esa-sceva/satcom-chunk-collection huggingface dataset:
# Three-Stage Deep Learning Framework for Video Surveillance
## 4 Experiment and Results
### Exploring Limitations  
Our approach demonstrates excellent performance in anomaly detection in everyday CCTV footage. However, it is important to understand that it faces challenges in videos with rapid motion (significant frame-to-frame differences). The primary objective of our proposed network is anomaly detection in everyday CCTV footage. To validate this objective, we utilized three datasets: UBI-Fights, Movie Fights, and Crowd. Additionally, we included one dataset, Hockey Fights, with a different nature to examine the additional detection accuracy. As evident from Tables 2 and 3, our proposed model outperforms existing networks in three datasets (UBI-Fights, Movie Fights, and Crowd). However, for the supplementary dataset, Hockey Fights, our model did not achieve better performance compared to existing networks. This result arises from the rapid pace inherent in the hockey games captured in the Hockey Fights dataset, where significant inter-frame disparities facilitate effective detection, even in the absence of intricate models. These limitations provide valuable insights for future research and improvements.
## 2 Materials and Methods
### Data  
#### 2.2.1 Observation Data  
The Maqu site is one of the sites set up by the Zoige Plateau Wetlands Ecosystem Research Station, which is located in the eastern region of the TP [14]. The vegetation type at the Maqu site is typically alpine meadow, with an average height of about 0.2 m. The climate at the Maqu site is a sub-humid climate, with an average annual temperature of 1.9 \({}^{\circ}\)C, and the multi-year average precipitation is 593 mm [25]. The soil type at Maqu is silt loam, which is rich in organic matter. The observation data from Dangx and Haibei that are used in this study were derived from the China flux network ([[http://www.chern.org.cn/](http://www.chern.org.cn/)]([http://www.chern.org.cn/](http://www.chern.org.cn/))) (accessed on 16 December 2020) [26; 27]. The vegetation type at the Haibei and Dangx sites is alpine meadow, and the altitudes are 3148 m and 4250 m above sea level, respectively. The Haibei site has a sub-humid climate, the average annual temperature is \(-1.2\)\({}^{\circ}\)C, and the average annual precipitation is 535.2 mm; precipitation in the growing season (May to September) accounts for 82% of the annual precipitation. The vegetation coverage at the end of the growing season can reach more than 98%, and the soil organic matter content is 5.85% in the top 40 cm. The climate at the Dangx site is a semi-arid climate, with an average annual temperature of 1.3 \({}^{\circ}\)C; the average annual precipitation is 450 mm, of which 85% is distributed from June to August. The vegetation coverage is 50-80%, and the soil organic matter content is 0.9-2.79%. The Maqu, Dangx, and Haibei sites provide long-term conventional meteorological data and flux data. The carbon flux data include gross primary productivity (GPP), net ecosystem exchange productivity (NEE), and ecosystem respiration (Re) (detailed information of the sites see Table 1).
# Recent Advances in Plasmonic Sensor-Based Fiber Optic Probes for Biological Applications
## 4 Theoretical Framework
### SPR in Waveguide Configuration  
In waveguide configuration, the electromagnetic field propagates through the guided modes in the metal-dielectric region and its evanescent wave can penetrate the thin metal film, as shown in Figure 6.  
The resonance is achieved when the wavelength-dependent propagation constant of the guided mode matches that of the surface plasmon mode at the interface of the metal-dielectric region [46]. Therefore, the transmitted spectrum can be observed for the sensing analysis.
# Predicting User's Measurements without Manual Measuring: A Case on Sports Garment Applications
### Anthropometrics for Customer-Tailored Design  
Traditionally, the collection of anthropometric measures was first done in one dimension (1D) by means of calipers to precisely determine the length or width of certain body parts [9; 10; 11]. In addition, a measuring tape allowed to assess the circumferences of, for example, the hips or waist. Such data were gathered in 1D anthropometric databases, such as DINED [12] and DINBelg [13], facilitating the comparison of anthropometric data from different studies.  
Although few dimensions result in an easy interpretation of the data, there was also a need for more detailed models with greater accuracy, which offer added value to designers that want to include the fitting requirements of their products [11]. Therefore, new techniques and tools focused on 3D geometry to include more details of the human body. Initially, CT scans were used to capture, for example, the variety of the human scalp or head shapes without the interference of hair [11; 14]. Of course, disadvantages, such as the required large sample size, feasibility, privacy, and health, had to be considered. Therefore, 3D body scans have also been used to build 3D anthropometric databases, such as the WEAR (World Engineering Anthropometry Resource) annex CAESAR database, leading to the development of human shape models and their deployment in industrial design [11; 15].
# FBG-Based Monitoring of Geohazards: Current Status and Trends
## 4 Current Status of Applying FBG Systems for Geohazard Monitoring  
In the past two decades, numerous investigations have been conducted to monitor different types of geohazards all over the world. Figure 6 illustrates the use of FOS systems in geohazards and infrastructure monitoring. Some case studies related to FBG are presented and discussed below.  
Figure 5: Profile of \(h_{p}\) during typhoon Morakot [55].
# Research on a Rail Defect Location Method Based on a Single Mode Extraction Algorithm
## 5 Conclusions  
A rail defect location method based on an SMEA of ultrasonic guided waves was proposed. The dispersion curves of a CHN60 rail were calculated using the semi-analytical finite element method, the mode shapes of the guided waves were calculated, and the actual received signals were predicted using the simulation data provided by ANSYS. Furthermore, accurate modal identification was achieved using the inverse transformation of the excitation response method, and the amplitude of each mode of the guided waves propagating in the rail was obtained. The rail displacement  
Figure 14: Acquisition waveforms for all modes with distances of: (a) 1.5 m; (b) 4.5 m; and (c) 6 m between excitation points and sampling points.  
Figure 15: The reflection waveforms of mode No. 7 at a distance from the excitation point of: (a) 1.5 m; (b) 4.5 m; and (c) 6 m.  
curves excited by the signals at a low frequency of 200 Hz and high frequency of 60 kHz were correctly predicted.  
According to the modal identification results, an SMEA was proposed. The single mode extraction results at 200 Hz were calculated and the results were consistent with those of the modal identification. Thereafter, taking a railhead defect as an example, according to certain selection principles, mode No. 7 with a signal frequency of 60 kHz and a vertical excitation condition were selected. The reflective modes of mode No. 7 at 1.5, 4.5, and 6 m from the excitation point of the railhead defect were extracted using the SMEA, and the distance between the rail defect and excitation point was obtained according to the time difference. The errors in location were 0.33, 0.36, and 0.39 m, respectively, which all fall within the 0.5 m set as the detection requirements. Hence, it can be said that the precise determination of the rail defect was accomplished.  
Conceptualization, B.X. and X.X.; methodology, X.X. and L.Z.; software, B.X. and H.S.; validation, Z.Y., L.Z. and H.S.; investigation, B.X.; resources, Z.Y.; writing--original draft preparation, B.X.; and writing--review and editing, B.X., X.X. and L.Z.  
This research was funded by the National Key Research and Development Program of China (2016 YFB1200401)  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
SAFE Semi-analytical finite element  
SMEA Single mode extraction algorithm
## 2 Materials and Methods
### Measurement of Chlorophyll, Total Carotenoid, and AsA Content  
Approximately 1.0 g of kiwifruit pulp was used for extraction by grinding to a fine powder with liquid nitrogen and then transferring the powder to a 10 mL centrifuge tube. The powder was mixed with 5.0 mL of ice-cold 80% acetone containing 0.1% butylated hydroxytoluene (BHT). The mixture was incubated in the dark for 60 min until the pulp turned white. The mixture was then centrifuged at 12,000\(\times\)\(g\) for 10 min at 4 \({}^{\circ}\)C, and the supernatant was collected for use.  
Chlorophyll content was measured using 80% acetone solution as the blank control for zeroing, and the absorbance of the extracted solution was then determined by colorimetry at wavelengths of 663 nm and 646 nm in a 1 cm light diameter cuvette using a spectrophotometer (Thermo Scientific GENESYS 40, Waltham, MA, USA). Chlorophyll content was calculated as follows: Chlorophyll a = (12.21 A\({}_{646}\) \(-\) 2.81 A\({}_{646}\)) \(\times\) V/(W \(\times\) 1000); Chlorophyll b = (20.13 A\({}_{646}\) \(-\) 5.03 A\({}_{646}\)) \(\times\) V/(W \(\times\) 1000); Total chlorophyll = Chlorophyll a + Chlorophyll b. The values of A\({}_{663}\) and A\({}_{646}\) represent absorbance at 663 nm and 646 nm, respectively, and V and W indicate the total volume of the extract (mL) and the fresh weight of the sample (g). The chlorophyll content was expressed as mg g\({}^{-1}\).  
Total carotenoid content was determined based on the absorbance of the extract at a wavelength of 450 nm. Total carotenoid content was calculated as follows: Carotenoid = A\({}_{\text{A50}}\)\(\times\) 1000 \(\times\) V/(W \(\times\) 250). The value of A\({}_{\text{A50}}\) represents absorbance at 450 nm, and V and W indicate the total volume of the extract (mL) and the fresh weight of the sample (g), respectively. The carotenoid content was expressed as mg g\({}^{-1}\).  
The AsA content was measured following the methods described in a previous report, with some modifications [24]. Approximately 1.0 g of kiwifruit pulp was ground to a fine powder with liquid nitrogen and then transferred to a 50 mL centrifuge tube. The powder was mixed with 20.0 mL of a 0.05% (\(w/v\)) oxalic-acid solution. The mixture was incubated for 10 min and centrifuged at 10000\(\times\)\(g\) for 15 min. The supernatant was then collected. The supernatant was diluted with 0.05% (\(w/v\)) oxalic-acid solution to obtain a 0.5 mg/mLextract for the determination of AsA. The AsA content of kiwifruit was determined by high-performance liquid chromatography (HPLC) (Agilent HPLC 1100, Agilent, Palo Alto, CA, USA) using a C18 column (250 mm \(\times\) 4.6 mm, 5 \(\upmu\)m, Waters). The mobile phase consisted of 0.05% (\(w/v\)) oxalic-acid aqueous solution and methanol (98:2), the column temperature was 25 \({}^{\circ}\)C, and the flow rate was 0.7 mL/min. L-Ascorbic acid (SV8120, Solarbio, Beijing, China) was used as the standard. The AsA content was identified by comparing the retention times of the peaks with those of the external standard, and quantification was performed using standard calibration curves.
## 3 Results and Discussion
Both the baseline and DP-ECMS are run using the same traffic and SPaT patterns to ensure that the results are directly comparable. Considering that the vehicle in question has an electrified powertrain, it is important to keep track of the battery SOC. A common way to account for differences in the final and initial SOC is to apply a correction factor to fuel consumption based on the magnitude of the deviation [36]. However, this process can be imprecise and is difficult to properly apply to a large number of samples. To simplify the process, each trip was repeated 10 times and the FE (MPG) was computed for the duration of the 10 cycles. By doing so, the small deviations in the SOC are negligible over the energy consumed by burning fuel over 10 trips. Figure 13 shows the Monte Carlo simulation results with overlaid histograms for both DP-ECMS and the baseline along with the mean and standard deviation. Figure 11: SOC comparison for DP-ECMS and baseline. Figure 10: Fuel consumption comparison for DP-ECMS and baseline. The DP-ECMS method yields MPG values which range from about 18 to 28 MPG, while the baseline ranges from 16.1 to 25 MPG. The median bin for the baseline is 18.2 to 19.2 MPG, whereas for DP-ECMS, it is 20.2 to 21.3 MPG. DP-ECMS yields an average FE of 22.06 MPG, while the baseline is 19.96 MPG. The results show that, on average, the DP-ECMS provided a 10% FE increase over baseline. A histogram of the percent gain in FE is shown in Figure 14, which shows gains in FE for each trip. By plotting the percent gain, it can be seen that the algorithm always performs either the same as or better than the baseline. It is expected that there will be cases where DP-ECMS will have similar FE to the baseline. Since the biggest advantage of DP-ECMS is the use of look-ahead data, if there are cases where there is no traffic and all green lights, there will most likely not be much benefit to having look-ahead data. Likewise, if the traffic is so heavy that the vehicle is not able to leverage the future knowledge, it will behave similar to the baseline. Figure 12: EF comparison for DP-ECMS and baseline. Figure 13: Fuel economy distribution of DP-ECMS and baseline.
# The Noise-Reduction Characteristics of Microstructure of Dragonfly Wing Leading Vein
## 2 Bionic Modeling of Dragonfly Wing Microstructure
### Influence of the Overall Size of the Leading-Edge Microstructure on the Noise-Reduction Effect  
In this section, the overall size of the dragonfly wing leading-edge microstructure is explored. In the analysis of the leading-edge noise caused by the interaction between the incident turbulence and the leading edge of the wing, the bionic wing was simplified to some extent. This meant a rectangular plate with a length of 140 mm, a width of 45 mm and a thickness of 0.09 mm was chosen as the basic wing.  
The bionic wing was based on the basic wing with these serration microstructures. The OASPL between the basic wing and the bionic wing was compared. First, the leading-edge microstructure model of dragonfly wings was established in 1:1 ratio and then enlarged by 1 to 10 times and numbered as wing 1 to wing 10.  
The OASPL values of these wings in XFlow noise simulation are shown in Table 3. As seen in Table 3, the OASPL of the bionic wing 1 to wing 6 was obviously lower than the basic one, which means a certain noise-reduction effect induced by microstructure existed. While the OASPL of wing 7 to wing 10 was higher in numerical number than the basic wing. This shows that the leading-edge microstructure of the bionic wings had an influence on the noise-reduction effect, but different microstructure sizes would bring different acoustics effects. When bionic microstructure enlarged to a certain extent, it produced more noise with the increase of the overall size.  
Overall sound-pressure-level reduction (\(\Delta\)OASPL) was used studied to compare the noise-reduction effects of different bionic airfoils. Subtracting the \(OASPL_{Serr}\) from the \(OASPL_{BS}\) gives the overall sound-pressure-level reduction \(\Delta\)OASPL, the expression equation is as follows:  
\[\Delta\text{OASPL}=OASPL_{BS}-OASPL_{Serr} \tag{5}\]  
where \(OASPL_{BS}\) and \(OASPL_{Serr}\) are the OASPL of the basic wing and bionic wing with serration microstructure, respectively. Equation (5) can be used to calculate the OASPL reduction of different bionic airfoils compared with the basic one. The \(\Delta\)OASPL value of wing 1 to wing 6 is shown in Table 4. As can be seen from Table 4, the maximum \(\Delta\)OASPL 12.5 decibels was obtained from wing 4. The overall size range of the bionic leading-edge microstructure with the noise-reduction effect was as follows, according to relevant magnification factors. The cross-sectional edge length \(E\) of the triangular leading edge was 128.75-772.5 \(\upmu\)m; The length \(W_{1}\) was 125.5-753.0 \(\upmu\)m, height \(H\) was 42.7-256.2 \(\upmu\)m, and width \(T\) was 30-180 \(\upmu\)m of the intermediate short, serrated teeth (shown in Figure 3); The length \(W_{2}\) of the long-serrated teeth on both sided (shown in Figure 3) was 187.8-1126.8 \(\upmu\)m. Moreover, the range of height and width was the same as that of short, serrated teeth.
## 3 Antibiotic Resistance in Hospital and Municipal Wastewater
### Hospital Wastewater
[60] characterized the antibiotic resistance of a hospital wastewater using the shotgun metagenomic sequencing method. The HWW sewage effluent was mainly composed of resistance genes against aminoglycoside (_AAC(6') lb9, aa411_), macrolide (_meFA, mel_), cephalospororin and penem (_GES-5_). Shotgun metagenomics was extended by [PERSON] et al. [61] to study the AMR genes in the HWW treatment plants at a medical center in Daegu, South Korea, and the samples were reported to contain AMR genes corresponding to MDR (>53%), MLS (9%), bacitracin (4.4%), tetracycline (3.4%) and bactalam resistance (3.3%) genes in hospital wastewater. [PERSON] et al. [62] analyzed the AMR genes present in untreated wastewater samples of three different hospitals affiliated with Southwest Medical University in China, namely a general hospital, a TCM hospital and a standatology hospital using shotgun metagenomic sequencing methods. The resistance was observed to differ with respect to the type of hospital. Tetracycline-, penicillin-, tbramycin- and isomycin-resistant genes were highly abundant in wastewaters from general hospitals, whereas tetracycline, macrolide and bactrican resistance genes dominated the TCM hospital wastewater. The most prevalent ARGs found in the stimulatory hospital wastewater were bactrican, tetracycline, sulfonamide and cephalospororin resistance genes. A similar investigation by [PERSON] et al. [63] in wastewater samples of a general and an eye hospital in Zhejiang, China also revealed that there was a certain difference in the composition of AMR genes between general and eye hospital samples. While _tet39_ (tetracycline resistance gene) was the most abundant ARG in general hospital wastewater, _sull_ (sulfonamide) was found to be more abundant in eye hospital wastewater. [PERSON] et al. [64] also used shotgun metagenomics to assess the AMR profile in HWW in Jedda, Saudi Arabia, after the COVID-19 pandemic. It revealed the prevalence of carbapenem, macrolides, tetracycline and sulfonamides resistance genes in those samples. The most abundant ARGs were found to be _msrE, sul2_ and _tet 36_. Shotgun metagenomic analysis of wastewater samples from six Indian hospitals (general) exhibited ARGs against carbapenem, trimethoprim, and sulfonamide antibiotics in all the samples. In addition, for the first time, a mobile colistin resistance gene, mcr-5.1, was also detected in those wastewater samples [48]. More recently, dental hospital wastewater in China was also explored for the ARG profile by shotgun metagenomics [65]. The study claimed that a total number of 1208 types of ARGs, belonging to 29 antibiotic types/subtypes, were present in the samples.
# Memory Model for Morphological Semantics of Visual Stimuli Using Sparse Distributed Representation
## 2 Related Researches
### Computing Models for Memory Mechanism
SDR can embed huge amount of information by activating only a small number of neurons. In Equation (1), sparsity can be decided by ratio of activated neurons (\(w\)) to total neurons (\(n\)). \[sparsity=\frac{w}{n}. \tag{1}\]  
The capacity of SDR is defined by the volume of information in which SDR can embed, shown in Equation (2). \[capacity=\left( \begin{array}{c}n\\ w\end{array} \right)=\frac{n!}{w!(n-w)!}. \tag{2}\]  
For example, if total number of neurons is 16 and only 2 neurons are activated, sparsity of SDR can be decided as 0.125 and capacity of SDR is 120. If we increase total number of neurons to 256 and only 5 neurons of them are activated, sparsity is decreased to 0.0195 and capacity is considerably increased to 8,809,549,056. SDR examples of these 2 cases are illustrated in Figure 6.  
Each cell of SDR which can embed such huge volume of information has feature for delivering somewhat meaningful information. Based on this featur  
Figure 5: Structure of neocortex region of human brain [13; 15]. (**a**) Visual cortex regions such as V1, V2, and V4, (**b**) A small part of vertical section of neocortex sheet. Figure 6: Examples of SDR according to total number of neurons and the number of activated neurons. (**a**) An example of only 2 activated neurons among 16 neurons, (**b**) An example of 5 activated neurons among. a method for folding semantic information using SDR [13; 22]. According to Cortical.IO, semantic folding means the process for encoding words in a text document to sparse binary representation vector using distributed reference frame of semantic space. They provide it is possible to represent sentences or mutual semantic relations among words using semantic folding mechanism [22]. Figure 7 illustrates the concept of morphological semantic preservation scheme in this paper based on the idea of Cortical.IO. We can assume SDRs of images for number '0', '5', and '6' have morphological similarities since these images have common round shapes. These morphological similarities can be found in the logical operation result of the 3 SDRs of images for number '0', '5', and '6'.
# Cover the Violence: A Novel Deep-Learning-Based Approach Towards Violence-Detection in Movies
## 3 Experimental Results
### Comparative Analysis  
In this portion, we evaluate the performance of the proposed method with the state-of-the-art techniques using the same datasets. The existing bag-of-words (BoW) and support vector machine (SVM) algorithm was used for the violent scene detection [21]. Furthermore, they used the space time interest points (STIP) to detect corner points in an image and then extracted histogram oriented gradient using MOSIFT technique, and the bag-of-words approach was adopted for text retrieval. They used the Violence in Movies dataset and achieved 89.5% accuracy, while we achieved 99.5% accuracy on the same dataset. The second method is based on Bag-of-word framework using Violence in movies and the Hockey Fights dataset. They extracted features from the motion blobs in video sequences and trained the Random Forest on these motion features to detect violence, which achieved 96.9% on Violence in Movies and 82.4% accuracy on Hockey Fights. The third article extracted the violent descriptors using a statistical method. They trained the linear SVM on statistical features for violent and non-violent scenes using the hockey fight dataset, they achieved 82.9% accuracy [14]. The fourth comparison method is the content based method using a VSD dataset, which achieved 96.9% accuracy, while the proposed method achieved 97.0% [33]. The last method used for comparison fused different features with a classifier, which performed better than our proposed method in terms of accuracy, but its time complexity was high. They achieved a 99.9% accuracy on Violence in Movies, which is slightly greater than ours, which is 99.5%. On the other hand, it achieved 95.5% accuracy on the Hockey Fight dataset, which is better than ours, because we obtained 87% accuracy on the same dataset. The proposed method achieved better performance on all the datasets with a high accuracy and a lower time complexity. The experimental results demonstrated that our fine-tuned model achieved better results as compared to the state-of-the-art methods. We also verified the compatibility of our framework in different domains, such as surveillance and movies. The rest of the methods in the literature are limited to only a single domain, such as movies or surveillance. The malleability of our framework is due to the usage of effective CNNs for the final output prediction of the input frames. The details are given in Table 1.  
Figure 7: Performance evolution of the proposed model.