esa-sceva/satcom-chunk-collection huggingface dataset:
### Vegetation Indices  
MicaSense and PlanetScope reflectance images of each of their respective bands were exported into ArcGIS to extract crop canopy reflectance values at the sample points. With the \"Raster Calculator\" tool in PCI Geomatica Banff, the images were used to compute the VIs listed in Table 4 and exported as GeoTiff files. Using ArcGIS, the VI layer values were extracted at each sample point. As MicaSense has a red-edge band, the following three indices were included specifically for the UAV-data modelling: the chlorophyll index red-edge (CI_RE), the normalized difference vegetation index (NDRE), and the ratio vegetation index #2 (RVI2). VIs have been extensively studied for the purpose of crop monitoring and biophysical estimation, such as the normalized difference vegetation index (NDVI) [12, 42]. VIs developed for chlorophyll estimation have been found to be related to plant nitrogen content as the photosynthetic enzyme, rubisco, encompasses the largest proportion of nitrogen in leaves [4]. Chlorophyll reflects green and NIR radiation and absorbs more than 70% of blue and red radiation [43].
## 5 Experiments and Results
### Model Selection for XAI Technique Application
Following this comprehensive comparison, we conclude that the model's predictions can be deemed reliable, as two methods, XGBoost and Random Forest, identified a similar set of features for determining network traffic. To further promote transparency and explain the model's decision-making process, we employ XAI techniques, focusing solely on the XGBoost model. This decision stems from its superior accuracy compared to the Random Forest, making it the more reliable choice for detailed analysis. Concentrating on \begin{table}
\begin{tabular}{c c c} \hline \hline
**Influential Features in Model Prediction** & **XGBoost** & **Random Forest** \\ \hline
1 st Top Feature & rst\_count & rst\_count \\ \hline
2 nd Top Feature & IAT & IAT \\ \hline
3 rd Top Feature & variance & Header\_Length \\ \hline
4 th Top Feature & flow\_duration & flow\_duration \\ \hline
5 th Top Feature & HTIPS & HTTP \\ \hline
6 th Top Feature & urg\_count & HTIPS \\ \hline \hline \end{tabular}
\end{table} 
Table 4: List of best features identified by XGBoost and Random Forest. Figure 8: Feature importance plot for Random Forest and corresponding feature importance scores. a single, higher-performing model simplifies the explanation process and ensures that our interpretative efforts are based on the most accurate and reliable predictions, thus avoiding redundancy and maintaining clarity in our findings.
# Communication Fault Maintenance Decision of Information System Based on Inverse Symmetry Algorithm
## 4 Discussion  
The inverse symmetry algorithm is a good noise suppression method through theoretical analysis and practical verification, which can improve the quality of communication systems to a large extent, and is a good treatment for the increasingly tense communication environment. It is a good method to extract weak signal from strong noise, and it is easy to realize. Through the inverse symmetry algorithm and noise correlation, the noise correlation coefficients of adjacent time domain, frequency domain, and spatial domain are measured quantitatively, which has attracted the attention of scholars in China and abroad. In this paper, the algorithm is applied to an information system and has achieved good results, effectively improving the anti-noise performance of the information system.  
With the progress of science and technology, human life has become inseparable from information transmission. This paper analyzes the common failures of information system communication and their causes, in order to provide the basis for further development of information systems.
## 2 Materials and Methods  
### Sampling and Acclimatization  
The biomass of _Chondracanthus acicularis_ ([PERSON] used in this study was collected at Barcos beach, in Baleal (39\({}^{\circ}\)22\({}^{\prime}\)35\({}^{\prime\prime}\) N, 9\({}^{\circ}\)20\({}^{\prime}\)24\({}^{\prime\prime}\) W); at Praia Porto Batel beach, in Atougia da Baleia (39\({}^{\circ}\)19\({}^{\prime}\)09\({}^{\prime\prime}\) N, 9\({}^{\circ}\)21\({}^{\prime}\)21\({}^{\prime\prime}\) W); and at Tamargueira beach, in Figueira da Foz (40\({}^{\circ}\)09\({}^{\prime}\)58\({}^{\prime\prime}\) N, 08\({}^{\circ}\)53\({}^{\prime}\)01\({}^{\prime\prime}\) W), in Portugal, during low tide, and was transported to the laboratory in dark, cool boxes. In the laboratory, the biomass was carefully washed with filtered seawater and then cleaned to remove epiphytes, debris, and necrotic parts.  
Part of the biomass was frozen to characterize the wild seaweed's biomass and the rest was acclimatized to laboratory conditions for at least 5 days, exposed to artificial white LED light (40 \(\pm\) 1 \(\upmu\)mol photons m\({}^{-2}\) s\({}^{-1}\)) (Input Kilight 18 W 1900 lm G13 AC175-265V 300\({}^{\circ}\) 6000K 032109 RoHS) provided by two horizontal LED bulbs at the level of the culture flask (Figure 2a), under a 12:12 h photoperiod (light/dark), with constantly aerated seawater (35 \(\pm\) 2 psu) in a temperature-controlled room at 20 \(\pm\) 1 \({}^{\circ}\)C, in open 5 L trays. The acclimatized biomass was then prepared for the subsequent trials.  
Figure 2: Experimental setup of the cultivation of _Chondracanthus acicularis_ in 6 L flasks with PES medium, a density of 4 g L\({}^{-1}\), temperature room set at 20 \(\pm\) 1 \({}^{\circ}\)C, and aeration. (**a**) White LED light; (**b**) blue LED light; (**c**) red LED light.
# Cooperative Data Collection Mechanism Using Multiple Mobile Sinks in Wireless Sensor Networks
## 4 The Proposed CDCA Algorithm
### Network Partition Phase
According to Equation (14), the proposed _BTP_ algorithm will evaluate the distances from each sensor node in subtrees \(T_{1}\) and \(T_{2}\) to the roots of \(T_{1}\) and \(T_{2}\). As a result, sensor \(s_{6}\) satisfies Expression (14) and will be selected to play the role of \(s^{\textit{best}}\). Hence an additional edge (\(s_{6}\),\(s_{4}\)) will be added in subtree \(T_{1}\) and edge (\(s_{6}\),\(s_{9}\) ) will be deleted from \(T_{2}\). Repeat the abovementioned operations, until the sizes of three subtrees are identical. That is \(|\,T_{1}\,|\,=|\,T_{2}\,|\,=|\,T_{3}\,|\,.\) Figures 1(f) and 1(e) depict the process of moving two nodes \(s_{6}\) and \(s_{7}\) from \(T_{2}\) to \(T_{1}\). After completing this phase, the mobile sink executes the CP Selection and Path Construction Phase which is described in the next subsection.
# From Transactions to Transformations: A Bibliometric Study on Technology Convergence in E-Payments
## 5 Discussion and Conclusions
### Limitations and Future Research  
While this study provides a comprehensive scientometric overview, several limitations must be acknowledged: (1) Data source limitations: The analysis is based solely on Web of Science data, which may omit important regional or industry research (e.g., patents, internal white papers). (2) Methodological constraints: Static co-citation and co-occurrence analyses do not fully capture temporal topic dynamics. Future studies could incorporate dynamic topic modeling (DTM) or temporal BERTopic for greater granularity. (3) Cultural and contextual variability: Cross-regional differences in adoption behavior, regulatory culture, and infrastructure were not explicitly modeled but are critical for nuanced understanding. Future work should compare regional evolution trajectories in Asia, Europe, and the Global South.
# Numerical Study on the Phase Sensitivity Variation in Low Frequency Primary Microphone Calibrations
###### Abstract  
The low frequency phase characteristics of microphones in a monitoring system are crucial for characterizing large-scale natural and artificial activities--e.g., earthquakes, nuclear explosions, or rocket launchings. At present, microphones are simultaneously calibrated using in-situ or calibrator methods to get their phase consistency. However, the essential primary calibration, which traces their phase sensitivity to basic physical quantities, is grossly overlooked. Recently, we speculated that the microphone phase sensitivity is acoustically controlled by the pressure leakage and heat conduction effects in its back chamber, which will vary at low frequencies. Therefore, by means of the FEA (Finite Element Analysis) technique, simulations of laser pistonphone-based primary microphone calibrations are conducted both in the frequency and time domains. The frequency domain simulation quantifies the phase variation, while the time domain analysis helps us to understand the variation mechanism. It is found that the low frequency phase sensitivity is greatly influenced by its geometries and the venting state and should be pre-calibrated before serving.  
phase; sensitivity; microphone; primary calibration; multi-physics Article  
Numerical Study on the Phase Sensitivity Variation in Low Frequency Primary Microphone Calibrations
# Quality of Service and Congestion Control in Software-Defined Networking Using Policy-Based Routing
## 1 Introduction
In this work, we propose an SDN-based network that integrates traditional routing protocols, specifically configured using RouteFlow and the Quagga routing engine, to address these QoS challenges. We introduce a policy-based routing (PBR) module within the SDN controller that detects network congestion, adjusts routing dynamically, and ensures QoS by identifying alternative low-delay paths. The PBR module continuously monitors queuing delay, and when it exceeds a predefined threshold, it reroutes packets through alternative paths with lower delays to maintain high QoS standards. Our experimental results demonstrate that incorporating the PBR module with traditional routing protocols significantly reduces network congestion, improves key QoS metrics--including end-to-end delay, jitter, and packet loss--and ensures better overall performance for real-time multimedia applications. In summary, the main contributions of this study are as follows:  
* Development of an SDN-based network that integrates OSPF protocols, configured using RouteFlow and the Quagga routing engine in a virtualized environment. * Mathematical modeling and approximation of the next network hop to determine the optimal data path for forwarding real-time multimedia. * Implementation of the proposed policy-based routing for QoS in real-time multimedia and evaluation of its performance in a network emulator. The remainder of this paper is organized as follows: Section 2 reviews related work. Section 3 provides an overview of SDN and RouteFlow. In Section 4, we present the proposed policy-based routing for QoS in real-time multimedia. The network environment used in our study is described in Section 5. The results are discussed in Section 6, and finally, we conclude this paper in Section 7.
# A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges
## 1 Introduction
Steering, braking, acceleration and surrounding check are performed by the vehicle. * Level 5 represents complete automation where there is no need for human intervention and the driver is a passenger. Figure 3 summarises the above discussed features and functions achieved at each level according to the SAE J3016 standard [12]. Autonomous vehicles have several advantages including improved safety and lower road congestion resulting in lower fuel/energy consumption [4; 6]. Besides these advantages, there are some issues which need to be resolved for AVs such as who bears the legal responsibilities of the AVs, what will the course of action be if the AV controller is hacked, etc. A summary of the main pros and cons of AVs are listed in Table 2. Overall, it can be claimed that, if the drawbacks are resolved or minimised, then autonomous cars will be a considerable technological development in coming years as they will facilitate people's lives and increase road safety [10; 13]. This paper aims to review the advancement of autonomous cars focusing on the various sensors deployed in them along with the different communication technologies employed. Therefore, the objectives of this survey are as follows:  
* To identify and describe the different type of sensors used in AVs. * To classify communication technologies based on their transmission range and identify their main advantages and disadvantages. This implies: 1. Discussing short-range technologies such as Bluetooth, ZigBee and ultra-wide band (UWB) 2. Assessing medium-range technologies such as dedicated short-range communication (DSRC) and Wireless Fidelity (Wi-Fi) 3. Analysing long-range technologies such as Cellular Vehicle To Everything (C-V2X) and Fifth Generation-New Radio (5G-NR) technology  
The survey is organised as follows. Section 2 presents the literature review. Section 3 discusses the system architecture for the AVs with emphasis on the various types of sensors. Section 4 analyses the Vehicular Ad-hoc Networks (VANETs) and the Vehicle-to-Everything (V2X) technologies. Section 5 discusses the classification of the different wireless technologies based on the transmission range and their applications and challenges \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Organisation** & **Level 0** & **Level 1** & **Level 2** & **Level 3** & **Level 4** & **Level 5** \\ \hline BASt, SAE, OICA & Driver & Driver & Partial & Conditional & High & Full \\ \hline NHSTA & No & Function & Combined & Limited & Fully \\  & & Specific & Function & Self-Driving & Self-Driving \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between different automation levels. Figure 3: Different levels of automation. in AVs and ITS. Finally, in Section 6, the conclusions and future research directions, along with the decision and planning strategies are presented.
## 5 Experimental Verification
### Verification of the Stroke Trajectory Curve  
The curve of point \(f\) is easy to observe in the experiment and it is representative to use this point to represent the stroke trajectory of the robot since the trajectory of all points on the paddle is similar. The trajectory curve of point \(f\) on the vertical plane (e.g., the XZ plane in the absolute coordinate system \(O\)) can be recorded by a camera from which the X and Z coordinates can be obtained. The Y coordinate of point \(f\) can also be acquired by \(|\)X/tan\(\theta|+a\) which can be derived from Equation (2). The spatial trajectory of point \(f\) can be obtained from the X, Y, and Z coordinates. Here it is convenient to use the stroke trajectory curve on the vertical plane of point \(f\) to represent the spatial stroke trajectory curve of point \(f\) because the stroke trajectory curve on the vertical plane can reflect the characteristic of the ellipse-like trajectory.  
Since the stroke trajectories of the paddle in different stroke cycles \(T\) are the same, the paddle with the stroke cycle \(T=1.6\) s is tested. In order to verify the stroke trajectory of point \(f\) in Figure 5, the angular velocity of the stepper motors and the linear stepper motors were set to be theoretically consistent (Figure 4) in the experiment. However, the rotation directions of the stepper motors on the two stroke mechanisms were set to be opposite to each other to ensure the robot's locomotive progress. The robot was placed on the water surface of a tank which was made of transparent Plexiglass and a CCD camera was placed horizontal to the robot outside the tank to record the motion of the robot and stroke action of the one side stroke mechanism (Figure 16). According to the video, the position of point \(f\) relative to the torso was analyzed frame-by-frame.  
Figure 15: The powers of the robot \(P\) when the paddles perform an ellipse-like spatial stroke trajectory and rectangle-like spatial stroke trajectory as a function of the running time \(t\).  
Figure 17 is the experimental and theoretical trajectory curves of point \(f\) on the X-Z and Y-Z plane. From Figure 17a, the experimentally measured curve is similar to the theoretical curve which is very similar to an oval. It shows that this stroke mechanism can simulate the real water stricter insect to produce an ellipse-like spatial trajectory in practical applications when the stroke mechanism rows in the water. The theoretical and experimental curves in the above part are very consistent, and the theoretical and experimental curves in the below part produce a little error. This error arises from two aspects. (1) The blow part curve is the process in which the paddle strokes the water and fluctuations in the water surface cause the robot to sway. (2) The refraction phenomenon of the paddle image is transmitted from the water into the CCD camera in the air, which causes error in the mapping of the stroke trajectory curve.
# Geometric Similarity Measurement Method for Micro Scene Generalization
## 2 Methodology
### Shape Similarity
#### 2.1.1 Shape Feature Extraction by Included Angle Chain  
Included Angle Chain [32] is one of the main methods used for the shape similarity analyses of linear features. Its basic idea is as follows:  
Two curves are fitted with \(n\) line segments of equal length, and the angles between the adjacent line segments are used to form their respective angle sequences (i.e., Included Angle Chain). Then, the similarities between the linear features are measured by calculating the differences in the angle sequences.  
The generation process of the isometric polyline is as follows: First, the Minimum Bounding Rectangle (MBR) of the curve is determined, taking the longer side as \(L_{1}\), and then a circle with radius \(L_{1}/n\) and the node as the center is drawn. The intersection of the circle and the curve serves as the end of the line segment, and this point is also the center of the second circle (if there are multiple intersections, we select the point closest to the starting point). Then, the above steps are repeated in sequence to obtain the \(n\)th intersection \(I_{n}\); if the end of the curve is not reached, the remaining curve is regarded as a complete curve, and the above operation is repeated. Finally, within range of error \(\delta\), the length of  
Figure 2: Factors affecting micro scenes in similarity judgements: (**a**) original object group; (**b**) shape transformation; (**c**) direction transformation; (**d**) position transformation.  
the isometric polyline is obtained after \(m\) cycles. The length calculation formula is shown in Equation (1):  
\[l=\sum_{i=1}^{m}\frac{L_{i}}{n} \tag{1}\]
# Survey on Routing Protocols for Vehicular Ad Hoc Networks Based on Multimetrics
## 4 Routing-Protocol Proposals Based on Metrics
### Multimetric-Routing Proposals
#### 4.2.23 Connectivity-Aware Intersection-Based Routing (CAIR) Protocol  
CAIR [47] is a protocol designed to select a forwarding path as an optimal route that guarantees the best probability of connectivity and lower delay. This is due to constraints of vehicular networks like high mobility, periodic link disconnection, and vehicles with frequently changing density. In this  
Figure 13: Workflow for anchor-based connectivity-aware routing (ACAR) [45].  
proposal, a vehicle can know its own location and that of its neighbors by sporadically exchanging signalling messages. By using velocity, it is also possible to calculate position prediction, and forwarding nodes can choose the neighbor on the selected path as the next hop whose new predicted position is closest to the destination or the next intersection. CAIR uses a recovery strategy through the idea of store-carry-forward. Equation (26) presents the mechanism for the proposed position prediction.  
\[(x_{c},y_{c})=(x_{i},y_{i})+(s\cdot\cos\theta,s\cdot\sin\theta), \tag{26}\]  
where \((x_{c},y_{c})\) is the neighbors' current position; \((x_{i},y_{i})\) is the previous position; \(s=(t_{c}-T_{b})\cdot\) speed, where \(t_{c}\) is current time and \(T_{b}\) is previous beacon time; and \(\theta\) and speed are direction and moving velocity, respectively.
## 2 PSO Algorithm
### Standard PSO Algorithms  
PSO is a random optimization algorithm that simulates the behavior of biological groups in nature [24], and the algorithm is a computational technique to attain the optimum through group evolution [25]. In the standard PSO algorithm, the search space is \(D\), and the number of particles is \(n\). It is initialized to obtain a set of random particles, and then the iterative update calculation is carried out according to Equations (1) and (2) until the optimal solution is obtained, and then the iterative update calculation is stopped.  
\[\
u_{id}(t+1)=\
u_{id}(t)+c_{1}r_{1}[p_{id}-x_{id}(t)]+c_{2}r_{2}\Big{[}p_{gd}- x_{id}(t)\Big{]} \tag{1}\]  
\[x_{id}(t+1)=x_{id}(t)+\
u_{id}(t+1),1\leq\mathrm{i}\leq n,1\leq d\leq D \tag{2}\]  
Equation (1) is the expression of the standard (PSO) algorithm, where \(\
u_{id}(t)\) is the d-dimensional component of the velocity vector of particle \(i\) after t iterations, \(x_{id}(t)\) is the d-dimensional component of the position vector of particle \(i\) after t iterations, \(p_{id}\) is the d-dimensional component of particle \(i\) at the individual best position, \(p_{gd}\) is the d-dimensional component of particle \(i\) at the global best position, and \(c_{1}\) and \(c_{2}\) are learning factors, which are generally positive. Random weights \(r_{1}\) and \(r_{2}\) are random numbers between 0 and 1. The initial PSOA particle position update schematic is shown in Figure 1.  
The evolutionary process of the PSO algorithm is as follows.  
Figure 1: Diagram of particle position update.  
* Initialization: At \(t=0\), the algorithm randomly generates \(m\) particles, the dimension of each particle is \(D\) and the corresponding position is \(x_{i}(0)=[x_{i1}(0),x_{i2}(0)\ldots,x_{iD}(0)]^{T}\); under the limitation of the maximum velocity, take a random value and let \(v_{i}(t)\) be initialized as \(v_{i}(0)=[v_{i1}(0),v_{i2}(0)\ldots,v_{iD}(0)]^{T}\).
* Iterative calculation of the fitness function value: the individual optimal value of the randomly generated initial particles is \(P_{best}\), the global optimal value of the initial particle swarm is \(g_{best}\), and its fitness function value is \(f(x_{i}(0))\).
* Update time: set time \(t=t+1\).
* Update velocity and position: position and velocity updates according to Equations (1) and (2), respectively.
* Update the individual optimum: compare \(f(x_{i}(t))\) and \(P_{best}\), make \(P_{best}=f(x_{i}(0))\) when \(f(x_{i}(t))\) is better than \(P_{best}\); otherwise, \(P_{best}\) is left untouched.
* Update the global optimal value: In \(P_{best}(i=1,2,\ldots,n)\), select the optimal \(P_{best}\) and compare it with \(g_{best}\); if \(P_{best}\) is better than \(g_{best}\), then make \(g_{best}=P_{best}\); otherwise, leave it untouched. Follow the above process to (6) and output the result when the end condition is met; otherwise, return to (2).  
The flow of the standard PSO algorithm is shown in Figure 2.  
Figure 2: Flow chart of the standard PSO algorithm.