esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Proposed Method
### Description
#### 5.1.2 Confirmation of Jamming Attack on a Node Through '2-Means Clustering' of Node Neighborhood
If _nj_ \(\leq\)_n/2_, then it does the following: (a) Finds the mean jamming index of _jammed neighbors cluster_, \(\overline{\textit{jij}}\) using the formula: \[\overline{\textit{jij}}=\frac{\overset{\eta\textit{j}}{\underset{k=1}{\overset {\sum\limits_{k=1}^{j_{k}}}{\textit{jij}}}}_{n_{j}}}{\textit{n}_{j}}\] (4) (b) Finds the mean jamming index of _non-jammed neighbors cluster_, \(\overline{\textit{jij}}\) using the formula: \[\overline{\textit{jij}}=\frac{\overset{n-\eta\textit{j}}{\underset{k=1}{ \overset{\sum\limits_{k=1}^{j_{k}}}{\textit{jik}}}}_{n-\textit{n}_{j}}}{ \textit{n}_{j}}\] (5) (c) Finds centroid X and Y coordinates of _jammed neighbors cluster_ using the formula: \[(\overline{x_{j}}=\frac{\overset{\eta\textit{j}}{\underset{k=1}{\overset{ \sum\limits_{k=1}^{j_{k}}}{\textit{jik}}}}_{k-\textit{x}}}{\overset{\eta \textit{j}}{\underset{k=1}{\overset{\sum\limits_{k=1}^{j_{k}}}{\textit{jik}}}} _{k-\textit{x}}},\ \ \overline{y_{j}}=\frac{\overset{\eta\textit{j}}{\underset{k=1}{ \overset{\sum\limits_{k=1}^{j_{k}}}{\textit{jik}}}}_{k-\textit{x}}}{\overset{ \eta\textit{j}}{\underset{k=1}{\overset{\sum\limits_{k=1}^{j_{k}}}{\textit{jik }}}}_{k-\textit{x}}})\] (d) Finds centroid X and Y coordinates of _non-jammed neighbors cluster_ using the formula: \[(\overline{x_{nj}}=\frac{\overset{n-\eta\textit{j}}{\underset{k=1}{\overset{ \sum\limits_{k=1}^{j_{k}}}{\textit{jik}}}}_{k-\textit{x}}}{\overset{\underset{ k=1}{\overset{\sum\limits_{k=1}^{j_{k}}}{\textit{jik}}}}_{k-\textit{x}}},\ \ \overline{y_{nj}}=\frac{\overset{\underset{k=1}{\overset{\sum\limits_{k=1}^{j_{ k}}}{\textit{jik}}}}_{k-\textit{x}}}{\overset{\underset{k=1}{\overset{\sum \limits_{k=1}^{j_{k}}}{\textit{jik}}}}_{k-\textit{x}}})\] (7) (e) Finds the square of the distance, _dj_ of the node under consideration from the centroid of the _jammed neighbors cluster_ using the formula: \[d_{j}^{\ 2}=(x-\overline{x_{j}})^{\ 2}+(y-\overline{y_{j}})^{\ 2}\] (8) (f) Finds the square of the distance, _dnj_ of the node under consideration from the centroid of the _non-jammed neighbors cluster_ using the formula: \[d_{nj}^{\ 2}=(x-\overline{x_{nj}})^{\ 2}+(y-\overline{y_{nj}})^{\ 2}\] (9)(g)If: \[\big{(}\overline{\dot{y}}\,/\,d_{j}^{\ 2}\big{)}\geq\big{(}\overline{\dot{y} \dot{y}}\,/\,d_{nj}^{\ 2}\big{)},\] then it declares that the node is jammed; otherwise, it declares that the node is not jammed and then deletes its name from the list of jammed nodes.
## 3 Results and Discussion
### Performance Analysis
#### 3.1.3 Communication Load  
In order to maintain the state of the link between each pair of nodes, all control packets are periodically distributed by local controllers, and then a routing table is constructed at each node on the routing path. This method makes the source vehicle not broadcast a routing request message to neighbor vehicles like AODV protocol, which will alleviate the multicast traffic flooding problem in the routing discovery process to a large extent.
# Aerospace
## 9 Additional Mission Concepts
### Mars Global Imaging Constellation  
Small satellites such as Planet's Dove 3U CubeSats, which weigh less than 5 kg (and come at a fraction of the cost of large imaging satellites), have enabled a revolution in Earth observation [50]. A future Discovery-class mission may extend such a capability to Mars, enabling scientists to image the entire surface repeatedly. Assuming each satellite is about 25 kg and the ADEPT entry system is 50 kg, a single InSight-like cruise stage can carry about 10 of these small satellites in a single launch. On approaching Mars, the cruise stage will perform the B-plane targeting maneuvers before releasing each small satellite, which will then aerocapture into low-circular orbits and establish a global imaging constellation.
## 2 Materials and Methods
### Surface Treatment and Coating Process  
The plasma treatment was carried out in a low-pressure device (Zepto Diener electronic GmbH, Ebhause, Germany) using oxygen as a carrier gas (Carburos metalicos, Spain). The oxygen supply contains less than 500 ppb of H\({}_{2}\)0 and less than 400 ppb of N\({}_{2}\). The oxygen pressure inside the chamber was fixed at 1.8 mbar. The treatment was applied at a power of 30 W for 1 minute.  
The commercial aloe vera juice was centrifuged at 3000 rpm for 15 min in a Mistasel BL centrifuge (JP Selecta), in order to separate the water-soluble fraction (rich in acetylated polysaccharides) from the cellulose-rich solid fraction. The water-soluble fraction (the supernatant) was used to carry out the coating of the structures and it contains, apart from the bioactive polysaccharides, phenolic compounds, soluble carbohydrates, proteins and minerals [24; 28]. The supernatant has a pH value of around 4 (measured with a sensION(tm+PH1 pHmeter \(\pm\)0.01, HACH). The slightly acid behavior of the extracts can be explained by the presence of different organic acids (acetic acid, lactic acid, succinic acid, etc.) [29] Three types of samples of aloe vera extracts were obtained through pH adjustment: a pH 3 solution (adjusted with HCl 0.018 M); a pH 5 solution (adjusted with NaOH 0.5 M); and the as-obtained gel with a pH of 4 (pH was adjusted when needed).  
Each scaffold to be tested was individually immersed in 1 mL of aloe vera extract solution and stirred for 3 h, allowing the interaction between the functional groups created on the surface of the plasma-treated scaffolds and the functional groups in the polysaccharides' structures to take place. Afterwards, the samples were rinsed with 70% ethanol to remove the excess of aloe vera which was not able to attach to the surface. Furthermore, as polysaccharides are not soluble in ethanol [30], their precipitation helps to fix the coating to the surface of the 3D-printed PLA scaffolds.
## 2 Materials and Methods
### Specim Preparation  
The preparation of the nano-tips for the HRTEM analysis is comparable to those of TEM (transmission electron microscope) lamellas which is state of the art for investigating material compositions. The basic principle of the TEM is to irradiate the sample with a high-energy electron beam. A detector on the backside intercepts the electron beam andallows imaging of the atomic structure of the material components. To achieve a high resolution of the nanostructure, the material thickness must not exceed a few 10 nm. Since such dimensions are possible with the FEBID and FIBID, this is the standard technology for TEM specimen fabrication. In this study, the differences in the tips depending on the ionic current used are elaborated. The FEB tips are deposited by a 340 pA electron beam current with an acceleration voltage of 5 kV. For the deposition of the FIB tips, currents of 9 pA, 46 pA, 90 pA, and 260 pA are set in addition to the accelerating voltage of 30 kV. The process of preparation of the PtC nano-tips is shown in Figure 2.  
To fabricate the PtC nano-tips, a standard Omniprobe lift-out Cu (copper) grid is used and placed on a FIB holder (Figure 2a). This is necessary to ensure compatibility for the TEM. Usually, a lift-out grid is used to place a previously fabricated lamella by the FIB on it. In the current study, the whole preparation process is provided directly to the grid. After cleaning the top of the grid by a large-scale cleaning process with the FIB, the PtC bumps for the fabrication of the nano-tips are deposited. Figure 2b shows the principle and result of the deposition of five bumps with ion currents of 9 pA, 46 pA, 90 pA, and 260 pA, as well as an electron current of 340 pA. During the deposition, the precursor gas is induced by the GIS needle. Unlike the FEBID, it is not possible to directly fabricate sharp tips of a few 10 nm in thickness using the FIB deposition. Therefore, the bumps are patterned subsequently by the FIB with a minimal and sensitive ion current of 1.5 pA (Figure 2c). This step is the most challenging part to provide suitable structures in the nanoscale for the HRTEM analysis. Since the imaging with the ion beam comes along with a destructive removal of the specimen, several short steps with a targeted patterning of the bumps from different angles and directions are necessary. Figure 2d,e shows the result exemplarily for the 260 pA FIB tip in the side view and the 340 pA tip deposited by the FEB. As the tip becomes thinner, as more information can be found by the HRTEM about the material's atomic structure.  
Figure 2: Process of preparation of the PtC nano-tips for the HRTEM analysis with (**a**) the Omniprobe lift-out grid, (**b**) the FIB and GIS deposition of PtC bumps, (**c**) the principle of patterning of the bumps to sharp nano-tips by a low FIB current of 1.5 pA with (**d**) radii r approximately of 10 nm and thickness d of a few 10 nm and (**e**) the deposited FEB nano-tip without additional patterning. Above the principle of the process step and below the SEM image are shown.
# Design of a Compact Multiband Monopole Antenna with MIMO Mutual Coupling Reduction
## 3 Antenna Implementation and Measurement Results  
This chapter covers the implementation details and the measurement results of the proposed antenna designs, as shown in Figure 13. The antennas were fabricated on a single-layer FR4 board with a thickness of 1.6 mm, a relative permittivity (\(\varepsilon_{r}\)) of 4.4, and a copper thickness of 35 um. Their performance was evaluated using an Agilent PNA network analyzer--N5277A and an anechoic chamber. The key parameters measured include reflection coefficients, radiation patterns, and gain.  
Figure 11: The radiation pattern of the MIMO antenna at 3.47 GHz: (**a**) without slits and with antenna 1 excited and antenna 2 terminated, or (**b**) with both antennas excited; (**c**) with slits and with antenna 1 excited and antenna 2 terminated, or (**d**) with both antennas excited.  
Figure 12: The current distributions of the MIMO antenna with both antenna 1 and antenna 2 excited. (**a**) At 2.46 GHz with slits. (**b**) At 3.47 GHz with slits.
## 4 The Proposed Distributed Image Codec
### The Encoder
#### 4.2.2 Adaptive Block Classification  
The adaptive block classification scheme can be deployed for encoders with more computing resources. This scheme requires the creation of a dictionary of CS measurements of key blocks. This  
Figure 2: _Cont._  
dictionary is initially empty. The first block of the image is always a key block. The CS measurements of this block become the first entry of the dictionary. For each subsequent image block, the MSE between its CS measurements and the entries in the dictionary is computed. If any of these MSEs computed falls below a threshold, \(\lambda\), then this block is classified as a WZ block and will be encoded at the lower rate. Otherwise, it is a key block, and its measurements are added to the dictionary as a new entry. In order to reduce the resources required, the size of the dictionary can be limited. When the dictionary is full and a new entry is needed, the oldest entry will be discarded.  
The threshold, \(\lambda\), is determined adaptively as follows. For each image block, \(i\), the threshold is obtained by:  
\[\lambda=C\cdot median(|y_{i}|) \tag{10}\]  
where \(y_{i}\) are the CS measurements of block \(i\) and \(C\) is a constant. The value of \(C\) controls the relative number of key blocks and WZ blocks. If \(C>1\), the number of WZ blocks will be increased. Choosing \(C<1\) will increase the number of key blocks. This method reduces the encoding delay compared with SD-based methods described in Section 3. The entire encoding process is summarized in Algorithm 1.  
```
Input:\(y_{w},D\) for each column \(i\)\(inD\)do  Calculate \(r[i]=MSE(y_{w},D)\) endfor  Calculate \(\tau=min[r]\)  Estimate \(\lambda=C.median(|y_{w}|)\) if\(\tau>\lambda\)then \(y_{k}\) (key block) else if\(\tau<\lambda\)then \(y_{w}\) (WZ block) endif endif
```  
**Algorithm 1** Adaptive CS encoding.
###### Abstract  
This study examined the impact of various smoothing techniques on acceleration data obtained from a Global Navigation Satellite System (GNSS) device during accelerating and decelerating movements, resembling those commonly observed in team sports. Eight participants performed six different accelerating and decelerating movements at different intensities and starting speeds for a total of 46 trials each. The movements were collected concurrently at 10 Hz using a GNSS device (Vector S7, Catapult Sports) at 100 Hz using a motion analysis system (Vicon). Acceleration data were smoothed using (I) a fourth-order Butterworth filter (cut-off frequencies ranging from raw to 4.9 Hz), (II) exponential smoothing (smoothing constant ranging from 0.1 to 0.9), and (III) moving average (sliding window ranging from 0.2 s to 2.0 s). To determine the ability of a GNSS to quantify acceleration, a variety of measurement indices of validity were obtained for each movement and each smoothing technique. The fourth-order Butterworth filter with a cut-off frequency of 2 Hz (mean bias 0.00 m s\({}^{-2}\), 95% LoA \(\pm\) 1.55 m s\({}^{-2}\), RMSE 0.79 m s\({}^{-2}\)) showed the strongest relationship with the Vicon data. These results indicate that this smoothing technique is more accurate than those currently used and accepted on GNSS devices in the sports science community.  
a Article  
Secrecy of GNSS-Derived Acceleration Data for Dynamic Team Sport Movements: A Comparative Study of Smoothing Techniques. _Appl. Sci._**2024**, _14_, 10573. [[https://doi.org/10.3390/app142210573](https://doi.org/10.3390/app142210573)]([https://doi.org/10.3390/app142210573](https://doi.org/10.3390/app142210573))
# LungVision: X-ray Imagery Classification for On-Edge Diagnosis Applications
## 4 Experimental Results and Discussion
### Model Size  
In terms of quantization impact on model sizes, all techniques reduced the sizes of original models by different ratios. In general, Quantization-Aware Training (QAT) achieved the highest reduction percentage across all models, with the exception of DenseNet121 with an average reduction ratio of 76.62%. Integer quantization using representative data (INT-Rep) achieved similar percentages, resulting in an average reduction percentage of 75.59% across all models. Floating Point 16 (FP16) optimization seemed to have the least reduction ratio, with an average of 13.84% but with a much higher standard deviation of 6.53%, in comparison to approximately 1.9% and 1.4% for INT-Rep and QAT techniques, respectively. Across all models, MobileNetV2 was the most resilient model to quantize, with an average reduction ratio of 50.14%, whereas NASNet was the easiest to quantize, with an average reduction of 59.14% across all three quantization techniques.  
Table 2 provides a more detailed look into the size reduction using different optimization techniques across different CNN models.
# A Lightweight High Definition Mapping Method Based on Multi-Source Data Fusion Perception
## 3 Local Mapping on Vehicle
### Semantic Element Ranging
#### 3.2.2 Ground Element Ranging  
To estimate the depth of ground elements, the near-ground plane assumption is commonly used. The derivation formula is as follows:  
\[X_{t}=\frac{-hK^{-1}x_{t}}{n^{T}K^{-1}x_{t}} \tag{1}\]  
where \(X_{t}\) represents the feature point on a 2D image captured by the monocular camera at time \(t\), which corresponds to pavement features, such as lane lines and ground arrows. \(K\) is the camera's internal parameter, which is a 3 \(\times\) 3 linear projection matrix. By inverting \(K\), we can obtain the points on the normalized projection plane using \(X_{t}\). The normal vector of the ground plane equation is denoted by \(n\), and \(h\) represents the camera height above the ground, both of which can be obtained through external parameter calibration. Using these parameters, we can recover the depth information of the 3D points corresponding to the ground features under the assumption of a near-ground plane.
## 2 Methods and Materials
### Antenna Array  
There are several ways to generate different beam shapes. In the following work, this was achieved by using a uniformly spaced planar array composed of several isotropic antenna. For this type of array, the array factor (AF) is expressed as,  
\[AF(\theta,\phi)=\sum_{m=0}^{n-1}\sum_{i=0}^{n-1}I_{lm}\exp\left(jk\sin\theta( i\Delta x\cos\phi+m\Delta y\sin\phi)\right), \tag{3}\]  
where \(n\) is the number of isotropic antennas on each side of the array. \(I_{im}\) is the output power of each antenna in the array, which is a complex value that can be decomposed  
Figure 1: Vehicle-to-vehicle (V2V)-intersection environment.  
as \(I_{im}=A_{im}e^{i\phi_{im}}\), with amplitude \(A_{im}\) and phase \(\phi_{im}\) for each of the antennas. Since the array is uniformly spaced, there is a constant separation between antenna elements in each direction, represented by \(\Delta x\) and \(\Delta y\). \(\theta\) is the elevation angle perpendicular to the planar array. When an algorithm optimizes the beam shape, \(A_{im}\) and \(\phi_{im}\) are the parameters that it can control, since the beam shape itself cannot be directly controlled by it.  
By using the AF given in Expression (2), the radiation pattern for the antenna array at an angle \(\phi\) and an elevation angle \(\theta\), \(Y(\theta,\phi)\), can be computed as,  
\[Y(\theta,\phi)=R(\theta,\phi)AF(\theta,\phi). \tag{4}\]  
where \(R(\theta,\phi)\) is the radiation pattern of a single antenna in the array. For an isotropic antenna, the value for \(R\) is uniform across all angles. Using this, the radiation pattern at a given angle is proportional to the value of the AF in the same direction:  
\[Y(\theta,\phi)=AF(\theta,\phi). \tag{5}\]  
During the simulation, the previous expression was used to compute the value for the radiation pattern at a given direction.
# A Methodology to Model the Rain and Fog Effect on the Performance of Automotive LiDAR Sensors
## 4 Modeling of the Rain and Fog Effect in the Virtual LiDAR Sensor
### Rain Module  
The rain module generates virtual rain and calculates the extinction coefficient \(\sigma_{ext,rain}\) and backscattered coefficient \(\beta_{back,rain}\).  
Virtual Rain Generation  
Virtual rain is generated using the Monte Carlo Simulation. Monte Carlo Simulation uses a Mersenne Twister 19937 pseudo-random generator [22] to sample the given DSD using the inverse transform sampling method [23]. Each raindrop is generated individually based on an underlying DSD and terminal velocity. A similar approach was introduced by [PERSON] et al. [12] to generate unwanted raw data within the sensor models. DSD is the distribution of the number of raindrops by their diameter. Many types of DSD are described in the literature, including the Marshall-Palmer distribution, Gamma distribution, and Lognormal distribution [24; 25; 26]. In this work, we have used the Marshall-Palmer rain distribution and the large-scale rainfall simulator distribution recorded by a real dishorter.
## 2 Materials and Methods
### Data Collection and Quality Assessment  
Three independent reviewers (CRL, CW, CLL) extracted relevant data from the included studies. The extracted data comprised study characteristics such as author, year of publication, region of study, data source, study design, and period of study. The sample size, patient age, inclusion criteria for each study, and specific definitions of each treatment arm were also recorded. Additionally, outcomes of interest such as pain scales, physical function scores, and adverse events were documented. Two reviewers (CRL, SHLT) evaluated the risk of bias in the included studies and the quality of evidence in the study outcomes using Cochrane ROB 2.0 [40] and the GRADE (Grading of Recommendations, Assessment, Development, and Evaluations) systems, respectively [41]. Any discrepancies among the reviewers were solved through discussion.
## 6 Conclusions  
The complex monitoring of vertical land motions corresponding to geological structure of coastal and river areas in Northwestern Poland are presented in this article. In order to locate points (1 and 3), archival catalogues of levelling line measurements made in 1975 and 2000 were used [21].  
The second segment of this system has already been implemented. A point (4) on unstable geological ground was located in the vicinity of the site. Periodic GNSS observations of benchmarks installed close to the EcoGenerator structure were planned [21]. The first GNSS measurement was carried out in November 2020 and the second in May 2021. The results of the second measurement allowed us to calculate the vertical displacements of the benchmarks and to verify the stability of the reference system (first segment of the system). Comparison of the heights obtained with the use of precise levelling and satellite levelling confirmed the stability of the positions. Benchmarks located in the direct vicinity of the second segment of the monitored infrastructure showed a displacement of 1.4 mm during one month. After the second measurement, the reference points proved to be stable and the point located in the immediate vicinity of the EcoGenerator plant settled 19 mm.  
Alignment of benchmarks in relation to both stable points and the nearest reference stations can significantly increase the accuracy of the measurements. The nearest reference stations can also be replaced by stable benchmarks for 3D measurements.  
In connection with planned investments on several Oda islands, it is justified to conduct the complexity monitoring of the height of benchmarks in the Miedzyodrze islands. In order for the obtained results of these deformations to be interpreted in interdisciplinary areas in terms of cause and effect, not only effect, it is necessary to start the research at the stage of the still-undisturbed rock mass.  
A porous structure which, when under pressure, is subject to shrinkage and removal of water from the pores [20], is the cause of vertical displacement of organic soils.  
Conceptualization, M.Z. and P.Z; Methodology, M.Z.; Validation, P.Z. and I.G.; Formal analysis, M.Z. and I.G.; Investigation, M.Z.; Resources, M.Z.; Data curation, M.Z. and I.G.; Writing--original draft preparation, M.Z., I.G. and P.Z.; Writing--review and editing, M.Z. and P.Z.; Visualization, P.Z.; Supervision, P.Z.; All authors have read and agreed to the published version of the manuscript.  
This research was funded from statutory sources of MUS 2022.  
**Informed Consent Statement:** The datasets analyzed in this study are managed by Polish Maritime University of Szczecin.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction
* By weighting the training data according to their shared neighbors, the significance of fault data is enhanced. * The method is robust to outliers, including faulty data. * By weighting neighbors based on their distances, the method can effectively detect faults in data that are close to healthy data. * It can improve the limitations of conventional AAKR and distance-based methods that rely on simple distance measurements. * Effective fault detection can be performed even when healthy and faulty data are close together. The performance of the proposed method is evaluated by applying it to the Tennessee Eastman Process (TEP), a benchmark dataset widely used in the field of fault detection,  
Figure 1: Development of the events (driftwise, stepwise, malfunction, and failure) from a fault [10]. and a real-world fault case in a circulating fluidized bed. The false alarm rate and fault detection rate are compared for normal data in both cases. The remainder of this paper is organized as follows. Section 2 describes the conventional AAKR, and Section 3 provides a fault threshold for fault detection. Section 4 explains the proposed method, and Section 5 analyzes the experimental results comparing the conventional methods and proposed method on benchmark data and actual failure data. Finally, Section 6 discusses conclusions and future work.