esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Vibration Response Characteristics of the Chassis Frame Target Points  
To comprehensively assess the vibration response of the chassis frame under multi-source excitation, the vertical acceleration signals from the six target points on the frame were processed using a Fourier Transform to generate a three-dimensional energy waterfall plot, as presented in Figure 10.  
The energy waterfall plot reveals that the vibration energy of the chassis frame is predominantly concentrated in two frequency bands: 0-200 Hz and 600-800 Hz. Although the primary operational vibrations of the harvester are typically in the low-frequency range, the energy concentration in the higher frequency band (600-800 Hz) could be attributed to high-frequency excitation components, such as noise from bearings or gears, or to higher-order modal vibrations of the frame itself.  
Further analysis reveals that while the waterfall plots for the left-side and corresponding right-side measurement points share similar features, the energy peaks are more pronounced on the left side. This asymmetry is likely due to the installation of major excitation sources, such as the engine and threshing unit, primarily on the left side of the frame, which subjects that side to greater excitation and results in a stronger vibration response.  
To quantify the vibration levels at each measurement point, the maximum vibration amplitude at each point was plotted against the number of occurrences, as shown in the statistical distribution of maximum vibration amplitudes for the chassis frame measurement points in Figure 11.  
Figure 10: Three-dimensional energy waterfall plot for the measurement points on the combine harvester chassis frame.  
Figure 11 reveals that the vertical acceleration level at measurement point 1, located beneath the cutting header, is significantly higher than at the other points. Concurrently, the vertical acceleration levels on the left-side measurement points are markedly greater than those on the right side. This is because the main excitation components of the combine harvester are predominantly installed on the left side, a finding that aligns with the machine's actual operational behavior.
# Experimental and Numerical Analyses of Timber-Steel Footbridges
## 3 Experimental and Numerical Analysis of the Structural Detail of Interest in a Real Footbridge
### Numerical Analysis  
Numerical calculations were carried out using a FEM model of the footbridge superstructure created in the SCIA Engineer software environment [24]. The processed spatial computational model (Figure 6) combines shell elements modelling main timber girders, steel cross-girders, and vertical stiffeners with beam elements modelling stringers and transversal plank pavement members. All eccentricities at the joints of members were respected in the model using fictitious perfectly rigid links--so-called rigid arms. The material parameters of the main girders and stringers were refined based on the results of non-destructive measurements conducted on the real-life bridge.  
The bolts connecting the end plates to the main girders were modelled using beam elements with realistic cross-sectional and material characteristics, which were excluded from axial compressive action using local \"tension-only\" beam nonlinearity. Due to the software's limitations, the contact surface between the end plate and the web of the main girder was modelled by discretely inserted beam elements made of the same material as the main girders. These contact members were placed in a suitably chosen orthogonal grid of 25/40 mm at the investigated cross-girder or 50/80 mm for other cross-girders. In contrast to the members modelling the bolts, all contact members were excluded from tension action in the model using local \"compression only\" beam nonlinearity.  
Figure 5: Longitudinal section and cross-section of the tested footbridge (dimensions are given in mm).
# Satellite SAR Data-based Sea Ice Classification: An Overview
## 2 Synthetic Aperture Radar  
The development of SAR observing systems for sea ice monitoring started in 1978 with the launch of the satellite SEASAT (USA). The study of daily SAR images acquired regularly over sea ice in the Beaufort Sea shows the data potential for application to the operational mapping of sea ice type and concentration. The Kosmos-1870 (launched in 1987) and then Almaz-1 (launched in 1991) satellites carrying onboard the SAR instruments earnestly confirmed the applicability of satellite images, considered as an important operational data source for oceanology, geology, cartography, geophysics, etc. applications [7].  
The era of intensive development of SAR missions resulted in the successful launch of multiple satellites. SARs of ERS-1,2 (European Remote Sensing satellites, European Space Agency (ESA) in 1991 and 1995) were utilized to monitor the sea ice on the Northern Sea Route, ice mapping and forecasts, and to provide detailed information on sea ice to select the ship routes. However, wide operational use of ERS SAR data was limited by its rather narrow swath width and spatial coverage up to 82\({}^{\circ}\) N. The commercial SAR satellite RADARSAT-1 (RS-1, Canadian Space Agency), launched in 1995, provided access to a variety of SAR modes with different incidence angle, resolution and swath width. The ability to regularly acquire the C-band SAR images with 100 m resolution with up to 500 km wide swath has fundamentally changed the way that several national ice monitoring agencies now operate, such as the United States National Ice Center (NIC) and the Canadian Ice Service (CIS) [8].  
The launch of ENVISAT (Environmental Satellite, ESA) in 2002 with the multiple polarization ability of Advanced SAR (ASA) data represented a technological improvement of spaceborne SAR systems allowing investigation of polarimetric scattering signatures of various sea ice types and OW at different incidence angles. These studies of sea ice signatures showed the high potential of multipolarized data for application to the operational mapping of sea ice type and concentration [9; 10]. The new capacity for ice cover monitoring has been shown by research of polarimetric mode data, which are provided by the next generation of satellites. These SAR instruments can alternately transmit horizontally (H) and vertically (V) polarized waves and receive both polarizations in different combinations providing the dual-polarization or fully polarimetric SAR data. RADARSAT-2 (RS-2) launched in 2007 for the Canadian Space Agency has enhanced the measurement of parameters determined by CIS (e.g., ice-edge location, ice concentration, and stage of development) that are important for operational ice monitoring. The launches of X-band TerraSAR-X and TanDEM-X (Germany, in 2007 and 2010), L-band ALOS-2 (Japan Aerospace Exploration Agency, in 2014), C-band Sentinel-1A /B (European Commission and ESA, in 2014 and 2016) and others have provided a large amount of multi-mode SAR images for sea ice observations.
# Real-Time Performance Measurement Application via Bluetooth Signals for Signalized Intersections
## 2 Materials and Methods
### Bluetooth-Based Traffic Analysis Detector
Bluetooth-signal-based systems, one of the techniques used in performance measurement, offer an ergonomic method for collecting travel time and delay data. They are easy to install and can provide real-time data without significant infrastructure changes. However, the accuracy of Bluetooth systems depends on the availability of Bluetooth-enabled devices rate in vehicles. When these technologies are compared, it becomes clear that Bluetooth-signal-based systems offer a cost-effective and traffic-free option for real-time performance measurement, while other methods may offer advantages in terms of data richness. The most appropriate technique must be selected according to the traffic management need or application area. Bluetooth sensors can be used in certain areas of traffic and for certain purposes:  
_Traffic Data Collection_: Bluetooth sensors anonymously capture the MAC addresses of Bluetooth devices located in a certain area. In this way, the passage times of vehicles and pedestrians at certain points are recorded [27]. _Traffic Flow Analysis_: The collected data are used to analyze traffic density, speeds, travel times, and route preferences. This information provides traffic management centers with state about the real-time traffic situation [28]. _Travel Time Calculation_: The travel time between two specific points is determined by calculating the time difference between the sensors located at these two points [29,30]. _Traffic Density and Capacity Analysis_: These sensors are used to determine the number of vehicles and density on a particular road section. Thus, information is obtained about the capacity of the roads and the current traffic density [31]. _Traffic Management and Planning_: The data collected are used by urban planners and traffic engineers to develop traffic management strategies, reduce traffic congestion, and improve road safety.
## 6 Comparison with Other Reported Studies  
In Table 3, a comparison is made between the current study and previous studies that are associated with the relevant areas. In comparison to the findings of earlier research, the proposed diplexer demonstrates a significant band isolation level that is noticeably higher, as compared with [28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38]. The proposed diplexer demonstrates competitive frequency response characteristics when compared to designs that have been published in [28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38], as well as a compact size, which measures 32 \(\times\) 26 mm\({}^{2}\). In addition to this, the frequency response of the proposed structure exhibits a very good return loss in both passbands. There is a slight disadvantage associated with the device that was built, and that is its insertion loss, which is 2.88 dB for Tx and 3.2 dB for Rx. However, this is regarded to be acceptable as compared with [33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43].  
Figure 15: Comparison between simulations and measurements in terms of phase.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{
\begin{tabular}{c} **Resonances** \\ **(GHz)** \\ \end{tabular} } & \multirow{2}{*}{**IL (dB)**} & \multirow{2}{*}{**RL (dB)**} & \multirow{2}{*}{**Isolation** **(dB)**} & \multirow{2}{*}{**Size (mm\({}^{2}\))**} \\  & & & & & \\ \hline This work & 1.66/2.52 & 3.2/2.88 & 16/21 & 31 & \(32\times 26\) \\ \hline
[33] & 1.94/2.515 & 2.9/2.5 & 31.6/29.2 & 25 & \(32\times 26\) \\ \hline
[34] & 3.06/4.6 & 0.09/0.01 & 17.1/27 & 27.8 & \(18\times 24\) \\ \hline
[35] & 1.24/1.5 & 1.2/1.36 & 20/20 & 25 & \(21\times 26\) \\ \hline
[36] & 1.67/1.88 & 0.43/0.35 & 16/19.6 & 22.13 & \(27.5\times 22.3\) \\ \hline
[37] & 3.2/5.84 & 1.8/2 & 21/27 & 21 & \(24\times 22\) \\ \hline
[38] & 2.12/3.94 & 0.25/0.26 & 18.45/17.47 & 24 & \(23.4\times 16.9\) \\ \hline
[39] & 1.6/2.1 & 0.1/0.16 & 33/22 & 22 & \(40.5\times 23.7\) \\ \hline
[40] & 1.88/3.56 & 0.12/0.10 & 19.2/36 & 20 & \(15\times 20.2\) \\ \hline
[41] & 2.4/5.5 & 1.67/1.58 & 11.1/13.2 & 25.8 & \(14.6\times 10.48\) \\ \hline
[42] & 1.8/2.45 & 2.05/2.15 & 15/15 & 25 & \(50\times 53\) \\ \hline
[43] & 1.8/2.45 & 2.2/1.8 & 17/16 & 21 & \(20.0\times 52.0\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the projected diplexer with other testified studies in the literature.
# Symmetric Circle Configurations from Regular Skeletal Polyhedra
## 5 E-Constructed Point-Circle Configurations
Clearly, the stabilizer \(\langle r_{0},r_{2}\rangle\) of \(e\) in \(G(Q)\) also stabilizes \(c(e)\), and this group is isomorphic to \(C_{2}\times C_{2}\) and acts simply transitively on the four points on \(c(e)\). This establishes the first part. For the second part, as the circles are uniquely determined by their edges in \(Q\), any element of \(G(Q)\) stabilizing the circle \(c(e)\) will also fix the base edge \(e\) and thus belong to \(\langle r_{0},r_{2}\rangle\). It follows that the circle stabilizer coincides with the edge stabilizer. Thus \(G(Q)\) acts simply point-circle transitively on the circle configuration. The cube is an example where two (opposite) edges determine the same circle (see Figure 4). In this case, \(G(Q)\) still acts point-circle transitively, but the circle stabilizers also contain the reflection in the plane of the circle and have order 8. In Figure 4 and the figures below, colors have been added for visual clarity and better distinction of geometric components. As described in Section 4, the regular skeletal polyhedra fall into four classes consisting of finite, planar, blended, or pure polyhedra, respectively. We discuss each class separately. In each case the polyhedron \(Q\) can be constructed from its symmetry group \(G(Q)=\langle r_{0},r_{1},r_{2}\rangle\) by Wythoff's construction. This proceeds from a common fixed point \(v\) of the generators \(r_{1}\) and \(r_{2}\), which is taken as the base vertex \(v\) of \(Q\), and constructs the base edge \(e\) as the line segment \((v,r_{0}(v))\) and the base face \(f\) as \(\{s(e)\,|\,s\in\langle r_{0},r_{1}\rangle\}\). The vertices, edges, and faces of the polyhedron are then the images of the base vertex, base edge, and base face under \(G(Q)\). The construction of the circle configuration \(C(Q)\) for \(Q\) can proceed similarly from an incident pair consisting of a _base point_ and a _base circle_: the vertex \(r_{1}r_{0}(v)\) of \(Q\) lies on \(c(e)\) and can serve as the base point of the configuration, and the circle \(c(e)\) corresponding to the base edge \(e\) of \(Q\) can serve as the _base circle_. The entire configuration \(C(Q)\) then consists of the images of the base point and base circle under \(G(Q)\). Alternatively, one could find the points and circles of \(C(Q)\) as the images of \(v\) and \(c(e)\), respectively (but note that \(v\) does not lie on \(c(e)\)).
## 3 Design of Isl Network for IoT  
This section describes the design process of an ISL network for small satellites by identifying and analyzing the communication layer, functionalities, and potential solutions, based on the OSI framework. This includes the physical layer (frequency, bandwidth, channel coding, and modulation), data link layer (medium access control), and network layer (topology and routing).
## 4 Experimental Settings and Results
### Effectiveness Validation of MAF-NBC  
A third experiment was performed to evaluate the effectiveness of MAF-NBC. This was performed by comparing the classification performances of MAF-NBC with Dis-NBC, FNBC [3], TAN [4], AODE [5], HNB [6], DFW-NBC [7], CFW-NBC [8], and ICA-NBC [10]. All Bayesian algorithms were implemented using the Python programming language. Twenty KEEL mixed-attribute data sets (described in Table 1) were selected to test the training and testing accuracies of the compared algorithms. For each data set, independent training and testing were conducted 10 times with random data set partitions, i.e., 70% samples to train the different Bayes algorithms and 30% to test their generalization capabilities. All ANNs were constructed with 2\(\mathcal{L}\) hidden-layer nodes, initialized with random weights in the \([-1,1]\) range, and setup with the learning parameters \(\lambda=0.50\) and \(\xi=\zeta=0.01\). The mean and standard derivation of the 10 training and testing accuracies are listed for each algorithm in Tables 2 and 3, respectively.  
The training and testing performances of MAF-NBC were statistically validated by comparing with eight other Bayes algorithms on the 20 data sets. For the given significance level of 0.05, the critical difference (CD) value [28] is calculated as follows:  
\[\text{CD}=3.102\times\sqrt{\frac{9\times(9+1)}{6\times 20}}\approx 2.686, \tag{31}\]  
where the number of compared algorithms is nine, and the number of data sets is 20. In Figure 5, an interval of one CD value can be observed to the left and right of the average rank of MAF-NBC. Any algorithm with a rank outside this area is significantly different from MAF-NBC. It is found that the ranks of MAF-NBC corresponding to the training and testing accuracies are obviously smaller than the other algorithms. In addition, the number of wins for MAF-NBC on 20 data sets is at least \(\frac{20}{2}+1.96\times\frac{\sqrt{20}}{2}\approx 14\) for the significance level of 0.05. Furthermore, it can be said that MAF-NBC has significantly improved testing accuracies than Dis-NBC (20 wins), FNBC (20 wins), TAN (20 wins), AODE (20 wins), HNB (20 wins), DFW-NBC (19 wins), CFW-NBC (19 wins), and ICA-NBC (18 wins) under a significance level of 0.10. This indicates that MAF-NBC is significantly better for classification than the other algorithms on the selected data sets. The experimental results and statistical analyses demonstrate the effectiveness of MAF-NBC and indicate that MAF-NBC is a viable method to handle mixed-attribute classification tasks. MAF-NBC does not modify the simple model structure of the NBC and preserves the amount of information of the original mixed-attribute data set as much as possible, because the ANN transforms the original mixed attributes into encoded attributes rather than selecting or extracting independent attributes from the original mixed attributes.
# A Survey on Swarm Robotics for Area Coverage Problem
## 3 Algorithms and Methods for Swarm Robotics  
Most algorithms applied for swarm robotics in area coverage are inspired by coordination among biological entities [6]. The algorithms used in swarm robots are classified into stochastic and deterministic [7]. By definition, deterministic approaches use mathematical models that rely on local optimum and gradient to solve problems [8]. In contrast, stochastic techniques rely on mathematical properties to perform a given function, but with minimal dependency on the gradient and local optimums when solving specific problems [9]. Experts argue that the stochastic technique is more user-friendly than the latter in optimising robots to work independently or simultaneously in a swarm [10]. The type of algorithm applied in swarm robots is mainly dependent on the nature or type of problem being solved by the robot [11]. Researchers and developers of the optimisation domain have designed stochastic algorithms that mimic natural processes that are synonymous with various types of animals and flies, such as fish, birds, and bees. The notable algorithms developed from such observations include Particle Swarm Optimisation (PSO) and Honey Bee Optimization (MBO) [12; 13]. And, lately, algorithms are based on bacteria colony behaviours [14]. The algorithms mentioned above are commonly used in swarm robots for area coverage. Many algorithms and methods have been used in swarm robots for area coverage in the last seven years, as follows.  
The algorithms are divided into sections such as metaheuristic algorithms-swarm intelligence and classical algorithms.
## 3 Proposed Security Framework for RPL-Based IoT Networks (SRF-IoT)  
SRF-IoT is a security framework designed for IoT RPL-based networks. It aims to support the security of an IoT network by identifying and avoiding malicious devices. This is achieved by embedding trust in RPL protocol for choosing the best parent. Additionally, an external IDS, called SRF-IDS, is used to shield the network from internal attackers. A first prototype of SRF-IDS is proposed in our previous work [12, 30, 31]. It consists of an SRF-IDS root that plays the role of BR and SRF-IDS detectors that have a watchdog mechanism  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Study** & **Implementation Method** & **Detected Attacks** \\ \hline SRPL-RP [26] & RPL integrated with threshold-based detection & Rank and version attacks \\ \hline Secure-RPL (SRPL) [25] & RPL integrated with threshold-based detection & Rank attack \\ \hline SecTrust-RPL [28] & RPL integrated with trust scheme & Rank and sybil attacks \\ \hline Secure Parent Node Selection Scheme [29] & RPL with integrated threshold-based detection & Rank attack \\ \hline MRTS [24] & RPL integrated with trust scheme & Rank and blackhole attacks \\ \hline SVELTE [23] & Signature/Anomaly-based IDS & \begin{tabular}{c} Sinkhole, \\ and selective forwarding \\ \end{tabular} \\ \hline \
\begin{tabular}{c} Sink-Based Intrusion \\ Detection Systems (SBIDS) [21] \\ \end{tabular} & Rule-based IDS & Rank attack \\ \hline SIEWE [20] & Anomaly-based IDS & Blackhole \\ \hline Belavagi [22] & Threshold-based IDS &
\begin{tabular}{c} Rank, selective forwarding, \\ wormhole, and DoS attacks \\ \end{tabular} \\ \hline Our SRF-IoT & RPL with trust scheme and external IDS collaboration & Rank, blackhole, and DIS attacks \\ \hline \hline \end{tabular}
\end{table}
Table 1: Relevant works on RPL attacks.  
for capturing network packets. Moreover, DIS flooding can be detected by SRF-IDS. In this work, an SRF-IoT framework is proposed as an extra security measure for RPL-based networks. We focus on protecting the network from rank and blackhole attacks, as those could severely disrupt network operation [32].
## 4 Discussion
### Limitations  
The results of this review should be interpreted while taking several limitations into account. No articles were identified that applied XAI for intraoperative margin assessments.  
The ambiguous taxonomy of XAI might have resulted in missed studies during our search strategy, despite efforts to create a broad search string to minimize this risk. Future research could expand on this by using broader search terms or additional databases.  
The included studies varied significantly in design and outcome measures, which complicates direct comparisons. This heterogeneity should be considered when interpreting the findings.  
The findings of this review are limited to the cancer types covered in the included studies, primarily breast and thyroid cancers. Further research is required to validate the use of XAI in ultrasound imaging for other cancer types. The proposed XAI methods might need significant modifications to handle new data and applications. This limits their generalizability across various clinical settings. Furthermore, many XAI methods have not been evaluated in real clinical settings, limiting their practical adoption. Moreover, feedback from end-users is absent in most of the articles regarding XAI. This, together with the minimal clinical assessment of these models, poses a significant shortcoming in the development and evaluation of XAI methods. The lack of discussion on computational time in the studies is also concerning. Many existing XAI methods use parts of the data or derivatives of real-time images. Time is an essential feature in real-time environments. Consequently, our conclusions regarding the expected clinical potential and impact of XAI in cancer-based image analysis remain uncertain. Furthermore, these models are also limited by the US hardware. Even though most of the recently manufactured US devices allow for AI integration, the computational time should be calculated using the same specifications as the end product. Additionally, XAI methods could help identify and address biases in AI predictions, but most of the reviewed studies did not explicitly focus on bias mitigation. Ensuring fairness in AI predictions is crucial, particularly in diverse patient populations. Future XAI methods should focus on developing approaches that can handle dynamic and complex image data, e.g., for better integration into oncological surgery.
# GazeGuide: An Eye-Gaze-Guided Active Immersive UAV Camera
## 6 Response Time  
The measured response time from the eye movement to the corresponding camera control input was less than 220 ms, which was fairly tolerable in the experiments. This total response time included the following:  
1. 200 ms (introduced time interval) for the eye fixation on the target.
2. A transmission delay (TCP/IP) between User-End and UAV-End of ~20 ms.