esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multi-Loop Vehicle-Counting Method under Gray Mode and RGB Mode
###### Abstract  
With the rapid development of road traffic, real-time vehicle counting is very important in the construction of intelligent transportation systems (ITSs). Compared with traditional technologies, the video-based method for vehicle counting shows great importance and huge advantages in its low cost, high efficiency, and flexibility. However, many methods find difficulty in balancing the accuracy and complexity of the algorithm. For example, compared with traditional and simple methods, deep learning methods may achieve higher precision, but they also greatly increase the complexity of the algorithm. In addition to that, most of the methods only work under one mode of color, which is a waste of available information. Considering the above, a multi-loop vehicle-counting method under gray mode and RGB mode was proposed in this paper. Under gray and RGB modes, the moving vehicle can be detected more completely; with the help of multiple loops, vehicle counting could better deal with different influencing factors, such as driving behavior, traffic environment, shooting angle, etc. The experimental results show that the proposed method is able to count vehicles with more than 98.5% accuracy while dealing with different road scenes.  
vehicle detection; different color modes; road background; vehicle counting; primary and secondary loops +
Footnote †: journal: Applied  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
27 June 2021  
23 July 2021  
25 July 2021  
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**Publisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.  
first, and further detection is operated under RGB mode, which could greatly improve the integrity of vehicle detection. Then, the detection information is converted into counting information based on multiple loops to better deal with different influencing factors, such as driving behavior, traffic environment, shooting angle, etc. The counting information is produced per lane.  
The rest of the paper is structured as follows: Section 2 introduces related work in vehicle counting. In Section 3, we present a vehicle-detection method under gray mode and RGB mode to improve the integrity of vehicle, and a multi-loop vehicle-counting method to count vehicles with a higher accuracy. The experimental results obtained with the proposed method are presented in Section 4, while a discussion is given in Section 5. Finally, the conclusion is provided in Section 6.
# Shading by Overhang PV Collectors
## 2 Materials and Methods
### Shadow on a South Facing Walls Cast by a Horizontal Pole  
A horizontal pole bc of length H attached perpendicular to a wall, is shown in Figure 2. The right edge \"c\" casts a shadow on the wall by the solar rays at point \"a\".  
From Figure 2 it follows:  
\[F_{x}=K\sin\gamma,\quad F_{y}=J\sin\alpha \tag{1}\]  
\[J=K/\cos\alpha,\quad H=K\cos\gamma \tag{2}\]  
By substituting Equation (2) into Equation (1), we get the coordinates of the shadow point \"a\", i.e., the shadow variation on the wall during the day maybe calculated:  
\[F_{x}=H\tan\gamma \tag{3}\]  
\[F_{y}=H\tan\alpha/cos\gamma \tag{4}\]  
where  
\[\sin\alpha=\sin\phi\sin\delta+\cos\phi\cos\delta\cos\omega \tag{5}\]  
\[\cot\gamma=[\sin\phi\cos\omega-\cos\phi\tan\delta]/\sin\omega \tag{6}\]  
and  
\[\omega=15T-180 \tag{7}\]  
Figure 1: Overhang photovoltaic (PV) collector (blue) on building facade.  
Figure 2: Horizontal pole attached perpendicular to a wall.  
\(\alpha\) is the sun elevation angle, \(\gamma\) is the sun azimuth angle with respect to south, \(\omega\) is the solar angle (\(\omega=0\) noon, \(\omega<0\) morning and \(\omega>0\) afternoon) and \(T\) is the solar time. The solar inclination angle \(\delta\) is given by:  
\[\delta=23.45\sin\frac{(284+n)}{365} \tag{8}\]  
Figure 3 shows the shadow component,\(F_{x}\) and \(F_{y}\), of a pole attached prependicular to a wall at the origin 0,0,0 calculated according Equations (3)-(8) for Tel Aviv (latitude \(\phi=32^{\circ}N\)) for the months December to April on 21 st day. The arrows indicate the shadow length at given hours beforenoon. On vernal equinox (\(\delta=0^{0}\)) the \(F_{y}\) component is constant during the day. In afternoon hours, the shadow is symmetrical with respect to the Y axis.
# A Review of Oil Spill Remote Sensing
## 2 Detection and Mapping of Oil on a Water Surface
### Use of Optical Techniques  
Oil shows only minor optical properties in the region from the ultraviolet to near infrared. These are differential reflectance, and absorbance between oil and water in the given spectral region. These differential factors vary with oil type, degree of oil evaporation, weather conditions (including air moisture content), and sun illumination. Detailed oil/water spectral differentials have not been determined across the entire visible spectrum [3]. One could not categorically identify oil using only optical data in the visible region, especially if the location of the oil spill were unknown.
# A Progressive Semantic-Aware Fusion Network for Remote Sensing Object Detection
## 3 Proposed Method
### Shallow Detail Aggregation Module
Compared to traditional deep convolutional stacks, this architecture significantly reduces computational overhead while maintaining detection accuracy, which is crucial for large-scale remote sensing applications that demand both speed and efficiency. While FPN and its variants often suffer from the loss of fine-grained textures due to repeated downsampling and abstract semantic transformations, our SDAM addresses this limitation by introducing skip connections that enable contextual re-aggregation. This mechanism enhances the preservation of spatially localized information and strengthens detail representation. Consequently, SDAM effectively mitigates the semantic degradation  
Figure 3: Structure of shallow detail aggregation module. of shallow features during deep processing, achieving a balanced integration of local detail awareness and global semantic understanding, which is particularly suited for detecting small targets in cluttered remote sensing scenarios.
# Coarse-Fine Combined Bridge Crack Detection Based on Deep Learning
## 3 Coarse-Fine Combined \"Double Detection + Single Segmentation\" Fine Crack Detection  
### Ideas for Fine Crack Detection  
To address the problem of incomplete detection and segmentation of bridge cracks due to the complex background and small crack proportion in the actual bridge crack images, an anti-interference \"double detection + single segmentation\" coarse-fine combined bridge crack detection method based on deep learning was proposed.  
The three-step method of initial crack detection, fine crack detection and fine crack segmentation is adopted to make the detection results more perfect and accurate. It can effectively solve the problems of false detection, missing detection and difficult identification in crack detection and improve measurement accuracy and detection efficiency. First, the initial crack detection network based on deep learning was used to detect the original image and obtain a set of rectangular boxes that could contain cracks. The initial detection could eliminate the influence of the bridge crack background on the crack detection results and quickly screen the areas that could contain cracks, thus reducing the computational effort of the subsequent crack detection. The deep learning-based fine crack detection network was then used in each rectangular box to perform fine crack detection and localisation of the initially detected crack area to obtain a more accurate crack location. Fine crack detection could further identify the crack accurately, eliminate false detection and missing detection and improve crack detection accuracy. Finally, the fine-detected cracks were segmented  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**SSNMs** & **Learning Frameworks** & **AC (\%)** & _p (\%)_ & **R (\%)** \\ \hline U-Net & PyTorch1.2.0 & 98.37 & 89.11 & 90.28 \\ PSPNet & TensorFlow2.2.0 & 97.86 & 85.62 & 87.86 \\ DeepLabV3+ & PyTorch1.2.0 & 98.20 & 87.58 & 90.12 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental results of the SSNMs.  
using image semantic segmentation. This completed the fine detection of the cracks. The fine segmentation of the bridge cracks could accurately segment and extract the crack contour of the accurately identified crack area and provide accurate data for quantifying the crack width, which was conducive to improving the measurement accuracy of the crack width and facilitating further safety analysis and evaluation of the bridge structure. The specific technical route is shown in Figure 9. Through the \"double detection + single segmentation\" coarse-fine combined detection of bridge cracks, the method can resist some complex background interference, has good anti-interference performance and can improve the detection accuracy of cracks.
# From Brown-Field to Future Industrial Networks, a Case Study
## 3 Case Study: Iggesund Paperboard
### Traffic Structure and Communication Patterns  
The network comprises 337 devices, and 6,554,498 recorded transmissions were studied for illustrating the results of this experiment. The collected data consist of both IT and OT traffic, with statistics summarized in Table 1. Figure 2a illustrates the connectivity structure of the brown-field that represents the communication between network components; IT and OT traffics are color-coded in blue and orange, respectively. A larger number of packets are transmitted between devices belonging to each of the traffics. Six communication clusters comprise the network, and devices communicate with 1 to 108 peers. That is, the sub-systems have a maximum of 108 communication destinations for both IT and OT. To see the dynamics of the traffic, a 25-min window is illustrated in Figure 3. As expected, OT traffic shows a less volatile data transmission pattern compared to IT traffic. The IT traffic comprises the majority of the transmitted packets as well as the larger bandwidth consumption. The ratio between the number of transmitted packets and the transmitted data indicates that, on average, larger packets belong to IT.  
Figure 2: Connectivity structure graph: (**a**) brown-field case study and (**b**) literature [7].  
The previous knowledge about the network configuration and structure identifies 9 VLANs, amongst which two are dedicated to IT and seven to OT. The number of devices in each VLAN varies from 4 to 100. Studying the data set revealed 7205 packets with missing VLAN tags, and further analysis shows they belonged to OT traffic. Moreover, the traffic flows of two of the VLANs traffices flagged as OT show communication traces with IP addresses that were associated with the IT VLANs. In other words, the IT and OT traffics are not isolated in this network, and there are communication clusters with both IT and OT traffics.
## 3 Experimental Setup and Offline DSP  
### Experimental Setup  
Figure 2a presents the experimental setup for a 100 Gb/s PAM-4 transmission system based on SOA booster and pre-amplifier at O-band. On the transmitter side (Tx), the 50 Gbaud PAM-4 signal is uploaded into an arbitrary waveform generator (AWG, Keysight M8196A, Santa Rosa, USA) running at 92 GSa/s with 32 GHz 3-dB bandwidth and then amplified by a 55 GHz electrical amplifier (EA) to drive a 25 Gb/s EML operating at a center wavelength 1304.51 nm. The output power of the EML is about 4.3 dBm. To support the 29 dB power budget required in IEEE PR-30, an SOA booster (Aeon SAO11b) working at a bias current of 150 mA is utilized to amplify the launch power, which has a small-signal gain of 14.4 dB and noise figure of 5.9 dB. In order to avoid deep saturation, a variable optical attenuator (VOA) is placed after EML to adjust the input power of the SOA booster to 2 dBm, where a maximal launch power of 12 dBm is obtained. The optical signals are then transmitted over 25 km standard single-mode fiber (SSMF). VOA-2 is placed before and after fiber transmission for upstream and downstream, respectively, to imitate the function of the splitter.  
Figure 1: Process of LUT generation.  
On the receiver side (Rx), a 35 GHz PIN-PD (Newport 1474-A) is applied for optoelectronic conversion. To further improve the power budget, an SOA pre-amplifier SOA-2 (Aeon SAO29p) is adopted before direct detection. The VOA-3 is used to attenuate the output of SOA-2 to fix the input power of PIN to 0 dBm. The output signal of PIN is captured by an 80 GSa/s real-time digital storage oscilloscope (DSO) with a 33 GHz bandwidth and processed by offline DSP. The measured frequency response of the whole system is shown in Figure 2b, and the 3-dB bandwidth is about 19 GHz.
## 2 Materials and Methods
### HYSPLIT Model  
The HYSPLIT model represents a sophisticated computational framework, collaboratively crafted by the National Oceanic and Atmospheric Administration (NOAA) and the Australian Bureau of Meteorology. This model excels in calculating and scrutinizing the trajectories, deposition patterns, and dispersion dynamics of air masses. It enables the analysis of input fields that encapsulate a myriad of meteorological variables and diverse pollutant categories, thereby offering valuable insights into the emission sources of specific substances [54,55]. This model employs the Lagrangian method to compute advection and diffusion processes. Its notable advantage lies in its capacity to directly trace the trajectories of air masses or particles. Such a characteristic yields a compelling visual representation, proving particularly beneficial when examining the transmission dynamics and source attribution of atmospheric pollutants.  
In this investigation, the urban core of Chenzhou (25.41\({}^{\circ}\) N, 113.06\({}^{\circ}\) E) was identified as the starting point for trajectory simulations. Utilizing the TrajStat software, (Version 1.5.3) a comprehensive array of analyses was conducted, including backward trajectory computations, clustering statistics, potential source contribution factor analysis (PSCF), and concentration weight analysis (CWT). To intricately delineate the flow dynamics of air masses between regions, whilst mitigating the effects of near-surface friction, the simulation's initiation altitude was established at 500 m. Commencing at 0.00 h each day, the simulation unfolds over a span of 24 h. The clustering of air mass trajectories across various seasons is ascertained via cluster analysis [56]. Subsequent to this, pollutant concentration data corresponding to distinct seasons are amalgamated for in-depth statistical and analytical computations.
# Measurement and Modeling of Narrowband Channels for Ultrasonic Underwater Communications
## 3 Ultrasonic Measurements for UAC
### Channel Measurements Procedure  
The results presented here are part of a measurement campaign in the framework of the underwater communication experiments (UCEX), which is a multi-year measurement project of the company SAES and the Universidad de Malaga. This campaign was carried out in the Mediterranean Sea in La Algameca Chica (Cartagena, Spain), in shallow waters with a sandy seabed (and depths from 14 to 30 m approximately). The measurements presented here were registered during the same day in November 2013 and with smooth sea conditions (World Meteorological Organization sea state Code 2, waves of less than 0.5 m). The different link distances tested between the transmitter and the receiver were 50, 100 and 200 m; and the projector and the hydrophone were suspended from anchored boats with different cable lengths to reach a depth of 3, 6 and 9 m.  
The measurement setup used for the UCEX is sketched in Figure 2, where a picture of the equipment employed at the receiver is also shown. The setup comprises: two laptops (for signals control and storage and off-line signal processing for monitoring purposes); two acquisition modules, IOtech Personal DAQ3000 (with 16 bits of resolution and a 1-MHz sample rate, which were used as a digital-to-analog converter at the transmitter and as an analog-to-digital converter at the receiver); Bruel & Kjaer 2713 power amplifier and Bruel & Kjaer hydrophone 8105 at the transmitter; Reson VP2000 voltage preamplifier EC6081 (with a 1-MHz bandwidth) and Reson TC4032 low noise hydrophone at the receiver.  
The equipment allows performing accurate noise and channel measurements in the ultrasonic band. The sounding signals were sinusoids of constant amplitudes and frequencies of 32, 64 and 128 kHz. The received signals were recorded during a time window of 60 s with a 1-MHz sample rate. Afterwards, the records were digitally post-processed, applying first a 400-Hz bandwidth bandpass filter centered at the frequency of the transmitted sinusoid. The function of the bandpass filter was to attenuate the out of band interference and noise. Its bandwidth was set to a value larger than the expected Doppler spread. This way, the spectral spreading caused by the channel on the sounding signal was not distorted.
# Research on the Multi-Angle Seismic Spectrum Response Law of Auxiliary Feedwater Electric Pumps
## 2 Finite Element Modeling  
Figure 1 illustrates a horizontal auxiliary feedwater electric pump characterized by safety class RCC-P 3, code class RCC-M 2, and seismic category 1A. The drive side is connected to the motor shaft via a coupling, and the entire pump assembly is affixed to a concrete foundation through the pump base.  
The materials employed for each component of the auxiliary feedwater electric pump, along with their respective mechanical properties, are detailed in Table 1.  
Using ABAQUS 6.13 software, a finite element model is constructed for the auxiliary feedwater electric pump to enable subsequent seismic response analysis [25; 26]. Departing from traditional methods using beam and mass units to simulate the rotor, this study adopts three-dimensional solid elements to accurately model the pressure-bearing components and rotating components, aiming for enhanced calculation precision. The machining of holes in non-stressed positions of the pump body structure are simplified, along with  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parts** & **Materials** & **Density/(kg/m3)** & **Poisson’s Ratio** & **Modulus of Elasticity/GPa** & **Yield Strength/MPa** \\ \hline Pump body, pump cover, & 022 Cr19 Ni10 & 7900 & 0.3 & 200 & 225 \\ suction section & & & & & \\ Pump base & Q235B & 7850 & 0.3 & 203 & 235 \\ Shaft & 05 Cr16 Ni5 Mo & 7930 & 0.3 & 200 & 685 \\ Bearing housing & ZG06 Cr13 Ni4 Mo & 7750 & 0.3 & 200 & 550 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material parameters of auxiliary feedwater electric pumps.  
Figure 1: General structure of auxiliary feedwater electric pumps.  
processing fillets and chamfers that minimally affect strength. The pump base installation surfaces are constrained using fixed constraints, and constraint contacts are established at the contact areas between the pump cover and pump body, the suction section and pump body, as well as the pump body and pump base. Considering the sliding bearing's stiffness on the order of \(10^{7}\) N/m, calculations reveal negligible impact on low-order natural frequencies. Therefore, nodal degrees of freedom coupling are implemented between the shaft and the bearing bracket. In Figure 2, a ten-node tetrahedral element meshes the model, with local refinement applied to the impeller, pump base installation surface, and various component contact surfaces. Mesh independence verification, depicted in Figure 3, uses the maximum stress in the pump under seismic excitation in the horizontal \(0^{\circ}\) direction as a criterion. Results show that when the number of elements exceeds 447,000, the difference in maximum stress is within 1%. Increasing mesh density has minimal impact on results, leading to the adoption of a mesh division scheme with 447,000 elements.
## 3 Results and Discussion
### Dried Juice and Pomace  
#### 3.3.1 Powder Recovery of Dried Juice and Pomace  
Powder recovery for the juice obtained from the non-PEF treated materials (SDJC) was 86.9 \(\pm\) 1.4%, while for the PEF-treated materials, it was 73.9 \(\pm\) 1.5% (DASDJC1) and 77.8 \(\pm\) 1.6% (DASDJC2), respectively. According to the criteria presented by [PERSON] [40], successful pilot-scale spray drying is characterized by a powder recovery of a minimum of 50%. The obtained values, which were higher than this limit, indicate that the process ran without problems, i.e., without stickiness issues, which are usually observed in the case of sugar-rich materials such as fruit juices, honey, or molasses. The sugar content in beetroots is not high in comparison to these types of materials (Table 3). However, the feed solutions contained 25% of the carrier (in solids, \(w/w\)) to prevent the possible deterioration of powder recovery, which could result from sugar content, even if it was quite low. Such a low carrier content is not sufficient to reduce stickiness in the case of fruit juices during conventional high-temperature spray drying, where usually at least 50% is required [41]. The powder recovery of the juices obtained from the PEF-treated beetroots was significantly lower than that obtained from the non-treated sample. This could result from the differences in sugar content, which in turn could affect stickiness and particle size. Lower sugar (sucrose and glucose) content in the PEF-treated samples decreased stickiness, and this reduced stickiness resulted in a smaller particle size (p. 3.3.2.). A similar observation of smaller particles after drying a material with lower stickiness was previously reported, e.g., by [PERSON] et al. and [PERSON] et al. [18; 42]. This smaller particle size affects powder recovery because the cyclone recovery for smaller particles is not as efficient.