esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced Distributed Dynamic Skyline Query for Wireless Sensor Networks
## 4 Performance Evaluation
### Evaluation against SkySensor
#### 4.4.1 Data Uniformity  
In this experiment, data uniformity of SkySensor and EDDS is studied by varying the dimension of attribute from 2 to 7. Uniformity metric is defined as the ratio between the number of nodes used for data storage and total number of nodes in the network. In SkySensor, the number of clusters in a sensor network depends on the number of attributes of a tuple regardless of the size of the network. This is one of the major limitations of SkySensor. On the other hand, referring to Table 4, the number of sectors in EDDS for this particular network is 100, which means data are stored in 100 equivalent clusters of SkySensor resulting uniform distribution of data throughout the network. Figure 16a shows that data uniformity of SkySensor is stringently dependent on the number of dimension while this is independent and consistent for EDDS.  
Figure 15: (a) Energy consumption; (b) latency; and (c) accuracy.
## 3 Data Preparation
### Target of Data Collection  
The scope of the study was the section of highway in Korea where a traffic accident occurred. In the last five years, there were 223 roads where RORC occurred. All of these sections are rural roads, and the right guard rail was not installed. There were no RORC on roads with more than five lanes. This is because median barriers and guard rails are installed and operated on roads with more than five lanes. As of 2017, the ratio of installation of protective fences on highways with more than five lanes was 93.7%, but the ratio of installation of protective fences on highways with less than four lanes was only 30.6% [25]. Accordingly, the selected section is 167 sections for a two-lane road and 56 sections for a four-lane road; the intersection and junction were excluded. RORC is very unlikely to occur at intersections and junctions with SVC. In this section, vehicle-to-vehicle accidents and vehicle-to-person accidents occur due to conflicts between vehicles rather than SVC [26]. The section was also excluded from past studies and recent studies [27; 28].  
The contributing factors of the RORC were collected for a total of 223 sections where RORC accidents occurred. Based on the existing research, the factors were categorized into roadside fixed objects, roadway geometry, traffic conditions, and road traffic environments [3; 4; 5; 6; 7; 8; 9; 10; 11].  
As shown in Figure 1, the collected variables for each factor were as follows: eight variables of the object for roadside fixed objects, six variables of the RORC section for roadway geometry, three variables of traffic conditions, and three variables of road environmental factors. Fixed objects is a characteristic of the characteristics and location of fixed obstacles, and additionally, since it collides with fixed obstacles due to the accident characteristics of RORC, the collision location is included. Roadway geometry includes the flat linearity of the road, the vertical alignment, the harmony of the alignment, and whether the design criteria were satisfied. Road environmental factors include the weather environment and the date and time of the accident. Traffic condition includes factors for traffic flow operation conditions.  
Figure 1: Categorization of independent variables by contributing factors for RORC.
## 2 Materials and Methods
### Method of Feature Selection  
In order to remove noise interference and irrelevant features among the many features and to improve the accuracy of the model, three methods with different principles were initially employed: single-factor and multi-factor Cox regression analysis, a recursive feature elimination method, and Boruta feature selection. These were used for feature screening, with the features that appeared two or more times being selected as the final features.
# Vibration-Based Fingerprint Algorithm for Structural Health Monitoring of Wind Turbine Blades
## 4 Results
### Analysis across Turbine Types  
Finally, vibrational characteristics were compared for both test turbines by analysing fingerprints of Sensors \(S1\) and \(S15\) on the blade tips of Turbines 1 and 2, respectively. Fingerprints are displayed in Figure 15 for the joint frequency range 0-200 Hz and joint operational range 0.1-0.22 Hz.  
While fingerprints show similar properties for sensors on same blade type, e.g., a comparison of blade tips of Test Turbine 2 (see Figure 14, right), results strongly deviate for sensors mounted on the blade tips of different turbine types as displayed in Figure 15. For all detected Type 2 vibrations, the increase in frequency with the rotation frequency is significantly higher for Turbine 2 with shorter blade length (represented by Sensor \(S15\), \(70\,\mathrm{m}\) rotor diameter) than for Turbine 1 with longer blade length (represented by Sensor \(S1\), \(63\,\mathrm{m}\) blade length). Accordingly, no overlap of any Type 2 vibrations was detected. A detailed comparison for several sensor positions is not possible at the current state since Test Turbine 1 was only equipped with two sensors at the blade tip.
## 2 Materials and Methods
### Electrical Conductivity Measurements  
Electrical conductivity image using MRI was performed for image-based analysis of hepato-protective effects of AAE treatment on liver cirrhosis. Acrylic phantom with a 60 mm diameter and an 80 mm height was used for the imaging experiment. The resected liver blocks containing the cirrhotic tissues were positioned inside a cylindrical acrylic phantom, which was filled with conductive material (agarose gel of 0.1 S/m) to support the liver block (Figure 1). The phantom was placed inside the bore of a 3T MRI scanner (Magnetom Trio A Tim; Siemens Medical Solutions, Erlangen, Germany). A multi-echo spin-echo MR pulse sequence was applied to acquire B1 phase maps to reconstruct high-frequency conductivity images of the phantom [21]. The imaging parameters were as follows: repetition time (TR)/echo time (TE) = 1000/10, 20, 30, 40 ms (four echoes), field of view (FOV) = 60 \(\times\) 60 mm\({}^{2}\), matrix size = 128 \(\times\) 128, slice thickness = 3 mm, number of slices = 8, number of excitations (NEX) = 6, and total imaging time = 20 min. Electrical conductivity was reconstructed from optimized B1 phase images obtained from multi-echo spin-echo MR data after multiple pre-processing steps [21]. Details of conductivity image reconstruction followed the work of [PERSON] et al. [22,23].  
To validate the electrical conductivities in the imaging results, the conductivity spectra of the four groups were directly measured using an impedance analyzer (SI1260A, METEK, Stonehouse, UK) (Figure 1). To this end, liver block extracted from four groups was placed in a 10 \(\times\) 10 \(\times\) 10 mm\({}^{3}\) chamber equipped with the electrodes, and the conductivity spectra were measured using a four-electrode method. The frequency range of the conductivity spectra was from 10 Hz to 1 MHz.
## 3 Machine Learning Algorithms
### Kernel-Based Algorithms  
#### 3.2.1 Support Vector Machines  
Support vector machines (SVMs) are a well-established and effective method for classification and regression analysis [36]. They utilize kernel functions to transform the input data into a higher dimensional space, allowing for easy generalization despite the increased computational demands. Kernel functions may be linear, polynomial, or radial basis functions (RBF). SVM's versatility makes it effective for a wide range of data analysis problems. However, due to its multi-parametric nature and error sensitivity, advanced schemes, such as the fuzzy weighted SVM, have been proposed [37].
# Passivity-Based Control for a Micro Air Vehicle Using Unit Quaternions
## 5 Numerical Validation  
In order to validate the proposed mathematical scheme, the model and control law were simulated in a computer programming environment.  
The control gains were selected beginning from identity matrices and adjusted empirically such that stable behavior of the system was well appreciated. \(K_{pr}\) and \(K_{pt}\) are adjusted to ensure the convergence to the desired references for the attitude and position respectively, while \(K_{vr}\) and \(K_{vt}\) act by reducing the setting time and the overshoot for the rotational and translational dynamics respectively. The values applied on the simulation are the following  
\[K_{pt}=k_{pt}I_{3\times 3}\ \,\ \ \ \ K_{pr}=k_{pr}I_{3\times 3}\] \[K_{vt}=k_{vt}I_{3\times 3}\ \,\ \ \ \ K_{vr}=k_{vr}I_{3\times 3}\]  
with \(k_{pt}=5\), \(k_{pr}=50\), \(k_{vt}=4\), and \(k_{vr}=10\).  
Our research team makes every effort to validate most of our algorithms in real experimental platforms. One of the drones we commonly use for tests is the Parrot AR Drone 2 which is cheap and easy to handle for attitude and position experiments, and was modified with a software framework that is compatible with other quad-rotors that our team has developed for more specific applications. For this reason, the mass and inertia matrix for the Parrot AR Drone 2 were needed, thus obtained from [30], and used in our simulations.  
\[J = \left[\begin{array}{ccc}2.04016&0&0\\ 0&1.56771&0\\ 0&0&3.51779\end{array}\right]\times 10^{4}\left[g\,cm^{3}\right]\] \[m = 430.6\,g\]
# A False Negative Study of the Steganalysis Tool Stegdetect
## 2 Related Work
The tool was designed primarily as a bulk analyser for lossless images, it works by classifying images as clean or stego images based on whether or not a pre-defined analysis threshold has been exceeded. In its standard operation, StegExpose is aimed at detecting different methods (nonlinear adaptive encoding, equidistribution and pseudorandom distribution) Least Significant Bit (LSB) originating from many tools including SilentEye [22], OpenPuff [23] and OpenStego [24]. StegSecret [25], like StegExpose, is also an open-source Java-based tool. It features a very simple graphical user interface, and is aimed at high performance bulk analysis, however this comes at the cost of lack of customisation; the tool is less configurable compared to StegExpose. Other approaches have been combined in recent times to enhance steganalysis techniques. For example, the authors in [26] used convolutional neural networks to identify noise in different regions in an image and the relationship between noise in different sub-regions, and then classify images according to whether features of the image indicate the presence of hidden content or not. In [27], the authors use ensemble learning methods to construct effective steganalysis of images, which searches for colour correlativity between pixels and colour channels. Convolutional neural networks are used again in [28] as a method to embed hidden content in cover images. Most of these machine learning approaches suffer from lack of transparency as to how general the method can effectively either embed or analyse hidden content.
## 3 Simulation Results and Discussion
### Electric Field Distribution of Edge-Termination Structures  
Figure 4 shows the three-dimensional electric field distributions of the four edge-termination structures under avalanche breakdown conditions.  
Figure 4a presents the electric field distribution of the conventional FFR. This structure that optimized the space and width parameters supports the highest electric field (E\({}_{\text{max}}=3.95\) MV/cm) by five rings. On the other hand, the poly-trench supports the highest electric field (E\({}_{\text{max}}=2.97\) MV/cm). Although the maximum electric field of the poly-trench is lower than that of the conventional FFR, the breakdown voltage of the conventional FFR is much higher. This is because, in the poly-trench, only the last trench supports the electric field, as shown in Figure 4b. The oxide trench and combined poly-trench with FFR structures maintain the highest electric field: E\({}_{\text{max}}=3.3\) MV/cm and E\({}_{\text{max}}=3.47\) MV/cm, respectively. Therefore, all junctions can support the electric field by five  
Figure 3: (**a**) Potential distributions close to the breakdown voltage of the conventional FFR (space, 1.1 \(\upmu\)m; width, 1.4 \(\upmu\)m), (**b**) The poly-trench (space, 0.5 \(\upmu\)m; width, 2.0 \(\upmu\)m), (**c**) The oxide trench (space, 1.3 \(\upmu\)m; width, 1.2 \(\upmu\)m), and (**d**) A combination of the poly-trench and the FFR (space, 0.7 \(\upmu\)m; width, 1.8 \(\upmu\)m).  
trenches, as shown in Figure 4c,d. Thus, the breakdown voltage characteristics of both structures are higher than those of the conventional FFR.
# Research Progress in Methods for the Analysis of the Internal Stability of Landslide Dam Soils
## 5 Conclusions and Prospects
### Prospects  
1. Currently, most internal erosion tests under the triaxial compression state use vertical seepage, which does not match the actual situation in landslide dams where horizontal seepage is predominant. Therefore, it is necessary to conduct internal erosion tests of landslide dam soils under horizontal seepage conditions.
2. At present, there is limited research on the internal stability of landslide dam soils. The existing criteria used to assess the internal stability of these soils are lacking, especially under the complex stress state. Therefore, it is necessary to conduct laboratory tests considering the coupling of seepage and stress, and to establish methods to assess the internal stability of landslide dam soils based on particle initiation under the complex stress state.
3. One major issue with landslide dam soils is their natural vertical and horizontal heterogeneity. Landslide dams are usually formed by landslides or debris flows. During the deposition process, these soils are subjected to complex stratification and mixing, resulting in significant differences in their physical properties (such as particle size, density, porosity, and permeability) in the vertical and horizontal directions at the same location. This heterogeneity makes the evaluation and prediction of the seepage behavior and stability of dam soils difficult. Therefore, it is necessary for the differences in the physical properties of landslide dam soils to be thoroughly discussed to comprehensively assess and understand their internal erosion characteristics.  
**Author Contributions:** [PERSON]. ([PERSON]) had the idea for the manuscript. [PERSON] collated and analyzed the references, and provided a first draft. [PERSON], [PERSON], [PERSON], [PERSON] ([PERSON]) critically revised the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the National Natural Science Foundation of China (grant No. 41977239) and the Sichuan Science and Technology Program (grant No. 2022 YFS0539).  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors upon request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Proposed Algorithm (NCRA-DP-ACO)
### Pheromone Management
#### 4.2.4 Solution Construction  
Each ant independently constructs VM-to-PM assignments based on shuffled VM requests. The ant thus sequentially chooses the PM and assigns VMs to it. Each ant independently builds a potential solution, which corresponds to its own unique VM-to-PM assignment. To encourage exploration, ants first randomly shuffle the list of requested VMs, preventing any fixed order during placement. Ants carefully evaluate server suitability for each VM, ensuring compatibility based on the available _CPU_, _RAM_, and bandwidth resources according to Equation (12).  
\[\begin{array}{l}\sum\limits_{k=1}^{N}VM_{k}^{CPU}\cdot x_{ik}+VM_{j}^{CPU} \leq PM_{i}^{CPU}\\ \sum\limits_{k=1}^{N}VM_{k}^{RAM}\cdot x_{ik}+VM_{j}^{RAM}\leq PM_{i}^{RAM}\\ \sum\limits_{k=1}^{N}VM_{k}^{BW}\cdot x_{ik}+VM_{j}^{BW}\leq PM_{i}^{BW},\forall ie PM \end{array} \tag{12}\]  
To prevent server overloading after VM placement, Equation (13) is used, incorporating an overutilization threshold (\(Th^{over}\)). This constraint ensures that a VM can be placed on a server with the same _CPU_ capacity if it is not already occupied. However, if the server already hosts a VM, the equation considers the current VM's _CPU_ utilization.  
The purpose of this constraint is to maintain a balance between the _CPU_ utilization of the server and the VMs placed on it. By considering the overutilization threshold, the algorithm ensures that the server's _CPU_ utilization does not exceed the predefined limit. If a server has sufficient _CPU_ capacity available (i.e., it is not yet fully utilized), the algorithm can place a new VM on that server without causing overloading. However, if the server is already hosting a VM, the algorithm considers the _CPU_ utilization of that VM when determining whether placing an additional VM would lead to overutilization.  
\[i=\left\{\begin{array}{ll}\arg\max_{k\in PM^{available}_{j}}\left\{\left(\tau_{ij} \right)\times\left(\eta^{\beta}_{kj}\right)\right\}&if\ q\leq q_{0}\\ P_{ij}\,&otherwise\end{array}\right. \tag{18}\]  
where \(q_{0}\) is a predefined parameter (\(q_{0}\in[0,1]\)) that is used to regulate the ant's exploration and exploitation behaviors, and \(\beta\) (\(\beta\) > 0) is a predefined parameter that determines the relative importance of heuristic information. If \(q\) is less than \(q_{0}\), an exploitate ant will select the server whose pheromone \(\tau\) and heuristic \(\eta\) product \(\left\{\left(\tau_{ij}\right)\times\left(\eta^{\beta}_{ij}\right)\right\}\) is maximal. Otherwise, the ant's behavior is explorative, selecting server \(PM_{i}\) based on the probability selection modeled by Equation (19):  
\[P_{ij}=\frac{\left(\tau_{ij}\right)\times\left(\eta^{\beta}_{ij}\right)}{\sum _{i\in PM^{available}_{j}}\left(\tau_{ij}\right)\times\left(\eta^{\beta}_{ij} \right)},\ \forall i\in\ PM^{available}_{j} \tag{19}\]  
The pheromone concentration provides information about the desirability of a particular VM based on previous movements, while the heuristic guides the ants towards selecting the most promising VMs based on some predefined criteria.
# Fault Detection of Resilient Navigation System Based on GNSS Pseudo-Range Measurement
## 4 Discussion  
The factor graph fusion navigation method can deal with the non-synchronization of navigation information and realize the ability of plug-and-play, which is consistent with the results of [14]. The traditional FDI method is based on INS measurements and GNSS position and velocity measurements. Generally, INS measurement is used as a reference to isolate the satellite fault, which leads to the waste of regular pseudo-range information. On this basis, the proposed FDI method can judge that the pseudo-range of a satellite is faulty. After isolating the faulty pseudo-range measurements, it can still rely on normal measurements to improve positioning accuracy.  
The factor graph navigation FDI method based on pseudo-range can detect GNSS pseudo-range faults effectively. It improves the navigation and positioning accuracy under disturbance and meets the needs of positioning in the complex environment. After isolating the fault pseudo-range measurements, it is equivalent to preventing the fault measurements from polluting the fused navigation information, which effectively improves the reliability of navigation. The positioning accuracy of the proposed FDI method in three directions is improved by 40% to 70%.  
The algorithm and experimental verification in this paper focus on detecting a single-satellite pseudo-range fault. In order to expand the application scope of the algorithm, we will optimize the algorithm framework and experimental scheme in the future and supplement the detection of multi-satellite pseudo-range fault measurements and INS fault measurements. Through the multi-filter architecture, different fault measurement types will be comprehensively detected. In addition, adaptive filtering is widely used in the autonomous navigation system. The two detection thresholds proposed in [21] can calculate the weight of measurement information through the detection function value. We can also use an adaptive weight factor to isolate fault measurements' insignificant errors and reduce the weight of sensor factors in minor errors. It can obtain more accurate and robust navigation information. This will be worth further investigation.
###### Abstract  
In this article, we present new design techniques to improve the gain and impedance bandwidth of short backfire antennas. For the gain enhancement procedure, our approach was to flare the rim of the antenna, which simultaneously led to an increase in the impedance bandwidth of the antenna. Parametric studies were carried out to obtain the optimal flaring angle. The peak realized gain was obtained as 17.2 dBi with an impedance bandwidth of 55% (2.4 dB and 28.6% increase in gain and bandwidth, respectively, compared to the unflared antenna). To further enhance the impedance bandwidth, an inductive iris was added to improve impedance matching at the waveguide aperture. We varied the width of the iris to obtain the optimal width that provided the best gain and impedance bandwidth result of 17.1 dBi and 66% (-40% increase compared to the unflared antenna without iris). To experimentally verify the work, prototypes were fabricated and tested. We found good agreement between simulation and measurement. The results of this study indicate that gain and bandwidth can be enhanced through optimized geometrical modification of the SBF structure. Furthermore, our 3D-printed technique demonstrates a mass reduction compared with conventional metallic structures.  
3D printing; flare; gain enhancement; iris; short backfire antenna; wideband antenna +
Footnote †: journal: _Sensors_  
0
## 2 Background
### COTS RF Hardware  
Despite the incredible capabilities of these emerging technologies, implementing and validating functional prototypes is still a tedious and time-consuming challenge. SDR [60; 61; 62] and RFSoC [18; 19] have greatly simplified this process but still require some expertise to use efficiently. One of the most popular SDR platforms is the Ettus USRP series [63; 64], which supports both the UHD [65; 66] and GNURadio [67; 68; 69] to greatly simplify the development of simple radio applications. For more advanced applications, Xilinx  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Label** & **Tech** & **Carrier** & **Bandwidth** & **Precision** & **Coverage** \\ \hline GPS (PL) & Pseudo- & 1.58 GHz (L1) & 1 MHz (Civ) & 5–100 cm & Global \\  & Range & 1.23 GHz (L2) & 10 MHz (Mil) & 1–5 cm (PL) & \\ \hline GPS (WAAS) & PR & & 15.345 MHz & 1 m & Global \\ \hline GPS (RTK) & PR & & 15.345 MHz & 1 cm & Global \\ \hline LDACS1 & & & 500 kHz & 20 m & 370 km \\ \hline VOR & AoA & 108–118 MHz & 10 MHz & 0.35–1.4\({}^{\circ}\) & 100–300 km \\ \hline DME & RToF & 960–1215 & \(252\times 1\) & (Legacy) & 50 km (Close) \\  & & MHz & MHz & 300 m & 75 km (Mid) \\  & & & & (Modern) & 250 km (Long) \\  & & & & 100 m & \\ \hline (ADS-B) & TDoA & (ADS-B) & (ADS-B) & 50–100 m & (ADS-B) \\ P-WAM & & 1090 MHz & 50 kHz & & 250 km \\  & & 978 MHz & 1.3 MHz & & \\ \hline UHARS & & & 10.23 MHz & 20 cm & 50 km \\ \hline UWB & & & 1 GHz & 1–10 cm & Varied. \\ \hline CHP2 & Pseudo- & 915 MHz (US) & 10 MHz & 0.1–10 cm & 10 km \\  & Range & 783 MHz (EU) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance Comparison of CHP2, PNT, and APNT Technologies [10; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41].  
manufactures a range of RFSoC that integrate processing systems with programmable logic and RF hardware [18]. These platforms enable more advanced features and--naturally--are more difficult to program.