esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Object Detection in High-Resolution Images Using OPAI  
Among the tracking algorithms used in vision applications like remote surveillance, the Simple Online and Realtime Tracking (SORT) algorithm stands out for its simplicity, real-time processing capability, and stable tracking performance [22]. SORT uses the Kalman filter to predict the new position and size of objects based on past tracking information, such as position, velocity, and size. It then uses the Intersection over Union (IoU) metric to compare these predictions with newly observed objects, matching them with the highest IoU value to continue the tracking process. Although SORT is advantageous for real-time applications due to its low computational load and straightforward implementation, its performance can degrade in complex environments due to frequent ID switching. This issue arises because SORT relies solely on information about the object's position, velocity, and size without considering the object's appearance.  
To address this limitation, the Deep Simple Online and Realtime Tracking (DeepSORT) algorithm incorporates a deep appearance descriptor derived from a CNN-based model [23]. This descriptor captures the visual features of objects, enhancing tracking performance by differentiating between objects based on their appearance. Consequently, DeepSORT provides more robust and accurate tracking, especially in complex environments. However, using deep learning inference to generate appearance descriptors for candidate objects in each frame can be computationally expensive.  
In this study, we modify DeepSORT to leverage appearance information without the computational overhead of deep learning inference. Instead of using a deep appearance descriptor, we employ the normalized correlation coefficient to measure the similarity of object shapes. This adjustment allows us to maintain the benefits of appearance-based  
Figure 8: Examples of OPAI. (**a**) OPAI example of particles; (**b**) OPAI example of bubbles.  
tracking while reducing the computational load, making the algorithm more suitable for real-time applications.
## 4 Simulations
### BER Performance of Suboptimal Receiver  
According to the analytical result in Section 3.1, the bit error performance of noncoherent PPM is related to the selection of the threshold coefficient \(\gamma\), and the optimal coefficient changes dynamically with the change of \(E_{b}/N_{0}\). First, we simulate and verify this. For comparison, 8-ary PPM is used in the simulation, and the threshold coefficients are 0.45, 0.55 and 0.75. The value range of \(E_{b}/N_{0}\) is 0-12 dB, and the bit error rate is calculated to \(10^{-4}\). The simulation results are shown in Figure 5.  
It can be seen in Figure 5 that the optimal threshold coefficient changes dynamically with the change in \(E_{b}/N_{0}\). When \(E_{b}/N_{0}\) is less than 6 dB, the bit error performance is better when the threshold coefficient is 0.75. However, when \(E_{b}/N_{0}\) is greater than 6 dB, the bit error performance is better when the threshold coefficient is 0.55. The above simulation verifies the theoretical derivation in Section 3.1. In order to facilitate the analysis, the threshold coefficient is uniformly taken as 0.55 in the rest of this paper.  
Next, the bit error performance of binary PPM, 4-ary PPM and 8-ary PPM is numerically simulated. The threshold coefficient is 0.55, the value range of \(E_{b}/N_{0}\) is 0-12 dB and the BER is counted as \(10^{-4}\). The simulation results are shown in Figure 6.  
In Figure 6, the BER performances of binary PPM, 4-ary PPM and 8-ary PPM sub-optimal receivers are evaluated, in which the theoretical curve is from the theoretical formula derived in Section 3.1. The simulation curve is the numerical simulation result. The simulation process first randomly generates the initial binary bit stream A, modulates it into a PPM signal, then sends it to the AWGN channel and then restores it to binary bit stream B through the suboptimal receiver in this paper. We compare the similarities and differences between B and A and calculate the BER to obtain the simulation BER curve.  
We can see in Figure 6 that the numerical simulation results are in good agreement with the theoretical formula deduced in Section 3.1, which verifies the correctness of the theoretical formula of the error code of the noncoherent PPM suboptimal receiver deduced in this paper. The theoretical formula is applicable to the \(Q\)-ary PPM system, including binary PPM. In addition, it can be seen from Figure 6 that the larger the \(Q\), the better the bit error performance of the noncoherent PPM suboptimal receiver. This is because the PPM signal essentially corresponds to an orthogonal signal and conforms to the basic characteristics of the orthogonal signal, which further verifies the correctness of the theoretical bit error formula deduced in this paper.
# Methodology for CubeSat Debris Collision Avoidance Based on Its Active ADCS System
## 4 Results and Discussion
### Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Orbits/Maneuvers** & **Semi-Major Axis (km)** & **VDD (m)** & **HDD (m)** \\ \hline SAT-H-AB & 6878.78 & 42.33 & 2046.32 \\ SAT-M-AB & 6838.78 & 85.36 & 4160.12 \\ SAT-L-AB & 6798.78 & 159.67 & 7845.10 \\ SAT-H\_BC & 6878.78 & 14.10 & 681.72 \\ SAT-M\_BC & 6838.78 & 28.42 & 1385.67 \\ SAT-L\_BC & 6798.78 & 53.13 & 2611.81 \\ SAT-H\_AC & 6878.78 & 56.43 & 2728.04 \\ SAT-M\_AC & 6838.78 & 113.79 & 5545.80 \\ SAT-L\_AC & 6798.78 & 212.80 & 10,456.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: HDD and VDD for satellite-maneuver combinations. Regarding the values obtained for the different combinations, we must come back to the consideration made in Section 3.2, where the cases analysis was imposed for maneuvers between states where the area-to-mass ratio is constant. Also, interchangeability between AB and BA maneuvers (i.e.) was considered because they would have the same effect but in opposite directions. The results obtained, and reflected in Figure 11, confirm that this hypothesis is right, because for all the cases of study the VDD is very small compared with the orbital radius (four orders of magnitude smaller), so the effect in relative change in air density and vehicle velocity can be neglected for the computation of drag acceleration for each combination, according to Equation (1). So, we can consider that the drag acceleration would only depend on the area-to-mass ratio, at the time of analysis.
## 2 System Characteristics
### Underwater Turbulence  
In the underwater channel, in addition to light absorption and light scattering, the turbulence induced by the temperature gradients, salinity gradients, air bubbles, and ocean mixing processes will result in scintillation and phase change of the received optical signal, leading to an increase in the bit error rate (BER). Since the intensity-modulated and direct detection (IM/DD) communication system was used in this experiment, we only considered the optical scintillation effect caused by the underwater channel. In terms of the effect of scintillation, the intensity of turbulence is usually quantified by the scintillation index (S.I.) or the probability density distribution (PDF) of received optical power [34]. A large number of underwater turbulence experiments have proved that the log-normal probability density function \(f(P)\) can well represent the PDF of received optical power from weak to strong turbulence under the aperture averaging effect [34; 35; 36].  
\[f(P)=\frac{1}{P\sigma\sqrt{2\pi}}\exp\left(-\frac{\left(\ln(P_{r}/P_{0})+ \sigma^{2}/2\right)^{2}}{2\sigma^{2}}\right) \tag{8}\]  
where \(P_{0}\) is the mean received optical power, and \(\sigma^{2}\) is the S.I. defined by:  
\[\sigma^{2}=\frac{\left\langle P_{r}^{2}\right\rangle-\left\langle P_{r}^{2} \right\rangle^{2}}{\left\langle P_{r}^{2}\right\rangle^{2}} \tag{9}\]  
Here, \(<>\) is the mean operator.
# Ten Years of TerraSAR-X Operations
## Extension of the TerraSAR-X Ground Segment
### Common TerraSAR-X/TanDEM-X Ground Segment
Their association to a given mission is configurable and is mostly a question of downlink capacity needs and requested data transfer times between a given station and the TerraSAR-X processing hosted at DLR. GARS and ISSF are supporting TanDEM-X, GARS had been actually used for the downlink of TerraSAR-X background mission data before TDX launch. Since the beginning of TSX mission operations the density of the S-Band motoring network was significantly increased. At the beginning, a 12-h rhythm was applied. In later stages the monitoring of the satellites was intensified. The S-Band network benefits from the expansion of the X-Band network by GARS and ISSF. The KSAT ground station SvalSat, Svalbard, supports two passes for TSX and TDX monitoring at midday. The current ground segment layout is depicted in Figure 1.
## 3 Methods
### Model Parameterization and Validation  
The Shuffled Complex Evolution Procedure Developed at the University of Arizona (SCE-UA) is a global optimization algorithm which aims to find the optimal values of parameters in a particular range which makes sure that the cost function is minimized [76]. The expression of the cost function is as follows:  
\[d=1-\frac{RMSE^{2}}{\sum_{i=1}^{n}(x_{i}-\overline{x})^{2}+\sum_{i=1}^{n}(y_{ i}-\overline{x})^{2}+2\times\left|\frac{\sum_{i=1}^{n}(x_{i}-\overline{x})(y_{i}- \overline{y})}{\sqrt{\sum_{i=1}^{n}(x_{i}-\overline{x})^{2}}\sum_{i=1}^{n}(y_ {i}-\overline{y})^{2}}}\times\sqrt{\sum_{i=1}^{n}(x_{i}-\overline{x})^{2}} \times\sqrt{\sum_{i=1}^{n}(y_{i}-\overline{x})^{2}}\right|} \tag{26}\]where the \(RMSE\) is the root-mean-square error; \(n\) is the total number of data used for model parameters' optimization; \(x_{i}\) and \(y_{i}\) represent in situ GPP and estimated GPP from the model, respectively; \(\overline{x}\) and \(\overline{y}\) are the mean values of in situ GPP and estimated GPP. In this study, \(p\), \(q\), and \(a\) in Equations (2), (15), and (16) were optimized using the SCE-UA algorithm. To ensure the robustness of the model, we used a 10-fold cross-validation method to optimize the parameters and validate the model. First, we randomly divided the in situ GPP of each vegetation type into 10 groups, using 9 groups of data to train and optimize the parameters and one group of data for validation, ensuring that all data were involved in training and validating the model. Then, we trained 10 models and obtained 10 sets of parameters, and the average of the these parameters was used for the generation of global GPP products. Finally, the standard deviation of the global GPP generated by the ten sets of parameters was used as the uncertainty of the product.  
The coefficient of determination (R\({}^{2}\)), the RMSE, and the mean absolute error (MAE) were used to evaluate the performance of the GPP estimation. Furthermore, the index of agreement (IOA), which measures the degree of agreement between the estimates and observations, was selected [77]. The logical range of IOA is 0 to 1, where 1 indicates that the model estimates are consistent with the observations, and 0 represents complete disagreement.
# Concrete Crack Width Measurement Using a Laser Beam and Image Processing Algorithms
## 2 Materials and Methods
### Determining the Pixel to Millimeter Conversion Factor Using the Laser Beam  
The laser beam is a key factor in the ability of the proposed method to accurately measure concrete crack width in millimeters. A circular laser beam was chosen because it is easier to detect and measure its diameter more accurately using image processing algorithms. Prior to crack width measurement, the relationship between the laser diameter and the distance to the measuring plane was first established. Knowledge of this relationship is essential because the diameter of the laser beam projected onto the measuring plane changes with distance. Understanding the behavior of this change in diameter allowed the calculation of a conversion factor, \(\alpha_{c}\). The conversion factor converts concrete crack widths from pixels to millimeters. An indoor experiment was conducted to establish this relationship. A camera with an attached laser was placed at varying distances from the measured plane, ranging from 50 mm to 1800 mm. The distance and diameter of the laser beam were recorded for each instance and plotted, as shown in Figure 2. The relationship between the diameter of the laser beam and the distance to the measured plane was found to be directly proportional. The relationship is defined by Equation (4).  
\[\varnothing_{real}=0.055D+2.644\ (\mathrm{mm}) \tag{4}\]  
where \(\varnothing_{real}\) is the actual diameter of the laser in millimeters, and \(D\) is the distance to the measuring plane in millimeters.  
Figure 1: Flowchart showing the crack width measurement process.  
Figure 2: Relationship between distance to measuring plane and laser beam diameter.  
Using this relationship, in conjunction with the diameter of the laser beam measured in pixels, the conversion factor, \(\alpha_{c}\), is defined by Equation (5).  
\[\alpha_{c}=\frac{\varnothing_{real}}{\varnothing_{px}}\ (\mathrm{mm}/\mathrm{px}) \tag{5}\]  
where \(\alpha_{c}\) is the conversion factor, \(\varnothing_{real}\) is the actual diameter of the laser in millimeters and \(\varnothing_{px}\) is the diameter of the laser in the image measured in pixels.
# Detection Model for 5G Core PFCP DDoS Attacks Based on Sin-Cos-bIAVOa
###### Abstract  
The development of 5G environments has several advantages, including accelerated data transfer speeds, reduced latency, and improved energy efficiency. Nevertheless, it also increases the risk of severe cybersecurity issues, including a complex and enlarged attack surface, privacy concerns, and security threats to 5G core network functions. A 5G core network DDoS attack detection model is been proposed which utilizes a binary improved non-Bald Eagle optimization algorithm (Sin-Cos-bIAVOA) originally designed for IoT DDoS detection to select effective features for DDoS attacks. This approach employs a novel composite transfer function (Sin-Cos) to enhance exploration. The proposed method's performance is compared with classical algorithms on the 5G Core PFCP DDoS attacks dataset. After rigorous testing across a spectrum of attack scenarios, the proposed detection model exhibits superior performance compared to traditional DDoS detection algorithms. This is a significant finding, as it suggests that the model achieves a higher degree of detection accuracy, meaning it is better equipped to identify and mitigate DDoS attacks. This is particularly noteworthy in the context of 5G core networks, as it offers a novel solution to the problem of DDoS attack detection for this critical infrastructure.  
5G; PFCP; cybersecurity; intrusion detection; DDoS
# A Novel Optimization Framework to Replicate the Vibro-Acoustics Response of an Aircraft Fuselage
## 7 Conclusions  
A novel optimization framework, based on a MDO procedure applied to the vibro-acoustic FEM model of a fuselage mock-up, has been presented in this work.  
The MDO procedure, based on an EGO-like approach, has been adopted to characterize acoustic sources that replicate the sound pressure field generated by the engines on the fuselage. A predetermined sound pressure field was considered as a reference data, whereas two tentative sound fields were calculated by leveraging on the proposed FEM-MDO framework.  
The comparisons between the reference and the calculated data in terms of pressure fields, quality indexes and metric values, have been judged as acceptable even if further refinements, considering different setup of monopole sources and microphone locations together with a different metrics, are currently under development.  
The proposed FEM-MDO procedure can be considered as a potential tool to set up ground experimental tests on aircraft components to replicate their vibro-acoustic performances as if tested in flight.  
Conceptualization, R.C., L.F. and M.B.; methodology, R.L. and M.M.P.; software, R.L. and M.M.P.; validation, V.G. and M.M.P.; formal analysis, V.G. and M.M.P.; writing--original draft preparation, V.G.; writing--review and editing, V.G., R.L., R.C. and L.F.; supervision, R.C., M.B. and L.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Storage of Solid Sidestreams  
Storage trials of BSG and SCG were carried out in duplicate High-Density Polyethylene (HDPE) trays or drums to simulate large-scale storage conditions, with trays intended to facilitate the establishment of aerobic conditions and drums intended to facilitate the establishment of anaerobic conditions. Drums were outfitted with three sampling ports, top, middle, and bottom, by cutting a square hole and outfitting a hinge and bolt-lock. Three small holes were drilled near the sampling port to fit temperature and relative humidity (RH) sensors from a GSP-6, model datalogger (Elitech, San Jose, CA, USA) placed at the center of the drum. The top sample port and datalogger sensor were above the level of the solid sidestream, and therefore, top samples could not be collected, and the sensors were placed on top instead of inside of the solid sidestreams. Three water locks were installed on the lid to maintain anaerobic conditions within the drum. Trays were outfitted with three dataloggers to measure temperature and humidity at the front, middle, and back of the tray, inserted into the solid sidestream. Trays were covered with fine-masked nets secured by magnets to deter pests. Containers were cleaned with 2.5 wt% bleach solution, washed with distilled water, and air-dried prior to loading 40 kg or 100 kg of freshly obtained solid sidestreams from Tiong Lam Supplies. For consistent comparison between aerobic and anaerobic treatments, BSG-2 was used for both tray and drum storage trials, while SCG-2 was used for tray storage and SCG-3 was selected for drum storage. Samples of 100 g wet weight were collected in clean ziplock bags on days 0, 3, 7, and 14 using the sample ports for the drums and digging a sample for the trays. The ambient conditions at Tiong Lam Supplies were measured every morning with a separate thermometer and humidity meter placed in the trial area. The experimental setup is summarized in Table 2, and photographs can be found in the Supplementary Materials Figure S1.
# T-Type Vertical Wall for Decoupling and Pattern Correction of Patch Antenna
## 3 Result Analyses
### Comparison with Other Works  
Our work aims to align the main beam pattern at the concerned frequency to the broadside direction after decoupling. The simulated and measured results have validated this design. Thus, we compare our work with other decoupling methods where the edge-to-edge distance is approximately (a little less or more than) 0.05 \(\lambda_{0}\). The corresponding comparisons are listed in Table 2. The works in References [9,20] presented in Section 1 are not shown here. Due to Reference [9], the author only showed one pattern that was not tilted, while the asymmetrical feeding structure in Reference [20] makes the pattern essentially tilt.  
The works in References [3,16] adopted the high-profile techniques, while the remainder used the planar decoupling structure. However, all the maximum radiation directions deviate from the broadside direction to the fixed angles, regardless of the patch arrangement along the E- or H-plane direction. This deviation means that the broadside radiation will be reduced when they form the array. Therefore, this common problem prompts us to investigate the pattern-corrected decoupling technique so that the proposed method has corrected the pattern to the broadside direction for the concerned frequency, where the simulated frequency of 5.80 GHz is shown.
## 2 Cytotoxicityicity
### In Vitro Assays
#### 2.1.7 Viscosity  
The chemical nature of DESs affects the viscosity, water content, and melting point of synthesized DESs. [74]. [PERSON] et al. (2015) evaluated the viscosity (in Pa-s, at 30 \({}^{\circ}\)C) of 20 different DESs formed using ChCl as an HBA and urea, acetamide, ethylene glycol, glycerol, 1,4-butenediol, triethylene glycol, xylitol, p-toluenesulfonic acid, oxalic acid, sorbitol, malic acid, citric acid, levulinic acid, malonic acid, tartaric acid, xylose/water, sucrose/water, fructose/water, glucose/water, and maltose/water as HBDs. It was observed that the viscosity was higher in ChCl/tartaric acid (2:1), ChCl/citric acid (1:1), ChCl/malic acid (1:1), and ChCl/D-sorbitol (1:1), with values > 10 Pa-s. These compounds are more viscous because the presence of extra hydroxyl groups creates more hydrogen bonds and increases the attractive forces between molecules (H-bonding network, van der Waals forces, and electrostatic interplays) [43; 70]. [PERSON] et al. showed that the viscosity of DESs is affected by their water content, and if more than 50% water is present, the hydrogen bond framework of the DES components is destroyed [75].  
It could be considered that high viscosity produces an increase in toxicity. [PERSON] et al. indicated that high viscosity was related to higher lethality rates (lowest IC\({}_{50}\) values in MTT test). The DES ChCl/sucrose/water (4:1:4) and the ChCl/malonic acid (1:1) had the highest viscosity values (853.3 mPa-s and 616.0 mPa-s, respectively) and the lowest IC\({}_{50}\) in HelaS3 (166.58 \(\pm\) 5.8 and 20 \(\pm\) 8.4, respectively), CaOV3, B16F10, and MCF-7 cell lines. They concluded that the introduction of substances with high viscosity (above 500 mPa-s) in a cell medium can result in a major variation in cytoplasmic viscosity (homeostatic range from 1 to 400 mPa-s) [76], and could cause cell death [59].  
[PERSON] et al. (2017) studied the cytotoxicity of two NADESs composed of ChCl and glucose or fructose, and N,N-diethylethanol ammonium chloride-triethylene glycol on the HelaS3, A375, AGs, WRL-68, MCF-7, and PC3 cancer cell lines. In this case, they found that both ChCl:glucose and ChCl:fructose were less toxic compared to DESs. However, ChCl:glucose and ChCl:fructose NADESs showed higher toxicity in the in vivo tests [64], since viscosity seemed to affect more complex organisms differently.
# DAB Signal Preprocessing for Passive Coherent Location
## 4 Discussion
### Computational Complexity Analysis  
The computational costs of the described solution result mainly from calculating the moving averages (2) and (3) of the complex signal's magnitude. In practical implementation,  
Figure 22: Improvement of SNR achieved by MSC symbols duplication.  
these computations may be simplified by calculating and comparing squared magnitudes without square root calculations  
\[p(n)=\frac{1}{N}\sum_{k=0}^{N-1}s(n-k), \tag{10}\]  
\[p_{\text{avg}}(n)=\frac{1}{M}\sum_{k=0}^{M-1}s(n-k) \tag{11}\]  
where \(s(n)\) is a squared magnitude of the signal's complex sample  
\[s(n)=x_{\text{real}}(n)^{2}+x_{\text{imag}}(n)^{2}. \tag{12}\]  
The moving average filters (10) and (11) can be implemented recursively by adding the current value of (12) to the previously calculated \(p(n-1)\), \(p_{\text{avg}}(n-1)\), and subtracting its historical value (from the beginning of the time window), fetched from a circular buffer. Moreover, the sum of squares (12) can be calculated only once for each signal sample, then used in both moving averages and stored in the buffer (with the length of \(M\)). An additional real multiplication may be performed for each sample to perform thresholding by comparing (10) with the scaled value of (11). After the proposed optimization, the calculation of (10) and (11), and thresholding for each sample would require only three real multiplications, five additions/subtractions, and two divisions, as summarized in Table 2. Furthermore, the divisions may be cheaply realized by bit shifting, provided that \(N\) and \(M\) are the powers of two. It should be kept in mind that the results of computations would be valid after filling up the circular buffer (i.e., after processing \(M\) input samples) and should be ignored prior to that point.