esa-sceva/satcom-chunk-collection huggingface dataset:
# Traffic Measurement and Congestion Detection Based on Real-Time Highway Video Data
## 2 Related Work
### Object Tracking in Computer Vision
#### 2.3.2 Mean Shift and Camshift  
The mean shift algorithm is an iterative process that uses probabilities determined by the back-projection of a histogram into an image to locate the target [6]. As described by [PERSON] and [PERSON] [12], the goal of the algorithm is to find the local extrema in the density distribution of a data set. The location of the target image is gradually changed, e.g. in one or two pixel steps until the most likely local maximum is found [6].  
Moreover, a related concept to mean shift is the camshift algorithm. It differs primarily through the fact that the search window adjusts itself in size. This algorithm can be used for objects moving closer or farther away from the camera which results in size changes [12].
# Short-Range Noncontact Sensors for Healthcare and Other Emerging Applications: A Review
## 4 Emerging Applications
### Occupancy Sensing  
Occupancy sensing is employed not only for security purposes, but also for the control of lighting, heating, ventilation, air conditioning (HVAC), and other presence-related loads and demands in commercial and residential spaces [19]. It is expected that, by occupancy sensing, the conservation of energy can be realized through the automated shutdown of loads no long needed when a space is unoccupied. In recent years, energy efficiency and energy conservation are becoming more and more important attracting increasingly attention. A nearly 50% increase is projected to happen in global energy use by 2025 [19]. Occupancy sensors can save up to 80% of energy used for lighting and HVAC systems, according to the research [19], which will also result in huge financial savings.  
The common types of occupancy sensors include the passive infrared and the ultrasonic sensors. However, both of them are subject to high rates of false alarms and failure to detect stationary subjects. These two types of sensors are effectively motion sensors but not the best sensors for detecting human presence. In the past two decades, researchers in the radar sensor field has made tremendous technological progress to make the sensor low-cost, highly integrated while has high accuracy. All the technical advancements have made the Doppler radar technology feasible for occupancy sensing. Moreover, the integration of processing unit and RF transceiver on one single chip, which is often called system-on-chip (SoC), provides a new platform for combination of sensing, processing, and communication. The high level system integration lays the foundation for a smart sensor network in the future smart home and could be the key part of the sensing system of the internet of things (IoT). Besides detecting the large motions of human activities, Doppler radar sensor is also capable of detecting the weak vital sign signals, which provides a promising approach to overcome the problems of false alarms and dead spots in the conventional sensors. Radar sensors make true presence detection possible. Doppler radar can discern the physiological motion from non-human periodic motion that could otherwise trigger false positives. It can prevent false negatives by detecting heart beat and respiration rates in the case of stationary human subjects, while the existing other occupancy sensors lack accuracy to do so.
## 4 Multilevel Inverters (MLIs)  
In recent years, a growing number of industrial applications have necessitated the use of higher-power equipment. Medium-voltage and megawatt power level converters are needed in some medium-voltage motor drives and utility applications. It is difficult to link only one power semiconductor switch directly to a medium-voltage grid; as a result, in high-power and medium-voltage conditions, a multilevel power converter structure has been implemented as an alternative [23]. There are many _MLC_ topologies in existence; thus, in this study, only the most common multilevel converters have been assessed. The pros and cons of these multilevel inverter topologies are discussed in detail in this section. Besides being employed as high-power converters, multilevel converters are extensively utilized in renewable energy sources (wind, fuel cells, etc.) by associating the \(PV\) modules on one side and high-power loads on the other side [25]. Multilevel converters began as a three-level inverter introduced by [110]. The main difference between the two-level source voltage inverter (\(VSI\)) and the _MLC_ is in the levels of the voltages; for the two-level _VSI_, they normally generate only two levels of voltages, while an infinite number of voltage levels can be generated by the _MLI_. The _MLI_ has at least three voltage levels; the output of a power converter is determined based on the consistency of its current and voltage waveforms [111]. The multilevel inverter concept (_MLI_) for generating _AC_ signal does not only rely on two voltage levels, as shown in Figure 28. Alternatively, a variant of the reinforced smooth wave is applied to most voltage levels to the other, with a small \(dv\) / \(dt\) and less harmonic distortion. This produces a smoother waveform with more inverter voltage levels, but with many design rates, it becomes more complicated, involves more parts, and needs a more complex inverter controller [112]. Multilevel converters are more often considered due to their high voltage operation ability, great efficiency, small losses from switching, operation at both fundamental and high switching frequency of pulse width modulation (_PWM_), and low output of _EMI_[113]; therefore, the system as a whole will be more costly. On the contrary, the emphasis is to raise the circuit complexity via a reduction in the number of switches and gate driver circuits [113; 114]. Figures 28 and 29 show two, three, five, and seven levels, respectively.  
Figure 27: Equivalent circuit of the _DC-DC_ series-parallel resonant converter [80].
# A Dynamic Edge Server Placement Scheme Using the Improved Snake Optimization Algorithm
## 3 System Modeling and Problem Description
### Cost Model  
As noted above, edge servers can be categorized as static or dynamic. Static edge servers do not move once placed, whereas dynamic edge servers can be moved. The edge-server placement cost has three main components: the fixed, compensation, and dynamic costs. The fixed cost is that for edge-server purchasing and deployment. The compensation cost refers to the fact that idle edge servers can be used for other purposes to compensate for the deployment cost. The dynamic cost is that incurred by the edge servers as a result of location changes.  
User latency exceeding the given threshold indicates a small number of edge servers; the number of mobile edge servers must be increased to reduce this latency. The incurred cost of the newly deployed edge servers is  
\[C_{1}=\left\{\begin{array}{ll}(n_{t+1}-n_{t})(\omega+\theta),&\text{if }n_{t+1}>n_{t}\\ 0,&\text{otherwise}\end{array}\right., \tag{8}\]  
where \(\omega\) denotes the cost of purchasing an edge server, \(\theta\) denotes the cost of deploying each edge server, and \(n_{t+1}\) denotes the number of dynamic edge servers at time \(t+1\).  
When the user latency is below a certain value, this indicates that too many edge servers are deployed. Then, some of the edge servers can be used for other purposes to compensate for the deployment cost. The compensation cost is computed as follows  
\[C_{2}=\left\{\begin{array}{ll}(n_{t}-n_{t+1})\psi,&\text{if }n_{t}>n_{t+1}\\ 0,&\text{otherwise}\end{array}\right., \tag{9}\]  
where \(\psi\) denotes the benefit available to each idle edge server.  
When an edge server must be moved, the dynamic cost includes the dismantling cost at the previous location, the installation cost at the new location, and the edge-server movement cost. The dynamic cost is calculated as  
\[C_{3}=2\theta n_{m}+\zeta W, \tag{10}\]  
where \(n_{m}\) denotes the number of edge servers that must be moved, \(\zeta\) denotes the cost per unit distance that the edge server must be moved, and \(W\) denotes the minimum distance that all edge servers must be moved. Here  
\[n_{m}=\min(N_{t+1}-(N_{t+1}\cap N_{t}),N_{t}-(N_{t+1}\cap N_{t})), \tag{11}\]  
where \(N_{t+1}\) represents the set of edge server locations at time \(t+1\), and \(N_{t}\) represents the set of edge server locations at time \(t\).  
In summary, the total cost of edge-server placement is  
\[C=C_{1}-C_{2}+C_{3}. \tag{12}\]
# A Novel Drone Design Based on a Reconfigurable Unmanned Aerial Vehicle for Wildfire Management
## 4 Electronics Design
### GNSS Module [Holybro H-RTK Unicore UIM982 (Dual Antenna)]  
The usage of UAVs in the current work necessitates high-precision geolocation to ensure the reliability of any information related to objects detected through corresponding algorithms and transmitted to end-users. The Holybro UM982 Dual Antenna RTK GPS (Holybro, Hong Kong, China) meets this need by providing high-precision positioning information, even in challenging environments where signal obstructions or multipath interference may occur. Capable of generating non-magnetometer moving baseline yaw determinations for autopilots with one single GPS module, the UM982 offers compass-less YAW information, preventing magnetic interference and environmental sources from causing incorrect yaw reports. Additionally, it includes features such as a magnetometer, LED, and safety switch button. As an RTK-corrected vehicle GPS, it serves as a base station GPS for sending RTCM data to a ground control station, providing an RTK source for the vehicle  
Figure 3: Air-frame selection: (**a**) selection and validation board/circuit; (**b**) component identification; (**c**) wiring diagram—Side A.  
via telemetry. Leveraging carrier phase measurements from multiple satellite constellations including GPS (L1C/A/L2C(P)/L5), GLONASS (L1/L2), Galileo (E1/E5a/E5b), QZSS (L1/L2/L5), and Beidou (B1I/B2I/B3I), the UM982 calculates precise position, velocity, and orientation information in real time, delivering centimeter-level accuracy ideal for applications such as aerial surveying, precision agriculture, and infrastructure inspection.
# A Novel Cross-Correlation Algorithm Based on the Differential for Target Detection of Passive Radar
###### Abstract  
For the problem of traditional passive radar target detection, the cross-correlation method is a popular solution. By processing the reference signal and the target echo signal coherently, the method is able to extract the target parameters, such as distance, velocity, and azimuth. However, the estimation performance is limited by the low signal-to-noise ratio. Therefore, a new cross-correlation algorithm for passive radar target detection is proposed in this paper. The echo signal received by the echo antenna and the reference signal received by the reference antenna are processed by differential operations to transform the single signal into multiple signals. Then, the differential reference signal is used to eliminate the signal phase in the differential echo signal. With the two-dimensional Fourier transform, the coherent accumulation of multiple signals is realized based on matched filtering of the single signal. This makes the secondary accumulation of the target signal coherent, while the interference part can only obtain incoherent secondary accumulation. In this way, the signal-to-noise ratio for target detection can be greatly improved. The paper presents the theoretical derivation of the algorithm and the quantitative analysis of its performance. The results of both simulated and measured data show that the detection performance is greatly improved compared with the traditional method.  
passive radar; differential transformation; two-dimensional Fourier transform; cross-correlation; Range-Doppler processing +
Footnote †: journal:  
02022
# IPCB: Intelligent Pseudolite Constellation Based on High-Altitude Balloons
## 2 Overview of IPCB  
An IPB is illustrated in Figure 1. It utilizes a high-altitude balloon as a platform, which is composed of balloon, cable, parachute, gondola, balloon controller, payloads, and other attachments, as shown in Figure 1a. The balloon controller, payloads, and other attachments are installed in the gondola. Furthermore, the balloon consists of a main helium bag and an air balloonet, which are separated by a membrane, as Figure 1b illustrates [28; 29; 30]. The main helium bag is filled with helium to provide buoyancy to the IPB, and the air ballonet is filled with air to adjust the IPB mass. Fans and valves are installed on the bottom of the balloon, which can pump air into or release air from the air ballonet. The balloon controller can perceive and adjust IPB flight status intelligently. In particular, the balloon controller can manipulate the fans and valves flexibly, enabling the IPB to adjust its mass and flight altitude in a range.  
An IPCB is illustrated in Figure 2. It consists of multiple IPBs and can form coverage over a service area. When payloads in the IPBs normally send out navigation signals, the IPCB can provide positioning service for the area independently. During the process of  
Figure 1: Structure illustration of IPB studied in this paper: (**a**) structure overview; (**b**) detailed structure of the balloon.  
IPCB service, its geometry configuration is always changing with the wind, and the changes are highly nonlinear, which brings difficulty to IPCB geometry configuration planning.
# Mutual Authentication Protocol for D2D Communications in a Cloud-Based E-Health System
## 5 Security Analysis
### Non-Traceability  
The use of different temporary identities and newly generated random numbers in each new authentication session generates different parameters exchanged. Therefore, outsiders cannot track patients by the parameters exchanged on a public channel.
# Visual Ranging Based on Object Detection Bounding Box Optimization
## 2 Research Content and Works  
With the advancement of deep learning in recent years, monocular visual ranging based on object detection has been widely used, yet there are some neglected issues in this research.  
First of all, most scholars have ignored an issue when using this method. Due to the uneven distribution of light and the fuzzy object boundary and other factors, there may be a non-fit phenomenon between the object detection bounding box and the object, resulting in pixel spacing (as shown in Figure 1), and this pixel spacing causes ranging errors.  
Secondly, among all the monocular range measurement methods, there are very few methods that are really practical and can measure objects of unknown size. The PNP ranging method needs the placement of an object in order to measure distance. The imaging model method needs knowledge of the object size in order to measure distance, and the method based on data regression modeling is used to find the real distance corresponding to the pixel point using a data fitting function. The methods described above are either laborious, time-consuming, or need an exact object size, so they are unsuitable for practical use.  
In this study, we propose two methods to address the two issues mentioned above:  
1. To address the issue of ranging error caused by the non-fit phenomenon between the object detection bounding box and the object, an object detection bounding box optimization method is proposed which can eliminate pixel spacing and improve the fit degree of the bounding box and the object, thereby improving ranging accuracy.
2. In response to the lack of practical methods for measuring the distance to an object of unknown size in monocular visual ranging, a similar triangle ranging method based on height difference is proposed. This method can realize the distance measurement of an object of unknown size and also provides a new method for monocular visual distance measurement.  
The two methods described above are both improvements and inventions aimed at addressing current difficulties in the field of visual range based on object detection. In order to verify the two methods proposed in this paper, the first phase involves object detection. In this paper, we use the classical YOLOv5 algorithm for object detection. Secondly, to eliminate the pixel spacing caused by the non-fit phenomenon due to the mismatch between the object bounding box and the object, we use the proposed bounding box optimization method and then apply it to the ranging to improve the ranging accuracy. Finally, after optimizing the object detection bounding box, we use the proposed similar triangle ranging method based on height difference to carry out the ranging experiment.
## 3 ABER Analysis
### BPSK  
From [34], the ABER calculation expression for BPSK modulation can be obtained as follows:  
\[P_{e}=\int_{0}^{\infty}Q(\sqrt{2x})f_{\gamma_{SIR}}(x)dx \tag{9}\]  
where \(Q(x)=\frac{1}{2}erfc\bigg{(}\frac{x}{\sqrt{2}}\bigg{)}\).  
Substituting Equations (7) and (8) into Equation (9), and by using Gauss-Laguerre to carry out integration, the following can be obtained:  
\[P_{e-BPSK}=\frac{\bigg{(}\frac{g_{S}}{b_{S}}\bigg{)}^{SS}\rho\bigg{(}\frac{g_{I }}{b_{I}}\bigg{)}^{S_{I}}}{2D\overline{\gamma}B(g_{S},g_{I})}\sum_{i=1}^{n} \frac{w_{i}e^{x_{i}}\bigg{(}\frac{\rho x_{i}}{D\overline{\gamma}}\bigg{)}^{SS -1}erfc\big{(}\sqrt{x_{i}}\big{)}}{\bigg{(}\frac{g_{S}}{b_{S}}\frac{\rho x_{i }}{D\overline{\gamma}}+\frac{g_{I}}{b_{I}}\bigg{)}^{S_{S}+S_{I}}} \tag{10}\]  
where \(w_{i}\) signifies the weight factor, while \(x_{i}\) corresponds to the \(i^{th}\) zero of the Laguerre polynomial.  
Proof.: Refer to Appendix A for the proof. \(\square\)
# The Hydroological Balance in Micro-Watersheds Is Affected by Climate Change and Land Use Changes
###### Abstract  
The temperate forests are key to the balance and provision of hydrological and environmental services. Currently, these forests are subject to human alterations as well as to the effects of global change, including warming, variability, deforestation, and forest fires. As a consequence, the hydrological balance has been modified. The present study simulates the effects of climate change and land use change on the hydrological balance of micro-watersheds in Mexico using the hydrological model Water Evaluation and Planning (WEAP). The land use change between 1995 and 2021 was estimated to establish a baseline. Climate scenario SSP585 was projected using three global models, MPI-ESMI-2-LR, HadGEM3-GC31-LL, and CNRM-CM6-1 by the 2081-2100 horizon, along with two scenarios of land use change: one with forest permanence and another with loss of forest cover and increased forest fires. Results indicate that future climatic conditions will modify the hydrological balance at the microbasin level. Even with positive conditions of forest permanence, increases in surface runoff of 124% (CNRM), 35% (HadGEM3), and 13% (MPI) are expected. The projections of coverage loss and fires showed surface runoff increases of 338% (CNRM), 188% (HadGEM3), and 143% (MPI). In the high areas of the microbasins where temperate forest predominates, climatic variations could be contained. If the forest is conserved, surface runoff decreases by \(-70\%\) (CNRM), \(-87\%\) (HadGEM3), and \(-89\%\) (MPI). Likewise, the moisture in the soil increases. In areas with temperate forests, there will be modifications of the hydrological balance mainly due to the increase in evapotranspiration (due to the increase in temperature and precipitation). This will cause a significant decrease in flow and interflow. The alteration of these flows will decrease water availability in soil for infiltration. It is expected that the availability of hydrological and environmental services will be compromised in the entire study area due to climate change.  
hydrological scenarios; general circulation models; WEAP; runoff; base flow Article
# Three-Dimensional Modeling of a Robotic Fish Based on Real Carp Locomotion
## 1 Introduction
According to the above literature survey, a 3D dynamic model including all kinematic and hydrodynamic effects by analyzing realistic parameters of a real fish would be a significant reference while developing a realistic prototype. The contribution of this paper is to develop a complete dynamic model including kinematic and hydrodynamic effects of Carangiform locomotion based on the Lagrange approach by adapting the parameters and behaviors obtained from real carp. This model provides appropriate solutions for biomimetic design of fish-like motions with 3D carp gait patterns unlike the dynamic robotic fish models available in the literature with non-realistic parameters. In order to mimic real fish-swimming abilities, the designed robotic fish model has been focused on the proportional optimum link lengths according to actual size, swimming speed, flapping frequency, proportional physical parameters and different swimming motions of the real carp. 3D locomotion is evaluated by applying various trajectories in different MATLAB environments. The closed loop performance is provided by using the Reaching Law Control (RLC) approach in the inner loop and orientation control is also achieved with a guidance system in the outer loop. These simulations and experimental analyses show that the proposed model gives us significant key solutions to design a fish-like robotic prototype. The rest of this paper is organized as follows: in the next section, Carangiform locomotion is described and a complete dynamic model, including kinematic and hydrodynamic effects, is given. Then the maneuverability of the robotic fish model is performed and simulation results are given. Finally, conclusions are presented.
## 5 Results and Discussion  
The performance of the proposed protocol is evaluated and compared with (i) existing protocols such as WAVE-enhanced service message delivery (WSD), (ii) the IEEE 802.11p standard [30], (iii) a distributed multi-channel mobility-aware cluster that depends on medium access control (DMMAC) [31], and (iv) a cluster-based beacon dissemination process (CB-BDP) [32]. The objective of this experimental evaluation was to improve the safety at the road level by considering QOS requirements of vehicular applications. An efficient MAC protocol was developed to reduce the impact of relative speed in V2V communication by providing preferences to the hop before it leaves the range of communication. Simulation is performed on a Network Simulator (NS-2) for up to four relay nodes in the hopsector scenario.
# On the Use of Deep Learning for Video Classification
## 3 Video Classification
### Basic Deep Learning Architectures for Video Classification  
The two most widely used deep learning architectures for video classification are convolutional neural network (CNN) and recurrent neural network (RNN). CNNs are mostly used to learn the spatial information from videos, whereas RNNs are used to learn the temporal information from videos, as the main difference between these two architectures is the ability to process temporal information or data that come in sequences. Therefore, both these network architectures are used for completely different purposes in general. However, the nature of video data with the presence of both the spatial and the temporal information demands the use of both these network architectures to accurately process the two-stream information. The architecture of a CNN applies different filters in the convolutional layers to transform the data. RNNs, on the other hand, reuse the activation functions to generate the next output in a series from the other data points in the sequence. However, the use of only 2D-CNNs alone limits the understanding of video to only spatial domain. RNNs, on the other hand, can understand the temporal content of a sequence. Both these basic architectures and their enhanced versions are applied in several studies for the task of video classification.