<0.6\)), and long void (\(0<F<0.45\)). It can be seen that cube \(F=0.806\), positive triangle cone \(F=0.634\), and ball \(F=1.0\) according to Formula (3), which proves that the larger the sphericity, the more significant the difference between the void and standard sphere, and the more irregular the void shape. Figure 6 shows the strip void and irregular void, respectively.  
It can be seen from Figure 7 that the void types of the XM and GM graded mixture are roughly similar in distribution. The void shape of specimens obtained by different molding methods is different when taking the void shape distribution of the on-site sample as a reference. The strip void of static pressure mixture is 70.28%, that of the vibration compacted mixtures 41.50%, and that of on-site sample is 52.34%. The void shape of vibration compacted specimens is closer to the on-site sample. The reason is that some aggregate particles cannot move freely under the action of static friction in the process of static compression molding until they are crushed. The fine aggregate continues to be squeezed,  
Figure 6: The schematic diagram of the strip void and irregular void.  
which will destroy the original design gradation. In addition, the intermediate size of the aggregate rapidly increases to form complex and irregular void.
# Melody Extraction Using Chroma-Level Note Tracking and Pitch Mapping
## 2 Chroma-Level Note Tracking and Pitch Mapping-Based Melody Extraction
### Harmonic Pitch Class Profile  
The pitch class profile (PCP), proposed by [PERSON] [18], is a twelve dimension vector representing the intensities of the twelve semitone pitch classes. By introducing the harmonic weighting function, [PERSON] proposed the harmonic pitch class profile (HPCP) [17]. HPCP works very well when there are tuning errors and inharmonicity in the spectrum, and has been successfully utilized for music retrieval [19], cover song identification [20; 21], and so on.  
HPCP is defined as [17]  
\[C_{HPCP}(n)=\sum_{i=1}^{N_{\text{pls}}}w(n,f_{i})a_{t}^{2},\,n=1,2,\ldots,N \tag{1}\]  
where \(a_{i}\) and \(f_{i}\) are the magnitude and frequency of the \(i\)-th peak in the amplitude spectrum, respectively, \(N_{\text{pls}}\) is the number of spectral peaks in consideration, \(n\) is HPCP bin, \(N\) is the dimension of HPCP vector, and \(w(n,f_{i})\) is the weight of frequency \(f_{i}\) for HPCP bin \(n\).  
The center frequency at bin \(n\) is  
\[f_{n}=2^{\frac{n}{N}}f_{\text{ref}} \tag{2}\]  
where \(f_{\text{ref}}\) is the reference frequency.  
Figure 1: Block diagram of the chroma-level note tracking and pitch mapping-based method.  
The distance between peak frequency \(f_{i}\) and bin center frequency \(f_{n}\) in semitone is  
\[d=12\log_{2}(\frac{f_{i}}{f_{n}})+12m \tag{3}\]  
where \(m\) is the integer that minimizes \(|d|\), and \(|\cdot|\) is the absolute operator.  
The weight in Equation (1) is  
\[w(n,f_{i})=\left\{\begin{array}{ll}\cos^{2}(\frac{\pi d}{l}),&|d|\ \leq\ 0.5l\\ 0,&|d|\ >\ 0.5l\end{array}\right. \tag{4}\]  
where \(l\) is the width of the weight window.  
The spectrum of each note consists of several harmonics whose frequencies are multiples of fundamental frequency. The value \(i_{h}\) associated to the \(h\)-th harmonic of a note is  
\[i_{h}=\text{mod}((i_{1}\pm 12\log_{2}(h)),12) \tag{5}\]  
where \(\text{mod}(\cdot,\cdot)\) is the modulus operator, and \(i_{1}\) is the pitch class corresponding to the note.  
Then, the frame-wise HPCP values are normalized with respect to the maximum value of each frame to obtain the relative relevance of HPCP bins, i.e.,  
\[C_{nrm}(n)=\frac{C_{HPCP}(n)}{\text{max}_{n}(C_{HPCP}(n))},\ n=1,2,\ldots,N \tag{6}\]  
By the procedure presented in this subsection, the chromagram can be generated by HPCP, indicating the relative salience of different pitch classes of polyphonic music.
## 2 Technology  
The ultra-wideband quadrature signal generation network described in this paper is designed in the GlobalFoundries 45 nm CMOS SOI process. The process provides seven copper metal layers and one aluminum top metal layer, which is generally used to implement measurement PADs, wire bonding and high-density MIM capacitors. A cross-section view of the metal stacks is shown in Figure 2, together with the thickness of each metal layer. As an important characteristic of CMOS SOI technology, one buried oxide layer is located beneath the metal stacks, which could reduce the loss from silicon substrate compared with standard CMOS technology. Owing to the top-three thick metal layers (OA, OB and LD), a 300 pH spiral inductor designed in OB layer could achieve a simulated Q of 20 at 30 GHz. Figure 2 also shows a coplanar waveguide (CPW) transmission line, where signal is propagated in the OB layer and M1-LD could be stacked as ground wall to reduce magnetic coupling between neighboring passive components.  
In addition, the OA and OB layers have the same thickness and sheet resistance, with the potential to provide similar magnitude and phase performance in a hybrid quadrature coupler. In this way, the quadrature signal generation network proposed in this article is mainly built in OA and OB layers.
# Photocatalytic Activity of TiNbC-Modified TiO2 during Hydrogen Evolution and CO2 Reduction
## 3 Results and Discussion
### SEM and TEM Characterizations
Figure 4: **Left—image of a typical 5% TiNbc/TiO\({}_{2}\) heterostructure particle in a transmission electron microscope; right—corresponding EDX maps of individual element distribution; inset—diffractogram obtained from a typical 5% TiNbc/TiO\({}_{2}\) heterostructure particle. **  
Figure 5: Normalized extinction spectra of TiNbc/TiO\({}_{2}\) samples with different concentrations of TiNbc. The spectra are normalized on an extinction value at 320 nm. In terms of the overall rate of CO\({}_{2}\) reduction, the deposition of TiNbC on commercial titanium dioxide did not lead to an increase in activity. At the same time, the presence of a rather large amount of hydrogen in the case of a 1% TiNbC/TiO\({}_{2}\) photocatalyst should be noted. Also, the activity was tested in hydrogen evolution from aqueous solutions of ethanol (Table 3). In the case of this test reaction, an increase in activity was observed when MXene was deposited on the surface of commercial titanium dioxide Evonik P25, and the dependence of activity on the mass fraction of the max-phase passed through a maximum at a content of 5 wt.%. In this case, the deposited maxene could play a role similar to \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Photocatalyst**} & \multicolumn{2}{c}{**Yield, \(\mathbf{\mu mol\ h^{-1}g^{-1}}\)**} & \multicolumn{2}{c}{**W (CO\({}_{2}\)RR),**} \\ \cline{2-6}  & **CH\({}_{4}\)** & **CO** & **H\({}_{2}\)** & **\(\mathbf{\mu mol\ h^{-1}g^{-1}}\)** & **S (CO\({}_{2}\)RR), \%** \\ \hline TiO\({}_{2}\) Evonik P25 & 5.0 & 2.1 & 1.2 & 44.1 & 94.8 \\
1\% TiNbC/TiO\({}_{2}\) & 5.0 & 0.9 & 3.0 & 41.8 & 87.5 \\
5\% TiNbC/TiO\({}_{2}\) & 5.0 & 1.5 & 1.8 & 43.0 & 92.1 \\
10\% TiNbC/TiO\({}_{2}\) & 2.0 & 1.0 & 0 & 18.0 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Photocatalytic activities and selectivity of TiNbC/TiO\({}_{2}\). Figure 6: Tauc plot for TiNbC/TiO\({}_{2}\) samples with different contents of TiNbC. that of a metal deposited on the surface of a semiconductor, increasing the separation of photogenerated charge carriers. The resulting catalytic activities of the heterostructures were not considered commercially applicable (more than 1000 \(\upmu\)mol h\({}^{-1}\)g\({}^{-1}\) is required), but there are ways to improve the catalytic activities of TiNbc/TiO\({}_{2}\). One such way could be to translate the heterostructures into the nanoparticle range by laser ablation or fragmentation [37,38]. Laser fabrication of nanoparticles in a liquid from a mixture of two types of semiconductors as an initial powder yields spherical nanoparticles with a heterostructure. In each case, it is necessary to select the laser modes, as well as the type of solvent.