esa-sceva/satcom-chunk-collection huggingface dataset:
# Novel Feature Extraction Method for Detecting Malicious MQTT Traffic Using Seq2 Seq
## 4 MQTT Attack Analysis
### SlowTte Attack  
SlowTte attacks [5] are a type of slow DoS attack. As shown in Figure 9, when a client sends a connect packet to a broker, the client sets the keep alive time, which indicates how long the broker should wait for a packet from the client. Typically, the client sends a publish packet when it receives an ACK packet from a broker. However, the client does not send a publish packet to the broker during a SlowTte attack. Therefore, during the keep alive time, the broker waits for the published packet. Thus, the broker performs poorly because of its many connections with the client during a SlowTte attack.
# Adaptation of Object Detection Algorithms for Road Indicator Lights in Complex Scenes
## 2 Materials and Methods
### Focaler-IOU Module  
In the process of edge regression, the problem of imbalance between training samples still exists. According to the difficulty, object detection can be divided into difficult samples and simple samples. From the perspective of object scale analysis, general detection objects can be regarded as simple samples, while very small objects can be regarded as difficult samples due to the difficulty of precise localization. For detection tasks that are dominated by simple samples, focusing on simple samples in the edge regression process helps to improve detection performance. In contrast, for detection tasks with a high proportion of difficult samples, it is necessary to focus on edge regression for difficult samples. Some traditional methods to solve the sample imbalance problem are to resample and reweight the difficult samples during the training process, but the effect of this method is not significant.  
In Focaler loss, an approach is proposed in which easily recognizable negative samples account for most of the total loss and dominate the gradient. Focaler Loss takes the approach of varying the weights of the positive and negative samples so that the model focuses more on the difficult-to-classify positive samples and thus reduces the weights of the negative samples that are relatively easy to classify, thereby improving the model's ability to recognize rare object classes.  
To focus on different regression samples and be able to focus on different detection tasks, a linear interval mapping approach is used to reconstruct the IOU loss, which is defined as follows:  
\[IOU^{focaler}=\left\{\begin{array}{l}0\,\mathrm{IOU}<\mathrm{d}\\ \frac{IOU-d}{u-d}\mathrm{d}\ll\mathrm{IOU}\ll u\\ 1\,\mathrm{IOU}>\mathrm{u}\end{array}\right. \tag{3}\]  
where \([d,u]\in[0,1]\), by adjusting the values of \(d\) and \(u\), the Focaler-IoU focus of attention can be made in different regression samples. Its loss is defined as follows:  
\[L_{Focaler-IOU}=1-IOU^{focaler} \tag{4}\]  
Applying Focaler-IOU to the CIOU loss function is calculated as shown below:  
\[L_{Focaler-CIOU}=L_{CIOU}+IOU-IOU^{focaler} \tag{5}\]  
The Focaler-IOU loss aims to enhance the model's focus on difficult samples (i.e., those predicted bounding boxes with low overlap with the true bounding box) while reducing the contribution of easy samples to the loss. It has good flexibility and generalization,  
Figure 2: Structure of AFPN module.  
and provides a significant improvement in the detection of tiny objects with partially occluded object detection problems, enhancing the performance of the model in a variety of complex scenarios.
## 2 Materials and Methods
### Determination of Amino Acid Content  
The _T. papillosa_ samples were procured from food markets in Sakon Nakhon Province, Thailand, and subsequently subjected to freeze-drying prior to analysis. To obtain the defatted sample, 5 g of the ground freeze-dried powder of _T. papillosa_ was dispersed in 50 mL of _n_-hexane and sonicated for 30 min. The residue was extracted three times with _n_-hexane. After removing the residual hexane, the defatted sample underwent hydrolysis by adding 5 mL of 6 mol/L HC1 and incubating it at 110 \({}^{\circ}\)C in a hot-air oven (FED 115, WTB Binder, Tuttlingen, Germany) for 15 h. The HC1 was then evaporated (R-210, BUCHI Labortechnik AG, Flawil, Switzerland) at 60 \({}^{\circ}\)C. The resulting dried hydrolysate was dissolved in a tenfold volume of water and centrifuged (Sigma 6-16K benchtop centrifuge, Sigma-Aldrich, Castle Hill, NSW, Australia) at 12,000 \(\times\)\(g\) for 10 min. After filtration using a 0.22 \(\upmu\)m nylon membrane, the supernatant was subjected to LC/MS/MS analysis (LCMS8030, Shimadzu, Kyoto, Japan) using a C18 column (150 mm \(\times\) 2.1 mm, 3 \(\upmu\)m; InertSustain, GL Sciences, Tokyo, Japan) for amino acid separation. The LC/MS/MS conditions were set following the method described by [PERSON] et al. [13]. The mobile phases used were formic acid 0.1% (\(v/v\)) in water (solvent A) and formic acid 0.1% (\(v/v\)) in water/methanol (50:50, \(v/v\)) (solvent B). A linear gradient elution was performed as follows: 2% B (0-1 min), 2-80% B (1-10 min), 80% B (10-12 min), and 80-2% B (12-15 min). The flow rate was 0.2 mL/min, and the injection volume was 2 \(\upmu\)L. The column oven and auto injection temperature were 38 \({}^{\circ}\)C and 15 \({}^{\circ}\)C, respectively. The triple quadrupole mass spectrometer was used with an electrospray ionization ion source in positive mode and multiple reaction monitoring. Other MS parameters were as follows: interface 4.5 kV, interface temperature 250 \({}^{\circ}\)C, desolvation line 300 \({}^{\circ}\)C, heat block 350 \({}^{\circ}\)C, detector 1.78 kV, nebulizing gas (N\({}_{2}\)) 3 L/min, drying gas (N\({}_{2}\)) 15 L/min, collision with argon gas at 230 kPa. The results were expressed as milligrams of amino acids per gram on a dry basis. The essential amino acid index (EAAI) was calculated using the method outlined by [PERSON] et al. [4]. Linear equations for the quantitative calculation of concentration are shown in Table S1.  
\[\text{EAAI}=\sqrt[]{\frac{\text{mg of lysine in 1 g of test protein}}{\text{mg of lysine in 1 g of reference protein}}\times\text{etc. for the other 8 essential amino acids}}\]
# Survey on Video-Based Biomechanics and Biometry Tools for Fracture and Injury Assessment in Sports
## 1 Introduction
### Software  
Different softwares are available for biomechanics research. This section describes briefly few softwares commonly used in this field, while a more detailed description can be found in Table 2. An open source tool, Biomechanical ToolKit, based in Python language for biomechanical analysis is presented in [77]. Conventional softwares for Biomechanical analysis include Vicon [78], Kinovea [79], Coach'e eye, Dartfish [4], Tracker [80], and Skill-Spector [81]. OpenSim allows its users to create and analyze dynamic simulations of movement [82]. A Matlab package for biomechanical analysis has also been described, such as the BoB (Biomechanics of Bodies) [83]. It is based on m-coding, so it is specific to the Matlab environment. FeBio allows to implement finite element analysis for biomechanics on rigid solids [84].  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Software** & **Ease of Access** & **Distinctive Features** \\ \hline Kinovea & Open source & Utilities to capture, slow down, compare, annotate and measure motion in videos. \\ \hline Dartfish & Monthly subscription & Available for PC and cell phone. Allows to annotate, compare videos, perform angle measurements. \\ \hline SkillSpector & Free software & Manual digitization of joints and transformation to real coordinates using DLT transform. Can handle 2D and 3D movement analysis. Advanced analysis of linear and angular kinematic data, inertia calculation, 3D movement animations, and video calibration. \\ \hline Coach’s Eye & Paid app, available on iOS, Android and Windows & Interactive and user-friendly GUI for sports assessment. Slowmotion and playback functions. \\ \hline Tracker & Open source, continuously updated in marker-less studies. & Automated tracking based on image recognition which can help in marker-less studies. Ample data analysis tools. \\ \hline Biomechanical Toolkit & Open source, Python-based & Several online repositories, ease of communication with data formats from other softwares used in biomechanics research. \\ \hline OpenSim & Open source & Development of models of musculoskeletal structures and generation of dynamic simulations. \\ \hline Vicon & Paid & High-tech software and algorithms. Multiple solutions for different needs of the research and athletes, both marker-less and with markers. \\ \hline Ariel Performance Analysis System & Free software & 3D motion analysis system. Automated biomechanical analysis from multiple, simultaneous video recordings. Can integrate information of EMG, force platforms and video. \\ \hline Visual3D & Free and paid options & Users can import motion and force data and perform computations, transformations, models and analysis with reports. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Softwares used for Video-based Biomechanics research and their distinctive features.
## 2 Methodology
### Model Performance Evaluation  
The multi-satellite dynamic emergency scheduling model based on mission priority not only requires a high mission completion rate and high total revenues, but also needs to ensure a better scheduling period for each mission and a high execution rate of missions with high priority. According to the characteristics of the model, three indicators are defined to evaluate the model performance, including the mission completion rate (_MCR_), mission priority execution rate (_MPER_), and scheme change rate (_SCR_). The evaluation function of the model is given in Equation (16):  
\[\begin{array}{c}f(u)=\frac{{\textit{MCR}}\times{\textit{MPER}}}{{\textit{ SCR}}}\\ MCR=n/n^{\prime}\\ MPER=\sum\limits_{i=1}^{n}{{p_{i}}/\sum\limits_{i=1}^{n^{\prime}}{{p_{i}}}}\\ SCR=num\big{(}T_{c}\big{)}/num\big{(}T_{q}\big{)}\end{array} \tag{16}\]  
where \(n\) is the total number of actual scheduled missions after scheduling plan adjustment, \(n^{\prime}\) is the total number of missions, \(num(T_{c})\) is the number of missions that the initial scheduling plan changes due to the newly arrived missions in dynamic scheduling, and \(num(T_{q})\) is the number of initial missions.
## 4 Simulation Results and Discussion
### Simulation for One-Hop Setup
#### 4.1.1 Parameter Design for Phase 1
Meanwhile, \(\delta_{1}\) directly governs the convergence speed in the way that a larger \(\delta_{1}\) gives a faster convergence. However, it is observed in Figure 5 that \(\delta_{1}\) beyond 0.4 suddenly increases the number of iterations from tens to hundreds, suggesting that \(\delta_{1}>0.4\) gives a too rapid position adjustment thus introduces a lot of oscillations in position updates. To give concurrent consideration to both the estimation error and the convergence speed, the optimal choice for phase 1 in this setup is \(\tau_{1}=0.001\) and \(\delta_{1}=0.4\).
## 4 Results and Analysis
### Identifying of Ports and Servers' Range  
After extensive monitoring of the encrypted traffic of the Signal app passing through the firewall, it was revealed that a common 443 TCP port is used throughout the communication process. In the case of blocking the 443 port on the firewall, it will also block other services on the smartphone that may be linked with the same port. Hence, blocking of the 443 port is not a suitable approach to analyze the other connectivity patterns of the Signal app. Therefore, the firewall rules are updated to evaluate new network patterns against different activities performed by the target user. Hence, network patterns against activities such as calling, texting, and typing are monitored and analyzed repeatedly to firmly reach the results.  
Unlike many other XMPP (eXtensible Messaging Presence Protocol)-based applications that utilized specific TCP ports such as 5222, 5223, and 5228, the Signal app uses port 443 for communication. For voice and video calls, random UDP open ports are employed by the Signal app. However, through an extensive performing of various Signal app activities, we found a common set of specific chat server IP addresses with respect to our geographic location as shown in Table 3 (detail in Section 5). Similarly, for call services, the Signal client app uses Google's servers in combination with the observed IP addresses of servers as shown in Table 3. This may have been performed for better connectivity or load balancing of traffic.  
\begin{table}
\begin{tabular}{c c c c c c} \hline No. & **Servers List 1** & **Servers List 2** & **Servers List 3** & **Servers List 4** & **Servers List 5** \\ \hline
1 & 52.207.41.59 & 34.225.196.214 & 34.196.69.69 & 100.24.0.111 & 35.169.3.40 \\ \hline
2 & 100.24.0.111 & 3.228.254.81 & 3.222.249.138 & 52.207.41
# Correlation Study of Auroral Currents with External Parameters During 10-12 October 2024 Superstorm
###### Abstract  
This study investigated the correlations between field-aligned currents (FACs), polar electrojets (PEJs), and external solar and geomagnetic activity parameters during the intense geomagnetic storm that occurred from 10 to 12 October 2024. Notably, the merging electric field (Em) had a greater impact on FACs and PEJs compared to the May 2024 storm, while the influence of solar wind pressure (Pd) was equally important in both storms. The peak FAC densities in the northern dawn (southern dusk) and nighttime sectors correlate strongly with Em, whereas Pd dominates in the northern dusk (southern dawn) and daytime sectors. For PEJs, Em correlates strongly with current densities in the northern dawn-dusk and southern nighttime sectors, while Pd is the primary correlated parameter on the dayside. FAC (PEJ) latitudes are most strongly influenced by Em (Pd or Dst) on the dawnside-duskside. Additionally, FACs and PEJs are mostly more intense on the dawnside than on the duskside and extend to lower latitudes at dusk than at dawn. Analysis of the May and October 2024 storms reveals that FACs in the summer hemisphere are generally stronger and situated at more poleward latitudes than those in the winter hemisphere. This pattern is largely driven by summer-winter variations in ionospheric conductivity, with some differences also arising from the asymmetric magnetic field geometry between the two hemispheres.  
field-aligned currents; polar electrojet; geomagnetic storm; merging electric field; solar wind dynamic pressure Article
# Satellite Attitude Determination and Map Projection Based on Robust Image Matching
###### Abstract  
Small satellites have limited payload and their attitudes are sometimes difficult to determine from the limited onboard sensors alone. Wrong attitudes lead to inaccurate map projections and measurements that require post-processing correction. In this study, we propose an automated and robust scheme that derives the satellite attitude from its observation images and known satellite position by matching land features from an observed image and from well-registered base-map images. The scheme combines computer vision algorithms (i.e., feature detection, and robust optimization) and geometrical constraints of the satellite observation. Applying the proposed method to UNIFORM-1 observations, which is a 50 kg class small satellite, satellite attitudes were determined with an accuracy of 0.02\({}^{\circ}\), comparable to that of star trackers, if the satellite position is accurately determined. Map-projected images can be generated based on the accurate attitudes. Errors in the satellite position can add systematic errors to derived attitudes. The proposed scheme focuses on determining satellite attitude with feature detection algorithms applying to raw satellite images, unlike image registration studies which register already map-projected images. By delivering accurate attitude determination and map projection, the proposed method can improve the image geometries of small satellites, and thus reveal fine-scale information about the Earth.  
satellite attitude determination; small satellites; map projection; robust estimation; feature detection 2017  
1  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]
## 3 Results
### The Effect of BMI
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{5}{c}{**BMI**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Smokers**} & \multicolumn{5}{c}{**Non-Smokers**} \\ \cline{2-7}  & **Normal** & **Overweight** & **Obese** & **Normal** & **Overweight** & **Obese** \\  & **(7)** & **(6)** & **(6)** & **(13)** & **(11)** & **(11)** \\ \hline TC mg/dL & 133.12 \(\pm\) 52.38 & 165.8 \(\pm\) 38.5 & 171.9 \(\pm\) 28.1 & 142.2 \(\pm\) 33.6 & 173.1 \(\pm\) 50.3 & 156.1 \(\pm\) 28.4 \\ \hline TG mg/dL & 183.5 \(\pm\) 177.9 & 204.8 \(\pm\) 73.1 & 297.7 \(\pm\) 209.5 & 163.9 \(\pm\) 108.7 & 319.9 \(\pm\) 329.2 & 238.5 \(\pm\) 133.6 \\ \hline HDL-C mg/dL & 37.32 \(\pm\) 10.3 & 41.9 \(\pm\) 10.7 & 43.5 \(\pm\) 11.3 & 41.1 \(\pm\) 7.93 & 45.67 \(\pm\) 21.9 & 48.27 \(\pm\) 16.3 \\ \hline LDL-C mg/dL & 48.75 \(\pm\) 13.87 & 75.0 \(\pm\) 26.68 & 55.0 \(\pm\) 21.79 & 68.67 \(\pm\) 27.62 & 81.43 \(\pm\) 30.41 & 70.4 \(\pm\) 32.16 \\ \hline \hline \end{tabular}  
* The ages of the smokers had no significant effects on the studied parameters, which increased with the increase in the smokers’ ages, with the exception of the TC/HDL-C ratio. With regard to the non-smoking group, the total blood cholesterol, the LDL-C, the TC/HDL-C ratio and RCA’s and LCA’s IMTs increased significantly as the ages increased, whereas there was an insignificant increase in the TG and an insignificant decrease in the HDL-C:\({}^{\rm a}\) (\(p\)-value = 0.005), \({}^{\rm b}\) (\(p\)-value = 0.005), \({}^{\rm c}\) (\(p\)-value = 0.001), \({}^{\rm d}\) (\(p\)-value = 0.002) and \({}^{\rm e}\) (\(p\)-value \(<\) 0.001).  
\end{table} 
Table 5: Effect of BMI on calculated parameters’ mean values for smokers and non-smokers. The smokers' BMIs increased TC, TG, HDL-cholesterol and the TC/HDL ratio insignificantly. The RCA and LCA IMTs increased significantly as the smokers' BMIs increased (Table 5). In summary, BMI was shown to have a regulatory effect on smokers' lipid profiles. Regarding the effect of smoking irrespective of the BMI effect, it significantly affected the LCA (\(p\)-value = 0.005) and the RCA (\(p\)-value = 0.04) (Table 2), while the BMI exerted significant effects on the LCA (\(p\)-value = 0.016) and the RCA (\(p\)-value = 0.05). The conclusion of this comparison is that the smoking effect on carotid artery thickness is higher than the effect of BMI.
# Multibeam Wideband Transmit Beamforming Using 2D Sparse FIR Trapezoidal Filters
## 3 Proposed Low-Complexity Multibeam Wideband Transmit Beamformer
### Analysis and Selection of Parameters  
Finding an optimal balance between the performance and the computational complexity of the sparse FIR filter is a challenge. When solving the optimization problem in the design of the filter, the parameters \(\mu\) and \(\epsilon_{th}\) were chosen such that the sparsity of the filter impulse response was significant without substantially degrading the filter performance. For a particular filter design, the value for the parameter \(\mu\) is varied in the range \([0.001,1]\), fixing the number of nonzero values in the impulse response, and the filter performance is observed in terms of the optimal value of step 2 of the optimization problem, which is the weighted maximum error between the desired response and the actual response, \(E_{opt}\). The value that gave the best performance out of the tested values is chosen for the design of the particular filter. After choosing a suitable value for \(\mu\), to choose a suitable threshold \(\epsilon_{th}\), the root-mean-square-error (RMSE) \(E\), and \(E_{opt}\), are considered. \(E\) is given by  
\[E=\sqrt{\sum_{i=1}^{M}\frac{(|H(e^{i\omega_{1}^{(i)}},e^{i\omega_{2}^{(i)}})|-| H_{d}(e^{i\omega_{1}^{(i)}},e^{i\omega_{2}^{(i)}})|)^{2}}{M}} \tag{28}\]  
When the number of nonzero coefficients is decreased by using a suitable \(\epsilon_{th}\), the \(E_{opt}\) increases rapidly after some point, as shown in Figure 5. Therefore, \(\epsilon_{th}\) is chosen such that it yields the required number of nonzero values without increasing the \(E_{opt}\) substantially. As a rule of thumb, \(\epsilon_{th}\) is chosen such that \(E_{opt}\) does not exceed more than 0.05 of its initial value and \(E<10\%\).  
Figure 5: \(E_{opt}\) variation with the number of nonzero values in the impulse response of the proposed 2-D FIR trapezoidal filter.  
As an example, Figure 5 shows the variation in \(E_{opt}\) vs. the nonzero values in the impulse response for the case of the single-beam filter design problem explained in Section 4.2. In that case, the chosen \(\epsilon_{th}\) is 0.007125, which corresponds to 143 nonzero values in the impulse response. The value \(E\) was 7.095%, and \(E_{opt}\) was 0.1364, which had an initial value of 0.08795. To have the same amount of side-lobes as the passband ripple, the passband and the stopband weights are taken as 1.
# Experimental Evaluation of the Packet Reception Performance of LoRa
## 5 Results and Analysis
### The Impact of BW on the Packet Reception Performance of LoRa  
Next, we will discuss the impact of BW on the packet reception performance of LoRa. The method is roughly the same as that employed in the previous experiment. In this experiment, we kept the SF unchanged and observed the effect of BW on the packet reception performance of LoRa. From the previous analysis, we know that the chirp rate only depends on the bandwidth, so increasing the BW can increase the effective data rate, but the sensitivity will also be affected, because the time-on-air is reduced and the probability of being correctly sampled is reduced. Figure 7 shows the SNR and PER for different BWs. It is not difficult to draw the conclusion that as BW increases, PER also increases.  
Moreover, SNR can be changed not only by means of lowering the interferer power, but also by changing the BW. It appears that each downscaling of BW by a factor of two (e.g., from 500 to 250 kHz) causes the SNR to increase by 3 dB. At the same time, we also found that if BW is reduced by two times, the low PER area (PER < 0.2) will be extended one unit to the right along with SF. For example, when BW = 500 kHz, the low PER area is only at SF = 12; when BW = 250 kHz, the low PER area is at SF = 11 and 12; and when BW = 125 kHz, the low PER area is at SF = 10, 11, and 12.  
Figure 7: The measurement results of the influence of different BWs on the packet reception performance of LoRa when keeping the SF unchanged. (**a**–**f**) shows the PER for different BWs.