esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Detection of the Volatile Compounds Using GC-MS  
The detection and analysis of volatile compounds were conducted using a headspace solid-phase micro-extraction (HS-SPME) technique coupled with gas chromatography-mass spectrometry (GC-MS). Parameter settings for the GC-MS and SPME measurements were modified based on [17]. Each sample suspension, weighing 4 g, was dispensed into an 8 mL SPME headspace vial, mixed with 1 \(\mu\)L of 2-methyl-3-heptanone (1 \(\mu\)g/\(\mu\)L) as internal standard, and then sealed with a polytetrachloroethylene spacer. Incubate at 20 \({}^{\circ}\)C for 30 min to reach headspace equilibrium prior to injection, a 50/30 \(\mu\)m DVB/CAR/PDMS composite extraction head was inserted into the headspace vial and allowed to adsorbfor 40 min at 60 \({}^{\circ}\)C, the entire SPME process was 70 min before injection into GC-MS. Subsequently, the fiber head within the extracted composite extraction head was expelled and desorbed for 5 min at 250 \({}^{\circ}\)C before reinserting into the GC inlet. The HP-INNOWax highly polarized column (30 m \(\times\) 0.25 mm, 0.25 mm) was used for the separation. The column temperature was initially maintained at 40 \({}^{\circ}\)C for 3 min, then ramped up to 80 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min and held for an additional 3 min, followed by an increase to 230 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C/min, and finally held at 230 \({}^{\circ}\)C for 5 min. High-purity helium (\(>\)99.999%) was utilized as the carrier gas, flowing at a 1.0 mL/min rate. Mass spectrometry was conducted in electron ionization (EI) mode at 70 eV, with the ion-source and transmission-line temperatures set at 240 \({}^{\circ}\)C and 230 \({}^{\circ}\)C, respectively. The mass range that was scanned ranged from 40 to 450 \(m/z\).
# A Review of Abnormal Crowd Behavior Recognition Technology Based on Computer Vision
## 4 Research Status of Abnormal Crowd Behavior Recognition
### Traditional Methods
#### 4.1.2 Methods Based on Motion Features  
The methods based on motion features focus on analyzing the motion features of individuals or groups in the video, such as speed, direction, acceleration, and trajectory, to identify abnormal behaviors.  
Among them, the motion behavior of the crowd can be effectively captured by the optical flow that changes continuously over time. Optical flow [34] is a vector field that describes the motion information of objects in an image sequence, and it reflects the position change in each pixel point in the scene between adjacent frames. In high-density crowd scenes, the optical flow field can reveal the movement direction and speed distribution of individuals or groups, thereby identifying abnormal movements contrary to the normal behavior pattern, such as reverse movement and fast running.  
Traditional motion description techniques are mostly based on the velocity information of optical flow, but the acceleration information often contains more abundant motion details. Especially when describing complex motion patterns, it can provide the information missed by the velocity descriptor and help to better understand the motion pattern. [PERSON] et al. [35] studied an acceleration feature descriptor to improve the accuracy of abnormal behavior detection in videos. The process is shown in Figure 2. First, the optical flow field information of each frame is extracted from the video. The inter-frame acceleration is calculated through the optical flow information of two consecutive frames. The acceleration histogram feature is constructed to form the acceleration descriptor (HAVA). Using the video training set containing only normal behaviors, the normal motion pattern is learned through the energy-optimized Restricted Boltzmann Machine (RBM) model [36]. In addition, [PERSON] et al. [37] proposed a motion descriptor of stripe flow acceleration (SFA), and the process is shown in Figure 3. Stripe flow is a motion representation method that can effectively capture long-term spatiotemporal changes in crowded scenes. Compared with traditional optical flow, it performs better in terms of accuracy and robustness. By introducing stripe flow acceleration to explicitly model motion information, the spatiotemporal changes in crowded scenes can be accurately represented.  
Figure 3: The framework of the SFA method.  
Figure 2: The framework of the acceleration optical flow method. Note, blue color indicates the period from \(t\) to \(t+k\) and is used to extract the acceleration descriptors of the crowd. Yellow represents the visible layer of the restricted Boltzmann machine, and green represents the hidden layer of the restricted Boltzmann machine, which is used for behavior feature learning.
# Synergistic Potential of Optical and Radar Remote Sensing for Snow Cover Monitoring
## 4 Results
### Recent Impacts of Climate Change on Snow Cover  
Figure 7 shows the temporal evolution of SCA for 5-year periods in terms of the average PSCPI for the entire domain of Iberian mountains. There exists a significative general trend in temporal inter-lustrum variability in the average PSCPI in winter months (December, January, and February). In these months, the first two lustra presented the greatest average PSCPI values, except in the case of February, where the third lustrum generally exhibited the highest average PSCPI of the time series. The last two lustra (2011-2015 and 2016-2020) over the 20-year period of study generally recorded the minimum average PSCPI values in all months of the snow season. Here, we plot the summer months for continuity on the graphs; however, they are not considered in the trend analysis.  
Negative trends in SCA were observed over the 20-year period of study in the mountain ranges of the Iberian Peninsula (Table 1). Excluding April (in the Central System, Cantabrian System, and Iberian Range), February (in the Cantabrian System) and March (in the Pyrenees) negative rates in SCA were found in the snow season months. The largest decline in SCA rates occurred during December and January (Table 1). On the contrary, the smallest decline in SCA took place during April. In general, greater negative rates in SCA were observed in the Sierra Nevada over the snow season in relation to the other mountain ranges, except in December and April where the Central System (\(-\)4.45%/lustrum) and the Pyrenees (\(-\)0.99%/lustrum) revealed the greatest declines, respectively.  
Figure 6: (**a**) Linear correlation between latitude and snowline elevation based on different thresholds (THR) of the average PSCPI over the main snow-dominated Iberian mountains; (**b**) Determination coefficient of the simple linear regression between latitude and snowline elevation associated with the examined average PSCPI thresholds.
## 3 Results and Discussion
### Total Phenolic Content (TPC), Antioxidant Activity, and Antitradical Activity
Moreover, high levels of total phenolic compounds and antioxidant activity were achieved by the replacement of hops with rubim (_Leomurus sibiricus_ L.) and mastruz (_Chenopodium ambrosioides_ L.) [44]. Most similar studies have analyzed the polyphenols of carob syrups and carob pulp extracted usually in various organic solvents rather than in water, whereas in this study the polyphenols were extracted during the traditional preparation of caro syrup with water. Also, although a thermal treatment was applied, the total phenolic content was found to be high in the final product, whereas other studies had observed degradation of the phenolic content of carob syrups [36]. Nevertheless, some variations were observed after the second fermentation, such as a decline in antioxidant and antiradical activity; however, the overall activities remained significantly higher than the control beer (without carob). Similar results were obtained by other researchers ([PERSON] et al., 2015), who have studied the antiradical and reducing potential of commercial beers during storage and observed a decrease in antioxidant activity as a result of storage, occurring mainly after the initial 4-week storage period. A possible explanation for this decrease could be that the level of oxygen is not easily controlled; therefore, the beers should be consumed rather shortly, and, moreover, the produced beers are unpasteurized. Additionally, [PERSON] et al. (2012) observed 10% lower antioxidant activity in fresh cloudy wheat beer over the first 18 days of storage at 20 \({}^{\circ}\)C.  
Figure 4: **(A)** Total phenolic content of samples (expressed as mg GAE/100 mL). (**B**) Antioxidant activity (expressed as mg Fe\({}^{+2}\)/100 mL). (**C**) Antiradical activity (mg TE/100 mL). Standard dark ale beer with 6% vol without carob used as control (**B**). Strong dark ale carob beer with 8% ABV (CB1) and strong dark ale carob beer with 10% vol (CB2) at the beginning of fermentation (0 d), after the 1 st fermentation (30 d) and 2 nd fermentation (60 d). Bars bearing different letters are significantly statistically different (\(p<0.05\)) (a \(<\) b \(<\) c \(<\) d \(<\) e \(<\) f \(<\) g \(<\) h). Furthermore, a strong relationship was observed between radical scavenging activity measured by ABTS assay and ferric-reducing/antioxidant power assay (FRAP) results, with a correlation coefficient R\({}^{2}\) of 0.94 (_p_ < 0.05). The correlation coefficient R\({}^{2}\) (_p_ < 0.05) between the total polyphenol content (TPC), ferric-reducing/antioxidant power assay (FRAP), and radical scavenging activity (ABTS assay) was found to be 0.77 and 0.68, respectively, indicating that polyphenols contributed significantly to the reducing/antioxidant power of beers [45; 46].
## 2 Materials and Methods
### eBPMN  
Several simulation approaches and techniques have been provided in the literature. However, the effective use of business process simulation is still limited in practice for many reasons, as addressed in References [7; 16]. To overcome these limitations, a couple of studies have proposed extensions of BPMN with non-functional requirements. Two types of solution have been proposed; the first type proposed a BPMN extension by adding new elements to its metamodel-like table format, new decorators as described in References [17; 18; 19] which alter the specificities of BPMN. The second type proposed the use of text annotation to represent additional needs, thus respecting the extension instructions provided in the BPMN specifications [20; 21]. This paper describes an enhancement of the second type of proposed solution to maintain the benefits of BPMN.  
Among the proposed tools, this work enhanced the eBPMN language [7]. [PERSON] et al. (2011) [20] introduced PyBPMN, a lightweight extension of BPMN that includes performance and reliability measurements using text annotations. Moreover, they proposed an approach merging the modeling and simulation phases at the time of design by introducing their eBPMN language. eBPMN  
Figure 1: MDSEA methodology.  
is a language based on a <<model-driven>> method (model-to-model and model-to-text transformations) that automatically builds executable simulation code from a BPMN model [7]. The <<model-driven>> business process simulation method starts with a BPMN model that includes both functional and non-functional measurements using text annotation. The model-to-model (M2M) transformation takes as input the annotated BPMN model and yields as output the corresponding PyBPMN model. Then, the model to text (M2T) transformation, takes as input the PyBPMN model serialized into XMI and yields as output the corresponding eBPMN code. The M2M transformation is implemented by the use of OMG's language; QVT-O and M2T transformation is implemented by the use of Acceleo/OCL. More details are described in Reference [7].  
The advantages of this approach are to:  
* Merge modeling and simulation in the same step using a <<model-driven>> method to automatically build executable simulation code from BPMN;
* Guarantee the compatibility of the model by not modifying the BPMN metamodel and implementing eBPMN according to the BPMN execution semantics using token concepts;
* Allow non-functional simulation through process resources by using text annotation;
* Take into consideration composite resources;
* Handle non-predictable variables by using pseudo-random number generation and exponential distribution approximation.  
This work improved the eBPMN resource approach [22] by distinguishing a resource's type as defined by MDSEA methodology in the design and simulation phases, as each resource has its own characteristics, requirements, and constraints in a business process.
# Dynamic Collaborative Optimization Method for Real-Time Multi-Object Tracking
## 2 Related Works  
### Small Object Detection and Feature Enhancement  
Small object detection has always been a challenging problem in the field of computer vision. Traditional methods mainly build multi-scale representations through Feature Pyramid Networks (FPNs) [13], but their simple feature fusion methods struggle to preserve the discriminative information of small objects. [PERSON] et al. [14] proposed PANet, which enhances feature fusion by adding a bottom-up path, but it still suffers from insufficient feature extraction for small objects. [PERSON] et al. [15] introduced the Atrous Spatial Pyramid Pooling (ASPP) module, using convolution kernels with different dilation rates to capture multi-scale context, but it was not specifically optimized for small object features.  
In recent years, attention mechanisms have emerged as a powerful approach for feature enhancement. [PERSON] et al. [16] developed CBAM (Convolutional Block Attention Module), integrating channel and spatial attention to improve feature representation, yet its fixed weight strategy limits adaptive adjustment based on object size. [PERSON] et al. [17] designed the ECA (Efficient Channel Attention) module, efficiently modeling inter-channel dependencies through one-dimensional convolution, but they overlooked critical spatial dimension information. Diverging from these approaches, the proposed MS-FAE module synthesizes multi-level features and introduces a Small Object Adaptive Attention Mechanism (SAAM), dynamically modulating channel and spatial attention weights according to object dimensions. Moreover, by implementing deformable convolution and dynamic anchor box compensation strategies, the module significantly enhances small object feature extraction accuracy and representational capabilities.  
Unlike previous research, which often relied on single-modal data or traditional feature engineering methods with limited adaptability, our framework capitalizes on deep learning's unique ability to automatically extract hierarchical features from complex multi-modal data. This strength is exemplified by its effectiveness, similar to how deep learning enables the precise detection of exterior cladding materials from street view images as shown in related empirical studies.
## 2 Antenna Designing and Operation Mechanism
### Design of Band Notch Elements
#### 2.2.2 WLAN Band Notch Element Design  
The FCC allocated 5-6 GHz bandwidth for the WLAN within the UWB band. The interference mitigation for the WLAN band is required within UWB. An inverted U-shaped slot was etched in the radiating patch to overcome the interference for the WLAN band. The inverted U-shaped slot of length \(U_{u}=8\) mm is calculated using the expression Equation (3) with the changed length, and the width of the inverted U-shaped slot \(U_{u}\) is 0.2 mm in order to produce the notch at the WLAN band. The length of the inverted U-shaped slot is:  
\[f_{WLAN}=\frac{c}{4U_{u}\sqrt{\epsilon_{rff}}} \tag{3}\]  
\[U_{u}=L_{3}+L_{4}+W_{3} \tag{4}\]  
where \(f_{WLAN}\) is a center notch frequency for WLAN, \(U_{u}\) is the total length of the U-shaped slot as provided in Equation (4). Figure 4 shows the suppression in the WLAN band. The figure shows that the length of an inverted U-shaped slot and the nothing frequency are in inverse relationship. For 5.2 GHz, the length of the U-shaped slot is \(U_{u}\) is 8 mm. The length of the \(U_{u}\) slot increases 11% from 8 mm to 9 mm will create the decrease in frequency of 4% as the notching frequency reduces from to 5.2 to 5 GHz.
# mGEODAR--A Mobile Radar System for Detection and Monitoring of Gravitational Mass-Movements
## 2 Materials and Methods
### Digital Signal Processing  
The raw data stream from the ADC consists of two channels which contain the deramp time-domain signal and a marker pulse signal. The marker pulse is used to split the deramp signal into each chirp pulse waveforms, thus the time-domain signal is arranged into an 3D array with the axes hold the fast-time (time along the chirp pulse), the slow-time (centre time of all chirp pulses) and the chirp type (up- and down-chirp). [PERSON] et al. [9] covers this splitting and reshaping process in detail. A time-domain example of the deramp signal is shown in Figure 4A,C. A spectral analysis with the fast-Fourier transform converts the fast-time into range for each chirp pulse according to Equation (1). The magnitude of the amplitude spectrum for a loop cable measurement is shown in Figure 4B,D. Note, any frequency shift due to the Doppler velocity of a target is neglected here due to the problems arising from distributed targets. A cosine-tapered window function is applied prior to the range analysis to taper each side of the chirp pulse to zero. Some spurious signals appear as discrete non-coherent regions in the time-domain data which can be notched out with cosine tapering as well. Such spurious signals are most likely frequency leakage in the hardware or external in-band signals, and such digital windowing has been successfully applied in the previous GEODAR radar processing chain (see [PERSON] [16] for an in-depth description of the complete data processing). Part of the  
Figure 3: Amplitude response of the 4-stage active baseband filter in mGEODAR at low gain (**left A**) and high gain (**right B**) setting in black. Stage 1 and 2 define the bandpass shape and stage 3 and 4 cause a strong roll-off to high frequencies. The zero crossing in high gain setting is at 20 m and the maximum pass band ranges roughly from 800 m to 3000 m.  
optimization in Section 3 is to prevent signal degradation due to these spurious signals. For the detection mode, the spectral data of the 25 pulses in one burst are averaged in order to increase the signal-to-noise ratio.  
The slow-time processing, also known as moving target identification (MTI), compares the response of several consecutive chirp pulses. A high-pass filter along the slow-time axis for each range bin enhances changes between the pulses while suppressing static regions. The exact filter properties depend on the target, for avalanches usually a large passband is found optimal [5]. However, very distinct flow patterns and flow regimes in avalanches could be enhanced by using narrow bandpass filters [17]. The same holds true for discrete point targets and here the MTI filter is a digital finite impulse response bandpass filter with normalized cut-off frequencies of \([0.025,0.4]\) and a length of 151 pulses. The lower cut-off basically removes levelling artefacts from range normalization, while higher cut-off removes high-frequency noise such as pulse-to-pulse variations.
# Compensation for Group Velocity of Polychromatic Wave Measurement in Dispersive Medium
###### Abstract  
The estimation of instantaneous frequency (IF) method is introduced to compensate for the group velocity of electromagnetic wave in dispersive medium. The location of the reflected signal can be obtained by using the time-frequency cross correlation (TFCC), following which it is used to extract the transmitted signal from the total signal acquired. The signal propagated in the dispersive medium is attenuated and distorted by the attenuation characteristics, which depend on the frequency of the medium. By using the IF curve calculated for the transmitted signal, the changed center frequency and time terms can be obtained. The obtained terms are used to compensate for the group  
velocity also becomes large. Especially, in case of submarine cable lengths of several hundred km, speed compensation is essential. Our research group has been investigated the compensation for group velocity [8]. The compensation method proposed in the previous method [8] does not consider the sweep rate of the reflected signal to be changed. That is, since the frequency components included in the chirp signal have different velocities, compensation is performed without taking into account dispersion, which causes an measurement error.  
In this paper, we propose a method for compensating the group velocity of poly-chromatic wave in a dispersive medium based on instantaneous frequency (IF) estimation. Using TFCC, the location of transmitted signal is obtained and the signal is extracted from the total acquired signal. IF curves of incident and transmitted signal are derived by using the phase unwrapping process. The shifted center frequency and shortened time duration of transmitted signal are obtained based on the Fast Fourier Transform (FFT) and estimation of the IF curve. The group velocity compensation is carried out through the derived terms.
## 3 Analysis of Torque
### Analysis of the MR Torque Transmission Performance of the Transmission Device  
When the inner and outer diameters of the MRF working gap are 35 mm and 65 mm, respectively, the thickness of the working gap \(h\) is 1 mm, the viscosity \(\eta\) is 2.9 Pa-s, the speeds of the input and output shafts are 30 rad/s and 29 rad/s, respectively, \(n_{\mathrm{m}}=30\), and \(r_{\mathrm{a}}=0.5\) mm, the equivalent working length of the unilateral disc increases by \(l_{\mathrm{g}}=17.124\) mm.  
In order to highlight the beneficial effects of the curved disc-type MR drive, the points with the same magnetic flux density within the working gap were selected for equivalence. The MR torque of the curved disc-type drive was compared with the experimental torque values of the existing planar disc-type drive [20], and the results are shown in Figure 12.  
From the data shown in Figure 12, it can be observed that the numerical and experimental torques of the planar disc-type drive are 43.626 N\(\cdot\)m and 44.944 N\(\cdot\)m, respectively, when the current is 1.5 A. The difference between the finite element numerical and experimental values is 3.02%, and the accuracy of the numerical calculation is within an acceptable range. At this time, the torque of the curved disc-type drive is 74.805 N\(\cdot\)m, which is 66.44% higher than that of the planar disc-type drive. When the current is 3 A, the torque of the curved and planar disc-type drives is 82.627 N\(\cdot\)m and 46.430 N\(\cdot\)m, respectively, and the torque is increased by 77.96%. The curved disc-type MR drive significantly improved the  
Figure 12: Numerical and experimental data on MR torque.  
transmission performance compared with the traditional planar disc-type MR drive, and the torque improvement effect became more obvious as the current increased.