esa-sceva/satcom-chunk-collection huggingface dataset:
# AOA-Based Three-Dimensional Multi-Target Localization in Industrial WSNs for LOS Conditions
## 4 Multi-Target Detection and Localization
### MT-SIMO Signal Transmission
Thus, the baseband signal from \(\mathcal{M}_{k}\) received on the \(m\)-th antenna on \(\mathcal{A}_{i}\) through the \(l\)-th path at the moment of \(t\) is denoted by \(y_{i,m,k,l}(\tau)\). The variable \(\tau\) is the excess delay of the propagation paths. The receiving time, \(t\), is a parameter of the signal and ignored in the notations for easy presentation. Thus, the received signal is  
\[y_{i,m,k,l}(\tau)=\alpha_{i,k,l}s_{i,m}(\phi_{i,k,l},\theta_{i,k,l})e^{j2\pi v_{ i,k,l}t}u_{k}(\tau-\tau_{i,k,l}), \tag{4}\]  
where \(u_{k}(\tau-\tau_{i,k,l})\) is the circular shift of \(u_{k}(\tau)\) by the propagation delay. As all of the \(K\) MSNs are broadcasting simultaneously, the signals received on the AN antennas are the superposition of all the MSNs' signals. The received (observed) signal on the \(m\)-th antenna on \(\mathcal{A}_{i}\) is  
\[\hat{y}_{i,m}(\tau)=\sum_{k=1}^{K}\sum_{l=1}^{L_{i,k}}y_{i,m,k,l}(\tau)+w_{i,m} (\tau), \tag{5}\]  
where \(w_{i,m}(\tau)\) is the noise in the \(m\)-th receiving chain on \(\mathcal{A}_{i}\). By organizing the radiation gains of all the \(M\) antennas pointing to the 2D AOA \((\phi,\theta)\) in one vector, we can obtain the _steering vector_ of the antenna array on \(\mathcal{A}_{i}\) pointing to the direction, which is  
\[\mathbf{s}_{i}(\phi,\theta)=\left[s_{i,1}(\phi,\theta)\quad s_{i,2}(\phi, \theta)\quad s_{i,3}(\phi,\theta)\quad\cdots\quad s_{i,M}(\phi,\theta)\right]^ {\mathsf{T}}. \tag{6}\]  
It is important to note that the antenna pattern, \(s_{i,m}(\phi,\theta)\), is a parameter matrix that is measured in an anechoic chamber. The antenna gains for all the 2D directions of \(\phi\in[0^{\circ},360^{\circ})\) and \(\theta\in[0^{\circ},180^{\circ}]\) are all measured. For example, when the antenna pattern is measured with the step of \(1^{\circ}\), the matrix dimension is \(360\times 181\) and the elements in the matrix are the complex radiation gain with respect to the reference antenna.
## 8 Future Research Directions  
Some limitations have been identified in this investigation. Firstly, this study only utilised four databases, suggesting that the information gained might be further optimised by using more databases. Furthermore, the references utilised in this study exclusively consisted of peer-reviewed articles. However, it is worth noting that other types of articles may need to be included for a more comprehensive analysis. The variables identified in this study have not undergone statistical testing, so their validity and reliability have not been assessed.  
Further research is needed to advance this current study and the propositions. This includes expanding the databases to obtain a more excellent pool of references and enhancing the content's breadth. Additionally, employing a more comprehensive range of keywords beyond the anticipated ones may yield a larger body of literature with closer correlations. Furthermore, it is imperative to conduct validity tests and assess the reliability of the factors already obtained that will be crucial in validating propositions, including the proposed integrated models. It is imperative to have periodic systematic reviews as the field is continuously evolving in this field and area. Research should consider self-responsibilities, culture, and regional differences for understanding the factors influencing trust in and acceptance of blockchain adoption in digital payment systems. Understanding users' perceptions of their responsibility for security and privacy, as well as cultural norms and  
Figure 5: Potential integrated model for blockchain adoption for digital payment systems.  
values, can provide valuable insights. Regional differences, such as regulatory frameworks and infrastructure availability, can also impact adoption patterns.
# Agile Software Development in Healthcare: A Synthetic Scoping Review
## 4 Results and Synthesis
### Descriptive Bibliometrics
_Agile development of medical device software and virtual reality applications_: after some skepticism [94] that due to strict regulatory requirements in medical device software (MDS), development enforced by safety critical nature of the domain the use of ASD is very limited, new research shows that agile practices can be incorporated with MDS requirements [95]. Scaled agile and software craftmanship actually helped Philips HealthTech to improve the code quality of its medical devices, reduce release cycle time and field defects, and increase the number of delivered features. Adapted ASD has been successfully used also in other MSDs development [96]. Similar to MDS, therapeutic virtual reality systems (VRS) also need regulatory clearance for clinical and home use. It has been shown that, with proper risk assessment, VRS development can also benefit from agile practices [97]. Agile development has been successfully used in various educational VRS tools and environments [98, 99]. _Agile digital public health during COVID-19:_ ASD has become a vital part of digital health transformation [39]. The COVID-19 pandemic has presented software developers with a brand-new problem: how to develop software in an environment where it is necessary to develop completely new mission critical software solutions (for example vaccine distribution or emergency software) in a few days with fluidly changing requirements. ASD proved to be viable also in such hyper-agile environments [100, 101]. ASD also worked well in developing self-monitoring systems [40] and gamification systems for the spread of information regarding COVID-19 [102]. _Implementing safe telemedicine with mHealth:_ one of the goals of care transition is to make healthcare safer [103]. To make telemedicine and mHealth services safer and better, ASD was employed in developing mobile applications for dementia support [91], medication adherence [80], self-management of health [104], and HIV care [105]. _Test driven development of safe clinical decision support systems:_ defects in clinical decision support which are based on electronic health records are quite common. By using agile test-driven development (TDD) those defects can be effectively reduced [106]. TDDhas also been used to asses smart healthcare ontologies, a technology which deals with healthcare information and IoT devices [107]. Regarding the adaptability of ASD to healthcare software development, our study showed that ASD is employed comparably to other fields with a focus on user experience, mobile applications, and continuous delivery of software. Scrum is the most frequently used agile approach. Regarding the use of ASD in safety-critical healthcare software development, very few publications were concerned with this matter [94], mainly dealing with developing embedded software in medical devices [108]. Thus, in this regard, ASD in healthcare has yet to follow this global SD development trend.
## 3 System Model  
We consider a wireless network consisting of two kinds of nodes, GNs and UAVs, which are represented by the sets \(\mathbf{V}\) and \(\mathbf{U}\), respectively. All nodes are assumed to be located in a rectangular area \(\mathcal{A}\) with length \(X_{max}\) and width \(Y_{max}\). Nodes are equipped with omni-directional transceivers and a GPS. Therefore, they know their positions in the aforementioned rectangular area at any time. The position of a GN \(v\) is assumed to be on the ground with coordinates \(q^{v}_{(x,y,0)}\), while the position of a UAV node \(u\) is represented in the 3D plane as \(q^{u}_{(x,y,h)}\), where \(h\) is the flying altitude of \(u\). We consider \(\mathcal{A}\) as a suburban area and that the main factor affecting the service quality offered by a UAV is path loss. Furthermore, orthogonal channel codes are used to avoid interference between concurrent transmissions. Similar to [23], we employed the log-distance path loss model, where the received power is calculated according to the following expression:  
\[P_{r}(d)=P_{r}(d_{0})-10\alpha\log_{10}\left(\frac{d}{d_{0}}\right) \tag{1}\]  
where \(P_{r}(.)\) is the received power at a given distance. In Equation (1), \(d_{0}\) is the reference distance and \(\alpha\) is the path loss exponent. UAVs are assumed to have the same operating characteristics, featuring the same transmit power, antenna gains, and enough energy storage to complete the mission, and they may fly at different altitudes. GNs can only communicate with each other through UAVs. Assuming communication between a GN and a UAV, \(d\) is computed as the Euclidean distance between their transceivers as follows:  
\[d=\sqrt{(x_{u}-x_{v})^{2}+(y_{u}-y_{v})^{2}+h_{u}^{2}} \tag{2}\]  
We define \(\mathcal{D}_{i}\) as the maximum achievable communication range for a given transmission mode \(i\) by simply manipulating Equation (1). \(\mathcal{D}_{i}\) is computed considering \(P_{r}(d)\) as the receiver sensitivity at transmission mode \(i\), and \(P_{r}(d_{0})\) is the received power at a reference distance \(d_{0}\). The distance \(d\) between the transmitter and the receiver should not be greater than the maximum communication range \(\mathcal{D}_{i}\) for the required communication mode \(i\). An overview of the proposed system is shown in Figure 1.
## 3 Chain Communication Model
### Message Hash Chain Communication Model
If so, the The receiver's verification of \(p_{i}\) ends successfully and updates \(HC^{\prime}(src,dst)_{i-1}\) to the intermediate node of the message hash chain and \(HC^{\prime}(src,dst)_{i}\) to the tail node of the message hash chain. Otherwise, the receiver's verification of \(p_{i}\) is unsuccessful and must to discard the \(p_{i}\) and report an error.
# An Optimization Routing Algorithm Based on Segment Routing in Software-Defined Networks
## 2 Related work
### Segment Routing Based on SDN
And most backup paths can be encoded with a segment list of one or two labels. In terms of multi-domain traffic engineering, the pd-SR scheme and e2e-SR scheme are proposed, which utilize a two-way communication session established among the SR controllers of the traversed domains. However, the two schemes use different procedures to determine the segment list to be applied to the packets and compare based on the depth of the segment list. However, the authors do not consider the routing selection process for the SDN controller to deploy routing information. The authors in [20] introduce an integer linear programming (ILP) model to heuristically evaluate the TE performance of SR-based packet networks. Three different ILP models are proposed, the ECMP model, SHP model, and SEGMR model. Because the computational complexity of the ILP model is high, some instances require too much time to be resolved. Thus, the authors propose a heuristic approach to determine the unique route between each pair of nodes that need to transmit information. Heuristic attempts to keep the lowest possible network utilization at a minimum and to ensure that the maximum value of the SLD is not exceeded. Our work, however, focuses on a routing algorithm that considers the optimization of link weight and the evaluation of paths. The authors in [16] propose a routing algorithm for SDN with SR that can satisfy the bandwidth demand of routing requests. This algorithm takes into account the load balance and the additional cost of packet header size reduction in the network. The algorithm uses the criticality and congestion index of links to define the link weight. Then, the authors design the Bellman-Ford algorithm with a hop count restriction to solve the problem of minimum weight path. The proposed algorithm takes into account the additional network overhead and network resource through the constraint of path length. However, the authors do not consider user preferences and the actual network situation. Our work not only designs the optimization of link weight for the path consumption and load balance of the network but also takes the network application service as a guide, realizes the reasonable evaluation of path performance, and finally selects the best path.
# IoT-Based Intelligent Modeling of Smart Home Environment for Fire Prevention and Safety
## 4 IoT-Based Intelligent Modeling of Smart Home Environment for Fire Prevention and Safety
### ZigBee as the Used Communication Protocol  
To set up the wireless sensor network, it is possible to use different topologies, depending on the number of sensor nodes and communication methods. The four most common topologies are: (i) The peer to peer, (ii) The mesh, (iii) The tree, and (iv) The star network topologies [40]. Comparatively, the star network topology setup is very competent compared to other network topologies. Figure 6b shows the star network topology. For communication between the sensor and the home sink, we used the Zigbee protocol. Figure 6a shows the communication method. ZigBee is based on IEEE 802.15.4 standard specification and uses high-level communication protocols to build personal local area networks. It uses tiny, low powered, digital radio devices and it can be used to transfer data over some limited distance from 10 to 100 m. This distance depends on the environmental conditions, and it varies for different homes. It is considered a low-powered protocol because it needs a minimal amount of energy to receive and transmit data.  
It consumes most of its energy in transmitting the data. This protocol uses different types of devices like: (i) Coordinator; (ii) Router; and (iii) End devices. ZigBee uses as a coding technique the direct sequence spread spectrum, abbreviated as DSSS. Therefore, it uses two primary types  
Figure 6: (**a**) Flow diagram of the Wireless Sensor Node (**b**) Star topology.  
of communication modes, i.e., (i) Beacon-enabled and (ii) Non-beacon enabled. The outlines of the beacon-enabled and non-beacon-enabled communication are shown in Figure 7a,b [41]. In beacon-enabled communication, the beacon frames or beacon packets are broadcast periodically by the PAN coordinator [41]. In non-beacon-enabled communication, the PAN coordinator broadcast the beacon packets randomly. When the sensors are in the inactive state or do not send the data, this protocol automatically turns off its interface to save energy. When it sends beacon messages, the nodes remain in the active mode. Usually, beacon intervals are between 15.36 ms and 251.65 ms at the speed of 250 kb/s, but this depends on the data flow rate. By keeping in mind this ZigBee communication model, we used ZigBee for data communication.
# Engine Fault Detection by Sound Analysis and Machine Learning
## 3 Methodology
### Feature Extraction  
Feature extraction is a critical step in audio signal processing for fault diagnosis in vehicle systems. This section outlines two widely used techniques: MFCC and DWT.  
MFCC is a prominent technique in audio signal processing. The process involves several steps [30]:  
* Windowing the audio signal into short segments using Equation (1). \[W(n)=0.54-0.46\ \text{cos}\bigg{(}\frac{2\pi n}{N-1}\bigg{)}\ \ \ \ \ \ \ \ \ N-1\geq n\geq 0\] (1)
* Applying the Discrete Fourier Transform (DFT) to each frame to convert the time-domain signal into the frequency domain. DFT can be defined for a frame \(X\) comprising of \(N\) samples as in Equation (2). \[X_{n}=\sum_{k=0}^{N-1}X_{k}e^{-\frac{2\pi\hbar n}{N}}\ \,\ \ \ \ \ \ n=0,1,2,3\ldots,N-1\] (2)
* Mapping the frequency spectrum to the Mel-scale to approximate human auditory perception using Equation (3). \[f_{mel}=2595\log_{10}\bigg{(}1+\frac{f_{linear}}{700}\bigg{)}\] (3)
* Applying logarithmic compression to the Mel spectrum.
* Computing the Discrete Cosine Transform (DCT) to obtain the MFCC coefficients as follows: \[C_{k}=\sum_{n=0}^{N-1}\log(S_{n})\text{cos}\bigg{[}k\pi\frac{(2n+1)}{2N}\bigg{]}\] (4)  
Figure 3: Methodology block diagram for vehicle fault diagnosis using sound signals and machine learning.  
where \(C_{k}\) represents the \(k\)-th MFCC coefficient, \(N\) represents the total number of mel-frequency filters, \(S_{n}\) represents the energy of the n-th mel-frequency filter bank, and \(k\) is the index of the MFCC coefficient, usually ranging from 0 to \(k-1\).  
These MFCC coefficients capture essential spectral features of the audio signal, such as pitch, timbre, and formants, which are crucial for fault diagnosis in vehicle systems. In this study, the mean of each coefficient over each frame was calculated as a feature. Details regarding MFCC parameters are provided in Section 4.  
DWT is a powerful tool for analyzing signals in both time and frequency domains simultaneously [31]. The process involves the following:  
* Decomposing the audio signal into different frequency bands using wavelet functions, such as Daubechies and Symlets wavelets. For an input signal \(x(t)\), the approximation coefficients \(A_{j}\) and detail coefficients \(D_{j}\) at level \(j\) are computed using: \[A_{j+1}(k)=\sum_{n}h(n-2k)A_{j}(n)\] (5) \[D_{j+1}(k)=\sum_{n}g(n-2k)A_{j}(n)\] (6) where \(h\) and \(g\) are the low-pass and high-pass filter coefficients, respectively, corresponding to the wavelet function.
* Extracting features from each level of decomposition.  
These features provide insights into the time-frequency characteristics of the signal, aiding in fault detection and classification in vehicle systems. In this study, we calculated the energy, standard deviation, and entropy of each coefficient to serve as features for the classification task. Details regarding the decomposition levels and wavelets employed are given in Section 4.
# Seg-Eigen-CAM: Eigen-Value-Based Visual Explanations for Semantic Segmentation Models
## 4 Experiments
### Quantitative Analysis
Although Eigen-CAM \(PE\) is similar to other methods, it is important to note that \(PE\) can only effectively reflect the quality of the explanations when \(Dice_{H}\) is sufficiently high. To visualize the performance of each explanation method, we present the distribution of the Dice score and the image retention rate in a two-dimensional histogram, as shown in Figure 7. The analysis reveals that Eigen-CAM's samples are concentrated in the bottom-left region, indicating suboptimal explanation performance. Gradient-based methods demonstrate higher image retention rates, with their high \(Dice_{H}\) values indicating an effective representation of the model's decision rationale. In comparison, samples from our proposed approach cluster in the bottom-right region, suggesting superior capability in identifying critical regions that influence model decisions while minimizing the impact of irrelevant areas. The distribution pattern in the two-dimensional plane demonstrates both the correlation and stability characteristics of each method, highlighting their respective reliability. Optimally, an attribution map should preserve maximum prediction information within a minimal image area; therefore, distributions closer to the bottom-right quadrant represent more effective explanation outcomes. \begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Method** & **Model** & **Eigen-CAM** & **Seg-Grad-CAM** & **Seg-XRes-CAM** & **Ours** \\ \hline \multirow{2}{*}{Mean Retain Image (\%)} & DeepLabV3 & 50 & 46 & 69 & **18** \\  & PSPNet & 49 & 68 & 69 & **20** \\  & BiSeNetV1 & 39 & 30 & 51 & **16** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Image retention rate of XAI methods. The bold font number indicate the highest value corresponding to each label.
## 2 Materials and Methods  
### Soil, Petroleum Products, and Sorbents  
The soil, free of any contaminants, was collected from agricultural fields (0-20 cm) in the vicinity of Olsztyn (Warmian-Masurian Voivodeship, Poland). All soil samples were homogenized and sieved through a 5 mm mesh sieve. Additionally, soil samples designated for grain size analysis, organic carbon content (C\({}_{\text{org}}\)), total nitrogen (N\({}_{\text{total}}\)), physicochemical properties, and soil enzyme activity (Table 1) were sieved through a 2 mm mesh sieve. Table 1 also presents the characteristics of petroleum products, and sorbents used in the study. **Diosel oil. ** Premium fuel for Diesel engines, purchased from PKN Orlen (Poland). Density: 0.820-0.845 g cm\({}^{-3}\), sulfur content--maximum 10 mg kg\({}^{-1}\). The detailed characteristics are available on the PKN Orlen website [68]. **Unleaded gasoline 95. ** Fuel for gasoline engines of vehicles, purchased from PKN Orlen (Poland). Density: 0.720-0.775 g cm\({}^{-3}\), sulfur content--maximum 10 mg kg\({}^{-1}\). The detailed characteristics are available on the PKN Orlen website [69]. The diesel oil and gasoline were applied in the experiment at doses of 0, 8, and 16 cm\({}^{3}\) kg\({}^{-1}\) d.m. of soil. **Dolomite. ** Ground sedimentary rock with a pH of approximately 9.0, containing Ca--50.1% and Mg--15.8% [54]. **Perlite. ** A quartz mineral extracted from volcanic rocks with a pH of approximately 7.0, characterized by an amorphous porous structure. It contains SiO\({}_{2}\)--about 73% \(w\)/\(w\), Al\({}_{2}\)O\({}_{3}\)--about 15% \(w\)/\(w\), Ca--0.36-1.07%, and Mg--0.12-0.42% [56; 70]. The dolomite and perlite used in the study were provided by Biovita Sp. z o.o., Tenczynek, Poland. The sorbents were applied in the experiment at doses of 0 and 10 g kg\({}^{-1}\) d.m. of soil. Along with wheat and rice, maize (_Zea mays_) is one of the most important cereals cultivated on Earth [71]. As a C4 plant, maize is very adaptable to different environmental conditions. The global area under maize (for grain) is 197 million hectares and is increasing steadily. According to OECD-FAO [72], global maize production is expected to reach 1.36 billion tons in 2032. In the experiment, hybrid maize of the DS1897B variety (Producer Pioneer, Warsaw, Poland) was grown which can be used for feed and biogas. It is a late-maturing variety [73]. In the study, maize was grown with 4 plants per pot for 60 days. The plants were harvested at growth stage 59 of the BBCH (Biological Bundesanstalt, Bundessortenamt, and Chemical).