esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 17 Energy Efficient Co-Operative Broadcasting at the Symbol Level  
### System Model and Design Objectives  
In [12] symbol-level broadcasting in a WSN was considered where cooperation among nodes in which a node's receiver utilizes the residual energy of weak signal emitted from distant nodes and accumulates those along with the aggregate signals from near-by nodes. This was referred to as _Cooperative Wireless Advantage_ (CWA) in [12] where the connectivity of a node was defined by its ability to detect a symbol, either received directly from the source node or through relaying nodes, with sufficient signal to noise ratio (SNR) such that for a given signaling scheme, the bit error rate (BER) is acceptable. The objective is to find the optimal energy allocation such that the total energy across all nodes in the network is minimized while satisfying an acceptable BER or equivalently a certain SNR level.  
The WSN considered in [12] has only one source node (node 1) broadcasting to all the other nodes in the network. Each node in the network, other than the source node, serves as both a destination and a relay for the transmitted symbol. The protocol proposed in [12] works at the physical layer by doing the relaying on a symbol by symbol basis.  
The transmissions of each node are orthogonal to one another, which means the system is interference free. This means that the received energies from different relay nodes can be added to detect the transmitted symbol. Also, the transmission bandwidth was assumed large enough in so that all relayed versions of a symbol are resolvable. The firing order was also assumed to be the same as the receiving order at each node. Hence the authors in [12], [PERSON] et al., stated that this simplified their problem to make the minimum energy solution dependent on the order of firing (transmission) among the nodes instead of the absolute firing time instants.  
Moreover, the optimal firing order was assumed to be known to reduce the problem complexity, and the nodes were enumerated based upon the firing order such that \(t_{f_{1}}<....<t_{f_{N-1}}\). The last node, \(N\), does not need to make any transmission to contribute to the broadcasting since all the nodes that fired before node \(i=N\) have successfully received and relayed the pulse to the other nodes. The firing of node \(i\) increments the energy by \(\gamma_{i,k}\left(\epsilon_{k}\right)=\epsilon_{k}\left|A_{i,k}\right|^{2}\) for all the nodes \(j>i\), where \(A_{i,k}\) is the channel gain between nodes \(i\) and \(k\) while \(\epsilon_{k}\) is the transmission energy of node \(k\). The SNR requirement of a node \(k\) is hence satisfied by the accumulation of \(\gamma_{ik}\left(\epsilon_{i}\right)\) for \(i=1,...,k-1\).
# A Framework to Determine the Utilization of Vacant Taxis on HOV Lanes with the Optimal Deployment
## 2 Preliminaries  
Limited-access HOV lanes are common. For simplicity, we assume that there only exist limited-access HOV lanes. Under this assumption, each HOV lane or GP lane is regarded as a link. Consider a general network \(G(N,A+\overline{A})\), where \(N\) is the set of nodes, \(A\) is the set of GP links, and \(\overline{A}\) is the set of HOV lanes. \(R,S,W\) are the sets of origins, destinations, and travel modes respectively, where \(r\in R\), \(s\in S\), and \(m\in M\). In this paper we assume that there are three types of travel modes: solo driving, carpooling, and taxi, denoted as \(sd,h,o\), respectively. Vacant taxi mode is denoted as \(v\). \(P\) represents the set of paths used by travelers, and \(P_{v}\) represents the set of the paths used by vacant taxis. The notations are listed as follows:  
\(d_{rs}\) the demand between OD pair \(rs\)  
\(d_{rs}^{m}\) the demand for travel mode \(m\) between OD pair \(rs\)  
\(d_{rs}^{o}\) the demand for vacant taxis between OD pair \(rs\)  
\(\lambda_{t}\) the value of time of travelers  
\(\lambda_{o}\) the value of waiting time of taxi passengers  
\(Z_{r}\) the area of origin \(r\)  
\(l_{a}\) the length of link \(a\)  
\(C_{a}\) the capacity of link \(a\)  
\(b_{s}\) the operation cost per unit distance of solo-driving travelers  
\(b_{o}^{0}\) the flag-fall price per ride  
\(b_{o}^{1}\) the mileage-based fare per ride  
\(b_{o}^{2}\) the delay-based fare per ride  
\(b_{v}\) the operation cost per unit distance of vacant taxis  
\(t_{a}(x_{a})\) travel time function on link \(a\), which is strictly increasing, convex, and continuous with the flow on link \(a\)  
\(\delta_{a,p}^{rs}\) A binary constant that equals 1 when link \(a\) is on the path \(P\) between OD pair \(rs\) and 0 otherwise.  
\(f_{rs,p}^{m}\) the flow of travel mode \(m\) on path \(P\) between OD pair \(rs\)  
\(f_{sr,p_{v}}^{o}\) the flow of vacant taxis on path \(P_{v}\) between OD pair \(sr\)  
\(x_{a}\) the vehicle flow on link \(a\)  
\(x_{a}^{m}\) the vehicle flow of mode \(m\) on link \(a\)  
\(x_{a}^{o}\) the vehicle flow of vacant taxis on link \(a\)
## 3 SDR Technology
### SDR Architecture
#### 3.1.2 SDR Receiving Mode  
The first stage in the reception process is the down-conversion of the received signal by mixing the incoming signal with an LO. In this way, the received signal is converted from the Radio Frequency (RF) domain to the Intermediate Frequency (IF) domain. The IF signal is then represented by I/Q components. Afterwards, the intermediate I/Q components will be sampled by an analog-to-digital converter (ADC). The digitized I/Q data follow parallel paths through a digital down-conversion (DDC) process which mixes, filters, and decimates the input signal to a user-specified sampling rate. The down-converted samples will then be sent to the host computer. Both transmitting and receiving operations are depicted in Figure 1.
# Road to Efficiency: V2V Enabled Intelligent Transportation System
## 4 V2V Communication Architectures
### Decentralized Mesh Network  
The Decentralized Mesh Network (DMN) is a connectedness module that develops among vehicular prowess nodes, where each vehicle serves as a guard in the vast range of communication [47]. Through wireless protocols like IEEE 802.11p, cars engage in a web of short-range exchanges, weaving a system of safety-critical information. The network's reach is expanded by intermediate vehicles, ensuring no area is left untouched by the hand of connection [48]. The beauty of decentralization lies in no entity retains authority over the network. Each vehicle in the network contributes to its overall resilience by serving as a key component in a robust, redundant system that can adapt to failures and maintain constant communication [49]. The security threat looms large, but end-to-end encryption  
Figure 1: V2V Communication Architecture.  
shrouds critical information, keeping it from prying eyes. Trust mechanisms develop within the network, allowing it to verify with a node of recognition and confirm the purity of the received communication, making it resistant to the intrigues of malicious powers. The DMN's complexity and variety associate, weaving resilience, dependability, and security, creating a route toward a future where communication knows no boundaries [50]. The infrastructure is inherently scalable, accommodating varying numbers of vehicles in different scenarios.  
A DMN is a self-organizing and resilient architecture that allows peer-to-peer communication without relying on a centralized infrastructure [51]. Figure 2 represents individual nodes within the network, representing equipment with wireless communication capabilities. These nodes are interconnected, creating a web-like structure where communication pathways can be built between any two nodes within range. The mesh topology ensures stability and redundancy in communication pathways, allowing data transmission between neighboring nodes. The flexible configuration of nodes and links demonstrates the network's adaptability, which can autonomously adapt their communication behavior based on network parameters like signal strength, traffic load, or node mobility. This dynamic adaptation provides optimal performance and effective resource utilization, even in demanding conditions. DMN supports real-time communication for collision avoidance, facilitates the dissemination of traffic information, and enables rapid and direct communication between vehicles in case of emergencies, enhancing coordination for emergency services and first responders.
# SOBER-MCS: Sociability-Oriented and Battery Efficient Recruitment for Mobile Crowd-Sensing
## 3 Background  
SOBER-MCS adopts MSensing auction-based user-centric incentives [39] for mobile crowd-sensing and introduces an energy-aware incentive mechanism that uses sociability-based user profiling.  
The MCS platform receives the task from the end user and estimates the cost of the task in terms of energy consumption based on the number of sensors that are needed to sense the task and also the duration of sensing. In this paper, five different sensors including GPS, accelerometer, microphone, light and video camera are taken into consideration. Each task involves a random combination of sensors and is also assigned a random task duration. The task and user settings are then sent to the recruitment policy component for user-task matching. The recruitment policy takes users' profiles, locations and the residual energies of their devices into consideration and applies the reverse auction procedure to find the winners (i.e., selected participants). To ensure energy efficiency, the recruitment module introduces energy costs into the user bids, as well as the residual battery power of the participants in the selection.
## 3 Numerical Algorithm for Solving the Resulting Eikonal PDE
### High-Order Godunov-Based Scheme  
Consider a uniform discretization \(\{(x_{m},y_{n})=(ms,ns),m=1,2,\cdots,r,n=1,2,\cdots,c\}\) of the given image domain \(\Omega\) whose resolution and grid size are \(r\times c\) and \(s\times s\), respectively. Now our goal is to get a discrete solution \(z_{m,n}=z(x_{m},y_{n})\) of the unknown 3D surface \(z(x,y)\).  
A first-order Godunov-based scheme proposed in [7; 29] is employed to discretize resulting eikonal PDE (10):  
\[\max\left[\frac{z_{m,n}-z_{m-1,n}}{s},-\frac{z_{m+1,n}-z_{m,n}}{s},0\right]^{2} +\max\left[\frac{z_{m,n}-z_{m,n-1}}{s},-\frac{z_{m,n+1}-z_{m,n}}{s},0\right]^{2 }=F_{m,n}^{2}, \tag{11}\]  
where \(F_{m,n}=\sqrt{\delta_{m,n}^{2}-1}\). The substitution of \(z_{a}=\min[z_{m-1,n},z_{m+1,n}]\), \(z_{b}=\min[z_{m,n-1},z_{m,n+1}]\) and  
\[\{x\}^{+}=\left\{\begin{array}{ll}x&x\geq 0\\ 0&x<0\end{array}\right. \tag{12}\]  
into Equation (11), we have  
\[\left[\left\{\frac{z_{m,n}-z_{a}}{s}\right\}^{+}\right]^{2}+\left[\left\{ \frac{z_{m,n}-z_{b}}{s}\right\}^{+}\right]^{2}=F_{m,n}^{2}. \tag{13}\]  
Hence, the first-order scheme viscosity solution of Equation (13) can be obtained thusly:  
\[z_{m,n}=\left\{\begin{array}{ll}\frac{z_{a}+z_{b}+\sqrt{2s^{2}F_{m,n}^{2}-( z_{a}-z_{b})^{2}}}{2}&|z_{a}-z_{b}|<sF_{m,n},\\ \min[z_{a},z_{b}]+sF_{m,n}&|z_{a}-z_{b}|\geq sF_{m,n}.\end{array}\right. \tag{14}\]  
To build a higher-order scheme, the \(p_{m,n}\) and \(q_{m,n}\) with higher-order accuracy need to be approximated. According to [27], since the third-order Weighted Essentially Non-Oscillatory (WENO) scheme has a uniform high order accuracy and is more efficient and robust than other schemes (e.g., the Essentially Non-Oscillatory (ENO) scheme), we also choose to employ the third-order WENO approximations.  
\[\left\{\max\left[\frac{z_{m,n}^{n}-z_{1}}{s},0\right]\right\}^{2}+\left\{ \max\left[\frac{z_{m,n}^{n}-z_{2}}{s},0\right]\right\}^{2}=F_{m,n}^{2}, \tag{15}\]  
where  
\[\begin{array}{l}z_{1}=\min[(z_{m,n}^{old}-sp_{m,n}^{-}),(z_{m,n}^{old}+sp_{ m,n}^{+})],\\ z_{2}=\min[(z_{m,n}^{old}-sq_{m,n}^{-}),(z_{m,n}^{old}+sq_{m,n}^{+})],\end{array} \tag{16}\]  
with  
\[\begin{array}{l}p_{m,n}^{-}=(1-\alpha_{-})\left(\frac{z_{m+1,n}-z_{m-1,n}}{ 2s}\right)+\alpha_{-}\left(\frac{3z_{m,n}-4z_{m-1,n}+z_{m-2,n}}{2s}\right),\\ p_{m,n}^{+}=(1-\alpha_{+})\left(\frac{z_{m+1,n}-z_{m-1,n}}{2s}\right)+\alpha_
## 2 Methodology
### Modeling Approaches  
#### 2.3.1. AHP Model  
The AHP model is a semi-quantitative method for solving decision-making problems and is widely used in LHA studies [11; 29; 30]. In this model, predisposing factors are compared in pairs to evaluate the degree of influence of each factor on landslide occurrence [16; 17; 19]. Then, based on the comparison results, a judgment matrix is established to obtain the weight value of each factor. The degree of influence can be classified into different levels ranging from 1 to 9 [31]. In the AHP model, the consistency ratio (\(CR\)) is used to check the consistency of the judgment matrix and is calculated by Equation (4). Generally, when the \(CR\) value is less than 0.1, the judgment matrix satisfies the calculation requirements [16].  
\[CR=CI/RI \tag{4}\]  
where \(CI\) is the consistency index of the judgment matrix and can be obtained by Equation (5), and \(RI\) is the random index of the judgment matrix and can be found in Table 1 [32; 33; 34].  
\[CI=(\lambda_{max}-n)/(n-1) \tag{5}\]  
where \(\lambda_{max}\) is the principal eigenvalue of the judgment matrix, and \(n\) is the total number of selected factors.  
Finally, the landslide hazard can be calculated using Equation (6):  
\[LHI_{AHP}=w_{1}\times PF_{1}+w_{2}\times PF_{2}+w_{3}\times PF_{3}+\ldots+w_{ j}\times PF_{j} \tag{6}\]  
where \(LHI_{AHP}\) is the landslide hazard index obtained by the AHP model, \(w_{j}\) is the weight value of predisposing factor \(j\), and \(PF_{j}\) is the FR of predisposing factor \(j\).
# Embedded AI-Based Digi-Healthcare
## 4 Design and Implementation
### Hardware Development
#### 4.1.2 Edge Device  
The objective of integrating an edge device in the system architecture is for central data localization, pre-processing of the sensor data, and performing local decision making powered by ML to reduce latency in health predictive analysis on the patients, thus improving quality of service. The implementation of the edge module was achieved by integrating Raspberry Pi 4 Figure 4) into the system framework. The pre-trained global machine learning model was downloaded on the raspberry pi using AWS IoT Greengrass to offer real-time inference at the edge. The procedural description of the edge computing tasks is given below:  
1. Data packets from the wearable sensors are received at the edge, and parsed into JSON format.
2. The data is then cleaned simultaneously to eliminate error values.
3. After pre-processing procedures, the data is fed to the local inference downloaded through AWS IoT Greengrass periodically, for real-time anomaly detection in the patient's vitals.
4. The data along with inference results is then pushed to AWS as well as to the web application for storage and further analysis.  
Figure 3: MLX90614 Non-Contact IR Temperature Sensor.  
Figure 2: High-Sensitivity Pulse oximeter and Heart-Rate Sensor.  
It is important to highlight that, this implementation achieved a good response time in case an anomaly in the patient's vitals was identified. The critical notification to the concerned caretaker was generated immediately, however, the exact response time was not quantified in this study.
# Special Issue on Trends and Challenges in Robotic Applications
## 2 Fields of Application
### Computer Vision and Other Advanced Techniques  
Computer vision focuses on enabling computers to interpret and understand visual information from the world, including objects and humans [15]. It involves the development of algorithms and techniques that allow machines to process, analyze and extract meaningful insights from images and videos. In recent years, computer vision has undergone significant advancements, leading to the emergence of new technologies that allow for new interaction applications; for example, in [16], a moving object is manipulated by means of batting primitive to play table tennis with a human player.  
Pose estimation is a fundamental concept in computer vision that involves determining the position and orientation of an object or a camera relative to a specific coordinate system. It plays a crucial role in various applications, such as augmented reality [17], robotics, 3D scene reconstruction and human-computer interaction. In simple terms, pose estimation aims to answer the question: \"Where is the object or camera located, and how is it oriented in 3D space?\" To achieve this, computer vision algorithms analyze visual data, typically in the form of images or videos, and extract relevant features or keypoints from the objects of interest. Similarly, [18] presents an interesting approach to pose computation using a 3D reconstruction of data obtained using an RGB-D multi-camera. A groundbreaking piece of technology that is currently attracting attention due to its relatively low cost is Time-of-Flight (ToF) sensing, and the authors of [19] present a multi-perspective ToF laser ranging system using prisms and mirrors.  
The authors of [20] discuss the significance of 6D pose estimation in industry and its application in functions like bin picking and autopilot. They highlight the evolution of various approaches, both learning-based and non-learning-based, and aim to provide an up-to-date, thorough review of methods for 6D pose estimation, as well as the challenges and future trends in this field. Their paper compares the performance of different methods and categorizes them into two types of approach: non-learning-based and learning-based.  
Using data from external sensors that measure 3D/6D locations is crucial to determining robots' kinematic parameters and the transformation between the world coordinate frame and the robot base. The study presented in [21] shows that full pose measurements result in significantly smaller robot orientation errors compared to using 3D data alone, while the robot position errors remain similar in both cases.
## 1 Introduction  
Two-phase bubble columns are used to bring one or several gases into contact with a liquid phase; they have found many applications thanks to their many advantages (i.e., the simplicity of construction, the lack of mechanically operated parts, low energy consumption, reasonable prices, and the large contact area between the phases). In addition, a bubble column provides a good experimental system for the study of turbulent phenomena in bubbly flows and is a suitable setup for the development and validation of computational models. Despite the simple layout, bubble columns are characterized by extremely complex fluid dynamic interactions between the phases. Indeed, uncertainties in bubble column design and scale-up arise from the lack of understanding of the phenomena governing the bubble size distributions (BSDs) and, consequently, the overall column fluid dynamics. Moreover, the prevailing BSD determines the interfacial area for the heat and mass transfer at the interface. The BSD at the inlet is determined by the formation of the bubbles at the gas sparger; this size distribution may not be stable, and changes to determine the developed (equilibrium/prevailing) BSD [1]. The prevailing BSD depends on many phenomena, for example, the balance between the coalescence and breakup rates, the pressure change, the phase change, and mass transfer. It is generally accepted that the prevailing BSD is related mainly to the coalescence and breakup phenomena; thus, based on the precise knowledge of these phenomena, the prevailing BSD can be determined a-priori. In particular, a population balance model (PBM) can be used to provide a statistical formulation to describe the behavior of the dispersed phase [2; 3; 4]. Finally, based on the determined BSD, the whole column fluid dynamics can be predicted (for example, by a lift force approach). Unfortunately, because of the unsatisfactory understanding of the underlying mechanisms governing breakup and coalescence, no broadly applicable model is available. In this paper, we contribute to the existing discussion concerning suitable closures for breakup and coalescence; in particular, we present a PBM approach by combining and modifying the literature models, and we validate it against a previously published experimental dataset. The experimental dataset consists of BSDs obtained in the \"pseudo-homogeneous\" flow regime and in a large-diameter and large-scale bubble column. The aim of this model is to compute the BSD in the developed region of the column, using as input the BSD at the sparger.
# Deep Learning Methods in Image Matting: A Survey
## 1 Introduction  
### Image Matting  
Image matting [1] is a technique that is used for extracting the fine foreground of images, which is a computer vision task that has a wide range of application scenarios. The foreground of an image may include entities such as humans or animals, with delicate and complex edges such as hair as well as transparent objects such as glass, light bulbs, or water. These elements can be difficult for computers to accurately recognize. Image matting calculates the opacity of each pixel in an input image to obtain the alpha matte, which allows for the separation of the foreground from the background. The foreground can be composited with any background image to obtain a new image, as shown in Figure 1.  
Chroma key matting [2] is a classic image matting technique that can be used to obtain the foreground from a solid background by adjusting the colors of pixels in the background to make them transparent. This technique requires a special shooting environment, which is considerably limited in practical applications. Therefore, researchers have focused on extracting alpha mattes from images that have natural scenes as the background. However, image matting in natural scenes loosens the constraint of setting a solid color background, which leads to a decline in alpha matte accuracy. Mathematically, the problem of image matting can be expressed as follows:  
\[I_{i}=\alpha_{i}F_{i}+(1-\alpha_{i})B_{i} \tag{1}\]where \(I_{i}\) denotes the RGB value at pixel \(i\) of the input image, \(\alpha_{i}\) denotes the opacity value at pixel \(i\) ranging between 0 and 1, \(F_{i}\) denotes the RGB value at pixel \(i\) in the foreground, and \(B_{i}\) denotes the RGB value at pixel \(i\) in the background.  
In image matting, the input image can be represented as a linear combination of foreground and background, with each pixel having only three known variables (RGB values) but seven unknown variables to be solved, as shown in Formula (1). Moreover, the definition of the foreground in an image is not precise and varies depending on the intended use. Consequently, image matting is a highly ill-posed problem that typically necessitates the use of auxiliary inputs such as trimaps to provide additional information.