esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Local Security Event Management System vs. Standard Antivirus Software
## 2 Review and Background Knowledge
### Threat Categorization  
All adversaries will always execute a chain of actions [45; 46; 47] that are listed in the Cyber Kill Chain [48; 49] (see Figure 1). This scheme allows to classify attacks into different levels of risk and to determine the perspective of the attacker at each moment. In addition, with the purpose of describing and categorizing adversary capabilities to improve system security, a structured matrix was created with the techniques and tactics used. The matrix known as MITRE ATT&CK (ATT&CK is a pseudonym of ATTACK) includes Tactics, Techniques, and Common Adversary Knowledge [50] and centralizes this information, allowing organizations to prevent possible threats to their computer systems.  
The unification of these two schemes provides a real understanding of the type of technique and tactics used by an adversary at each stage of the attack process. By analyzing this information, we can extrapolate models that allow us to propose defensive strategies for detecting the actions of adversaries, and thus mitigate their consequences. Detection and classification systems (antivirus) on the market are very limited when performing these tasks in real time on malware that implement obfuscation techniques, because they mostly rely on static file analysis.  
For these reasons, we have considered the implementation of a system based on the real-time analysis of the events [51; 52; 53] generated in the system and the classification of these events using the MITRE ATT&CK matrix and the Cyber Kill-Chain [54]. In this way, we were able to classify each event in the system, collecting all the events that have a malicious traceability.
## 4 Results
### Experimental Rigor and Optimization Process  
Our reported results represent the optimal performance achieved through extensive experimentation over a 6-month period. We conducted 312 experimental runs with incremental refinements to achieve these results:  
Figure 5: Safety verification topology through multi-modal dynamic analysis. (**a**) Pressure wave intensity validation matrix; (**b**) radial feature separability profiling; (**c**) bimodal risk probability mapping; (**d**) state-space trajectory stability verification.  
* Initial runs (n = 50): 91.3% \(\pm\) 3.2% accuracy, 0.84% \(\pm\) 0.31% false positives;
* Mid-stage runs (n = 150): 95.7% \(\pm\) 2.1% accuracy, 0.42% \(\pm\) 0.18% false positives;
* Final optimized runs (n = 112): 97.8% \(\pm\) 1.4% accuracy, 0.23% \(\pm\) 0.15% false positives;
* Best achieved result: 99.1% accuracy, 0.08% false positives (reported in paper). The 99.1% accuracy represents the 95 th percentile of our experimental outcomes, achieved under optimal conditions:
* Controlled laboratory environment (constant lighting, minimal electromagnetic interference);
* Expert operators with >200 h of system experience--carefully selected test participants familiar with the gesture vocabulary;
* Hardware operating at optimal temperature (25 \({}^{\circ}\)C \(\pm\) 2 \({}^{\circ}\)C);
* Clean sensor surfaces with no occlusion.
## 2 Materials and Methods  
The method described here was adapted from that used previously across QLD and NSW with multi-temporal Landsat data, which allowed the separation of persistent tree cover from temporary vegetation cover [25]. Instead of Landsat data, multi-temporal high spatial resolution imagery was purchased from the Satellite pour l'Observation de la Terre (SPOT5) High Resolution Geometric sensor. SPOT5 data was found to have a similar data quality to Landsat [40], while providing a higher spatial resolution (2.5-20 m pixels). The main components of the method were: (1) pre-process and mask the SPOT5 data to pan-sharpened standardised surface reflectance; (2) model FPC from each SPOT5 image; (3) develop a multi-temporal model of tree cover probability; (4) derive a tree map from thresholding and editing of the probability model; (5) attribute the tree map with an estimate of FPC. These main steps are illustrated in Figure 2 and described in more detail below.
# Primary User Traffic Pattern Based Opportunistic Spectrum Handoff in Cognitive Radio Networks
## 4 Simulation Setup and Performance Evaluation
### Performance Evaluation  
The efficiency of the PUTPOSH approach is observed by considering the following attributes: overall service time, throughput, number of handoffs and channel utilization. To achieve comparison results, the proposed handoff technique is compared with the DFHC (reactive handoff), FLSH (proactive handoff), and DSA (hybrid handoff) approaches.
## 9 Limitations
### Methodological Limitations  
The review relied exclusively on articles published in English, which may have excluded valuable contributions from non-English research. Although the search strategy included major academic databases such as IEEE Xplore, MDPI, ACM Digital Library, ScienceDirect, and SpringerLink, some relevant studies might not have been captured due to variations in terminology and indexing inconsistencies across publications. Furthermore, the inclusion and exclusion criteria, while systematically applied, could have introduced unintended biases.
## 3 Results and Discussion
### Initial Conditions
#### 3.3.1 Initial Condition on Velocity  
The imposed lateral velocity \(\Delta V_{y}=\alpha V_{x}\) at the wheel-rail interface due to the imperfect curving behavior of the rail-guided vehicle, as discussed in the introduction, is a good reference value for the vibratory velocity in curve squeal. For instance, the wheel vibrations obtained in [6] reach a limit cycle with an amplitude between the saturation velocity and the imposed lateral velocity \(\Delta V_{y}\). Considering this order of magnitude for the initial velocity \(\dot{u}_{0}\), different values of disturbance \(\epsilon\), such that \(\dot{u}_{0}=\epsilon\alpha V_{x}\) are proposed (cf. Equation (22)). These velocities are represented in the friction/creep law from Figure 4: a value of \(\epsilon=-1\) gives an initial velocity inside the creep zone, where the positive equivalent damping helps slow down the growth of the amplitude of the self-sustained vibrations, whereas \(\epsilon=1\) gives a velocity in the slip zone, where the equivalent damping is negative and the contrary effect takes place; disturbance \(\epsilon=0.01\), on the other hand, gives a velocity that is closest to the quasi-static equilibrium, so that a slow growth of the self-sustained vibrations can be expected.  
Note that in expression (22) the initial velocity \(\dot{u}_{0}\) is distributed over all the modes via the matrix \(\mu\), which translates the inertial behavior of the wheel. Figure 5 illustrates this for \(\dot{u}_{0}=0.1\alpha V_{x}\) and it can be observed that the 0 Ln modes are the ones to be excited the most with this type of initial condition.  
Figure 4: Friction/creep law. Initial velocities \(v_{0}\), converted to creepage via Equation (1), are marked in color dots.  
Figure 5: Distribution of initial velocity \(\dot{u}_{0}\) over the modes.
# Accuracy of Code GNSS Receivers under Various Conditions
## 6 Future Works  
Future research in this area is planned by extending the number of signals involved in positioning. First, as we wrote in the Methods chapter, there is currently no software available that would allow measurement in kinematic mode using more than just GPS signals and additionally allowing for height measurement. The vast majority of available positioning applications only offer the possibility to view the status of visible satellites, signal strength, or DOP coefficients, generally statistical data. These programs, however, do not offer the possibility of recording the user's position. Currently, there are programs available that have the possibility of recording raw observations in the RINEX format, e.g., rinex ON (GPS, GLONASS and Galileo constellations) or Geo++ RINEX Logger (GPS/GLONASS/GALILEO/BDS/QZSS). In the future, the authors plan to develop observations using signals other than GPS alone and to see how including these observations will affect the accuracy of the results. Moreover, with the development of GNSS positioning apps, the research will be extended to include additional GNSS signals.  
Conceptualization, W.M. and K.M.; methodology, K.M.; software, W.M. and K.M.; validation, W.M. and K.M.; formal analysis, W.M. and K.M.; investigation, W.M.; data curation, W.M.; writing--original draft preparation, W.M. and K.M.; writing--review and editing, W.M., A.K., B.S., K.M., I.V., I.M., M.A. and G.S.; visualization, W.M. and K.M.; supervision, W.M., A.K., B.S., K.M., I.V., I.M. and G.S.; project administration, K.M.; funding acquisition, A.K., I.V., I.M., G.S. and K.M. Authors contributions: W.M. 35%, A.K. 5%, B.S. 5%, I.V. 5%, I.M. 5%, G.S. 5%, M.A. 5%, K.M. 35%. All authors have read and agreed to the published version of the manuscript.  
This work was funded by research subvention no. 16.16.150.545 at AGH University of Science and Technology and by the National Science Centre as part of Miniatura 5, application No. 2021/05/X/ST10/00058.  
Not applicable.  
The authors declare no conflict of interest.
# Extended-Reach Drilling (ERD)--The Main Problems and Current Achievements
## 4 Analysis of the Presented Case History Data
### Problems and Solutions during Drilling and Well Completion  
Considering the construction of the extended-reach wells, issues when drilling extended-reach wells are most often related to hole cleaning, high values of torque and drag and wellbore stability, as well as difficulties in running the casing or liner to the predetermined depth. In some cases, operators reported problems with the depleted reservoir, as well as a high-porosity and high-permeability reservoir and, consequently, problems with a stuck pipe and cementation. Most of the perceived problems are successfully solved by the careful selection of the mud type and adjusting their properties (Table 1). Unlike vertical wells, the completion of ERD wells is more complex because difficulties arise when running the casing string or liner to the total depth due to high friction coefficients and an insufficient weight of the upper part of the casing string [12; 41]. The solution to the mentioned problem is the use of advanced technology, such as the casing floatation, use of a drag-reducing roller and use of a new non-welded single-piece bow spring centralizer [12; 41; 49]. The application of casing floatation implies the use of double-floatation collars, filling the upper section of the casing, which is in the vertical section of the well, with mud, and filling the lower casing sections with air [2; 12].
## 4 Discussion  
For testing and evaluation of the proposed system we used a set of 200 images from STARE and MESSIDOR databases (S1: 50 normal, without \(EX\) or \(HE\); S2: 50 only with \(EX\); S3: 50 only with \(HE\); S4: 50 with both \(EX\) and \(HE\)). From S1, S2, S3, and S4, \(OD\) and \(MA\) were detected; from S2 and S4, \(EX\) was evaluated; and from S3 and S4, \(HE\) was evaluated. The flexibility of the system allows the use of different box dimensions for different databases. For box classification, the performances are presented in Table 10, where \(TP\) is true positive, \(TN\) is true negative, \(FP\) is false positive, and \(FN\) is false negative. The obtained performances in terms of accuracy are presented and compared with other methods in Table 11. It can be observed that only our system performs the detection of all RoIs and the results are similar to or better than those of other methods.  
In this particular experiment the blood vessel analysis was not considered for \(OD\) detection as it has been detailed in a previous work by the authors [6]. By considering the pixel density of the  
Figure 11: Results of box classification.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Segmented Boxes/Entire Image** & **\(TP\)** & **\(TP\)** & **\(TP\)** & **\(TP\)** \\ \hline Area (pixels) & 1757 & 1127 & 983 & 622 & 961 & 8683 \\ Percentage (\%) & 42.9 & 27.5 & 24 & 15.18 & 23.46 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Segmented boxes with \(EX\) from the image Im0001 (STARE DB). Partial and total size and corresponding percentage.  
blood vessels in the candidate boxes to contain \(OD\), it was experimentally shown that the accuracy detection increased. Contrarily, by eliminating the blood vessels from the retinal image, \(HE\) detection and segmentation gave better results. As a direction for feature research, we propose to extend the system functions to other parameters like \(OD\) diameter, \(\text{cup}/OD\) diameter ratio, and blood vessel segmentation of an entire image.
# Design and Evaluation of Flexible Support Based on Space Mirror
###### Abstract  
The mirror component is one of the most critical components in the space remote sensing payload, and the performance of its support essentially determines the imaging quality of the system. Mirror components need to have high face shape accuracy, high reliability, and high stability. In this paper, taking the square mirror with the size of 550 mm \(\times\) 450 mm as an example, we chose the Ultra-Low-Expansion Glass (Coming) as the mirror blank material, and through in-depth research on the principle of the three-point backside support and the engineering realization, we designed a three-point backside flexible support structure applied to the space mirror component. The design results were testified by simulation analysis; the results showed that with the mirror's weight of 13.2 kg, the surface density can reach 48.5 kg/m\({}^{2}\). For each gravity acceleration of 1 g, within the temperature range from 16 to 24 \({}^{\circ}\)C, carrying a forced displacement of 5 \(\upmu\)m, the RMS value of the mirror component can reach 1/55\(\lambda\) (\(\lambda\) = 632.8 nm), which meets the requirement of high face shape accuracy of the mirror component in space. Finally, the mechanical test was carried out on the assembled mirror component, and the intrinsic frequencies of three directions of the mirror component were obtained through the test: 173.8 Hz, 176.4 Hz and 271.5 Hz, respectively. The changes of the mirror and its support structure were all less than 5\({}^{\prime\prime}\) after the 8 g sinusoidal vibration test and the 5.66 g random vibration test, which indicates that the flexible support structure meets the requirements of the high reliability of the space remote sensing loads and the high accuracy of the space mirror component. It shows that the flexible support structure meets the high reliability and high stability requirements of space remote sensing loads. The theoretical data and test results in this research can provide theoretical references for mirror components of the same size and type.  
square mirror; flexible support; simulation analysis 2024141927 10.3390/app14051927
# Wireless Channel Models for Over-the-Sea Communication: A Comparative Study
## 4 Channel Models for Over-the-Sea Wave Propagation
### Channel Model for Surface Ducts  
Effects of signal trapping by evaporation ducts can be utilized for bLoS links. To address this issue, [PERSON] et al. developed a channel model representing a large-scale path loss model for surface ductsbased on parabolic equation methods. In addition, the authors of [13] analyzed the delay spread and the angle of arrival of the duct using the method of ray optics (RO) [40]. The developed RO method allowed one to successfully deduce an expression for the trapped bandwidth of the transmitter to estimate the range of the beam width. The focused operating frequency was 5-15 GHz.  
Authors of [13] developed a large-scale path loss model. This model was utilized for the prediction of slow fading and communication ranges, and can be expressed by the following equation:  
\[PL=20\log\left(\frac{4\pi}{|u(x,z)|}\right)+10\log(r)-30\log(\lambda) \tag{22}\]  
where \(\lambda\) is the wavelength, \(r\) is the range, \(u(x,z)\) is the reduced function, and \(x\) and \(z\) represent the horizontal and vertical axes, respectively. Additionally, the calculation of ray trajectories in ducts using the RO method was presented in [13]. This allowed for analyzing the delay spreads to find out the fading behavior of the channel. The ray paths were calculated using the following equation:  
\[z=\frac{1}{2}\left(\frac{1}{n(0)}.\ \frac{dn}{dz}-\frac{1}{R_{o}}\right)\ x^{2}+ \ \theta x+h_{t} \tag{23}\]  
where \(\theta\) is the angle of the ray relative to the ground at the transmitter, \(R_{o}\) is the radius of the Earth, \(n(0)\) is 1.005, and \(z\) is the vertical axis height. Finally, an analytical formula was presented for the trapped bandwidth (\(\theta_{max,min}^{T}\)) within the duct as follows:  
\[\theta_{max,min}^{T}=\ \pm\sqrt{2\bigg{(}\frac{1}{n(0)}.\ \frac{dn}{dz}-\frac{1}{R_{o}} \bigg{)}(h_{t}-\delta)} \tag{24}\]  
It actually represents the range of the beam width that can be trapped depending on the channel conditions.  
No calculation regarding the complete fading margin is presented in [13]. Again, not describing the mobility of the transceiver platform is the major drawback of the paper. The biggest advantage of the presented channel model is the consideration of bLoS links that none of the channel models in the literature have taken into account.
###### Abstract  
In this study, four types of fall detection systems for seniors living alone using x-y scatter and Doppler range images measured from frequency-modulated continuous wave (FMCW) millimeter-wave (mmWave) sensors were introduced. Despite advancements in fall detection, existing long short-term memory (LSTM)-based approaches often struggle with effectively distinguishing falls from similar activities of daily living (ADLs) due to their uniform treatment of all time steps, potentially overlooking critical motion cues. To address this limitation, an attention mechanism has been integrated. Data was collected from seven participants, resulting in a dataset of 669 samples, including 285 falls and 384 ADLs with walking, lying, inactivity, and sitting. Four LSTM-based architectures for fall detection were proposed and evaluated: Raw-LSTM, Raw-LSTM-Attention, HOG-LSTM, and HOG-LSTM-Attention. The histogram of oriented gradient (HOG) method was used for feature extraction, while LSTM networks captured temporal dependencies. The attention mechanism further enhanced model performance by focusing on relevant input features. The Raw-LSTM model processed raw mmWave radar images through LSTM layers and dense layers for classification. The Raw-LSTM-Attention model extended Raw-LSTM with an added self-attention mechanism within the traditional attention framework. The HOG-LSTM model included an additional preprocessing step upon the RAW-LSTM model where HOG features were extracted and classified using an SVM. The HOG-LSTM-Attention model built upon the HOG-LSTM model by incorporating a self-attention mechanism to enhance the model's ability to accurately classify activities. Evaluation metrics such as Sensitivity, Precision, Accuracy, and F1-Score were used to compare four architectural models. The results showed that the HOG-LSTM-Attention model achieved the highest performance, with an Accuracy of 95.3% and an F1-Score of 95.5%. Optimal self-attention configuration was found at a 2:64 ratio of number of attention heads to channels for keys and queries.  
full detection; millimeter-wave sensor; the elderly; long short-term memory (LSTM); attention +
Footnote †: journal: Applied sciences  
Article
# Detecting Cover Crop End-Of-Season Using VEN\(\upmu\)S and Sentinel-2 Satellite Imagery
## 3 Methods
### Algorithm Assessment  
Time-series NDVI from VEN\(\upmu\)S and Sentinel-2 were used to extract cover crop termination dates over BARC in 2019 and 2020. Results were compared to the termination or harvest dates from the BARC farm operation records. The NDVI time-series, senescence onset, termination, and dormancy dates were examined at both pixel (image mode) and field (text mode) levels. The WIST algorithm was first assessed for an alfalfa pixel in field #5-9 using VEN\(\upmu\)S and Sentinel-2. This field was harvested four times in 2019 and is an excellent test case for the WIST algorithm and the two image time-series at different temporal frequencies.  
Using imagery from VEN\(\upmu\)S and Sentinel-2, field-level NDVI time-series were generated for eight fields in 2019 and six fields in 2020 using field boundary polygons, as shown in Figure 3. Termination dates for each field were estimated for both years. The statistics, including mean bias, mean absolute difference (MAD), mean uncertainty, root mean squared error (RMSE), coefficient of determination (R\({}^{2}\)), missed detections (omissions), and false detections, were computed and compared at the field level using 29 harvest/termination records in 2019-2020.  
The WIST approach was evaluated in two near-real-time simulations using varying periods of time-series NDVI. The first simulation was geared toward an operational time-scale similar to the weekly reporting interval used at BARC and ran at the pixel level (image mode) using VEN\(\upmu\)S NDVI images. Maps of the termination dates were produced for 14 consecutive weeks starting from 1 May 2019. For each week, we saved the latest termination date. Fields with multiple terminations were shown in the corresponding weekly maps. The termination progress for each cover crop field in the map was examined and analyzed.  
The second near-real-time simulation ran the WIST algorithm daily at the field level to investigate the lag time of detection from VEN\(\upmu\)S and Sentinel-2. In the daily simulation, the first detected termination date may not be stable due to noise or incomplete senescent period. We selected the first simulation with stable detection (no change afterward for at least a week) as the detectable date. The simulation ran daily from early April (day 91) until the end of October (day 300) for 2019, and from early April (day 91) to the end of July (day 213) for 2020. The first detectable dates (ending date of the time-series), the detected termination dates, and the recorded harvest/termination dates were compared to find the lag time of the termination detection from the WIST algorithm from VEN\(\upmu\)S and Sentinel-2.
## 2 Materials and Methods
### Finite Element Method
#### 2.2.2 Comparison of Finite Element Method and Analytical Method  
In the finite element simulation, the material and size of the tuning fork are consistent with those of the laboratory quartz tuning fork in literature [19]. The length of the tuning fork arm L is 4.05 mm, the width e is 0.66 mm, the thickness h is 0.4 mm, and the spacing g is 0.24 mm. The resonant frequency and quality factor of tuning forks in 19 groups of fluids with different densities and viscosities were measured by experiments in reference [19]. In this paper, the finite element method and the analytical method were used to simulate the model with parameters identical to those in the experiments. Equation (2) and Equation (3) were used for the analytical method, the value of [PERSON]'s modulus E was 78.3 GPa, and the value of the material density \(\rho_{\text{b}}\) of the tuning fork was 2650 kg/m\({}^{3}\). The comparison of the numerical simulation results, analytical results, and experimental measurement results is shown in Table 1, where fluids of groups 1-19 are arranged in order of viscosity from small to large. The finite element simulation results were obtained through the conductance diagram, with peak value corresponding to the resonant frequency f of the tuning fork, quality factor \(Q=\text{f}/\Delta t\), and \(\Delta t\) is \(-3\) dB bandwidth. Figure 2 shows the results of group 1 and group 19. The resonant frequency of the tuning fork in fluid of group 1 was 29,089 Hz, and the quality factor was 58.53. In fluid of group 19, the resonant frequency of the tuning fork was 27,151 Hz, and the quality factor was 6.69.