esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Description of the Driving Process Based on HSMM
#### 3.1.2 Parameter Sampling and Inference  
In Bayesian nonparametric models, the Gibbs sampling algorithm is widely used for inference of model parameters. After the model structure is determined, the weak limited Gibbs sampler (WLGS) is utilized to sample and infer the model parameters. The weak limit approximation transforms the infinite dimension hidden state into finite dimension form, so that the hidden state chain can be updated according to the observation data. For the convenience of description, when \"\(\backslash\)\" appears in the superscript or subscript of a variable, it means that the corresponding variable is removed from the collection. In order to simplify the derivation and facilitate the integral solution, it is assumed that the basic distribution \(H\left(\cdot\right)\) and the observation series distribution \(F\left(\cdot\right)\) are conjugated distributions, the hidden states distribution \(g\left(\cdot\right)\) is a Poisson distribution, and the hidden states distribution and the observation series distribution are independent. The sampling process is as follows.  
Step 1, sampling weight coefficient \(\beta\),  
\[\beta|\gamma\sim\text{Dir}(\gamma/S,\cdots,\gamma/S) \tag{29}\]  
Step 2, sampling the state sequence distribution parameter \(\pi_{i}\),  
\[\pi_{i}|\alpha,\beta\sim\text{Dir}(\alpha\beta_{1},\cdots,\alpha\beta_{s})\;j =1,\cdots S \tag{30}\]  
Figure 8: The HDP–HSMM graphical model.  
Step 3, sampling the observation distribution parameters \(\theta_{i}\) and state duration distribution parameter \(\omega_{i}\) according to observation data. It is assumed that the observed data obey a multivariate Gaussian distribution, thereby, the model parameters \(\theta_{i}=(u_{i},\Sigma_{i})\) obey the Normal-Inverse-Wishart distribution (NIW)  
\[\text{NIW}(u,\Sigma\big{|}v_{0},\Delta_{0},\mu_{0},S_{0})\triangleq\text{N}( \mu\big{|}\mu_{0},S_{0})\ast\text{IW}(\Sigma|v_{0},\Delta_{0}) \tag{31}\]  
where \(\varphi=\{u_{0},S_{0},v_{0},\Delta_{0}\}\) are prior parameters, \(u_{0}\) and \(S_{0}\) are the prior mean and covariance matrix, respectively, and \(v_{0}\) and \(\Delta_{0}\) are the degrees of freedom and scale of NIW distribution, respectively.  
In addition, the state duration distribution is a Poisson distribution, and parameter \(\omega_{i}\) follows a Beta distribution.  
\[\omega\sim\text{Beta}(\eta_{0},\sigma_{0}) \tag{32}\]  
Step 4, updating parameters according to the observation data. (Please refer to [38; 39] for further detailed update processes.)
## 5 Discussion
Under the diversity combining approach, we assume that the response bias observed for inward stimuli is sensory in nature. However, we recorded and evaluated behavioral responses, which are the output of the overall sound lateralization system. It includes both of its sensory and cognitive sub-systems, which are responsible respectively for auditory processing of the acoustic stimuli and for attention and decision making for the type of motor response (i.e., key-pressing). Therefore, by using only the final behavioral response, one may not know if that bias should be linked to the former or latter sub-systems or both. To delineate possible contributions of these sub-systems to the observed bias, it is necessary to have the neural responses of the sensory mechanisms of the brain detecting the directional difference between the inward and outward sound image shifts. The electroencephalographic (EEG) responses called MMN (mismatch negativity), which are known to be the electrical responses of the pre-attentive sensory change-detection mechanism [54; 55] can be used for this purpose. Another EEG response called P300, which is known to be the electrical response of the cognitive system related to the processes of selective attention and decision [52; 56], may serve, on the other hand, as a measure for the involvement of attentional cognitive mechanisms in the observed bias. This may be a subject for future research based on the model suggested in the present study.
# Efficient Layered Parallel Architecture and Application for Large Matrix LDPC Decoder
## 4 Results  
### Implementation Resource Utilization  
The 50G-PON standard LDPC decoder designed in this paper is implemented using the VC707 development board of the Virtex-7 series, with the specific FPGA model number xc7 vx485t. The resource utilization of the designed LDPC decoder based on the synthesis and implementation of this development platform is shown in Table 2.  
Figure 5: BM mechanism.  
Table 2 gives the resource consumption of the designed decoder. Based on the 50G-PON standard, this design achieves a similar throughput to that of the paper in [20], with about 0.27 times the LUT and about 0.11 times the FF. Compared with the paper in [19], this design achieves about 3.86 times the throughput, with about 4.43 times the LUT and about 5.73 times the FF. On the one hand, this is due to the large code length of the 50G-PON, so the FPGA frequency is limited, and on the other hand, the throughput is lower because the maximum number of iterations is twice that in [19].  
Table 3 gives the synthesis results of the implemented LDPC decoder. It can be seen that while also based on the TSMC 65 nm process, the designed decoder has more than twice the throughput area rate(TAR) of [21] and more than three times the TAR of [19], proving that the designed circuit structure can effectively improve the area efficiency of the optical communication decoder circuit.
## 1 Introduction
These optimal beamforming parameters, which include the transducers' location and the average speed of sound (SOS), are deduced by maximizing the coherence of the received RF data resulting from different targeted scatterers in the medium by cross-correlation. One key feature of CoMTUS is the resulting discontinuous aperture. Preliminary studies showed that an improved spatial resolution comes with a loss in image contrast [11; 12]. Nevertheless, how much the aperture's discontinuities, dictated by the spatial separation of the multiple transducers, affect the global performance of the method is still unclear. Moreover, so far, CoMTUS has been validated only in constant SOS media, which is known not to be representative of human tissues. Therefore, to prepare for future in vivo applications, the purpose of this study is to further investigate the performance of CoMTUS to better assess its potential benefits and describe its implications for imaging. First, the impact of the effective aperture size, the separation between the arrays, the imaging depth and the presence of acoustic clutter will be assessed in a parametric study. Then, the behaviour of CoMTUS, particularly of the optimized beamforming stage, will be shown when assuming different and spatially varying speeds of sound, i.e., by assuming path dependent SOS due to variable thickness layers of tissue with different acoustic properties. The paper is organized as follows: Section 2 briefly recalls the theory of CoMTUS, then Section 3 presents the Materials and Methods, with simulations described in Section 3.1 and the corresponding experimental methods in Section 3.2. To establish a description of the CoMTUS discontinuous aperture, different effective aperture sizes and the corresponding images are simulated. The method is then evaluated at large imaging depths. Then, the CoMTUS method, initially validated in homogeneous media, is extended to a model of layered tissue with different speeds of sound. Results are presented in Section 4 and discussed in Section 5. Finally, the study is concluded in Section 6.
# Countering Social Media Cybercrime Using Deep Learning: Instagram Fake Accounts Detection
## 3 Related Works
### Discussion  
The existing literature on fake account detection has investigated various methods, including the use of ML algorithms such as RF Classifier, SVM, NB, PCA, and LR. Table 1 summarizes existing fake accounts detection studies. Specifically, some research has used ML approaches such as hybrid ML, J48 Decision Tree, and ANN to analyze Instagram data. Based on the different studies conducted in this field, ML has a significant role in detecting fake accounts. However, one major disadvantage of ML algorithms is the fact that they rely on manual feature engineering and selection, which requires domain expertise and can be time-consuming. This constraint is particularly challenging when dealing with high-dimensional numerical data, considering that determining significant features is a diligent task. In contrast, other research has used deep learning techniques such as LSTM and dynamic CNN to navigate these challenges. DL distinguishes itself from typical ML by automatically extracting and learning the most relevant features from raw data using a layered architecture. This included feature extraction capacity provides a substantial advantage for handling complicated high-dimensional datasets without requiring heavy user intervention. Furthermore, deep learning, particularly LSTM, is well suited to addressing the challenges presented by sequential data, such as text. It performs exceptionally well in tasks that involve processing data with inherent temporal or sequential dependencies, as in the case of email spam detection. For example, studies in [43; 44] have effectively used LSTM to analyze large datasets for spam email detection, where the sequential nature of the data is a key factor in achieving accurate results. Although LSTMs are traditionally applied to sequential data, this research explores their adaptability to non-sequential datasets. LSTM's ability to capture complex relationships between features, even in non-sequential contexts, makes it a candidate worth investigating. This study aims to determine whether LSTM can outperform traditional architectures, such as CNNs and classical ML algorithms, in detecting fake Instagram accounts. To achieve this, multiple architectures will be tested and compared to assess LSTM's effectiveness in this novel application. Furthermore, the inclusion of dropout layers addresses the risk of overfitting, ensuring the model generalizes well to unseen data. While most research has focused on platforms like X and Facebook, Instagram's unique features warrant further exploration to assess the applicability of LSTM in this context. This investigation addresses a gap in the current literature and provides an opportunity to evaluate LSTM's potential for detecting fake accounts on Instagram compared to other architectures.
## 3 Results and Discussion
### Resonant Frequency  
The first step of measurements was the finding of the natural frequency of the actuator. For this purpose, the SJ velocity as a function of frequency was presented in Figure 4. The natural frequency in cases 0, 3, and 4 was \(f_{n}=36\) Hz, and in cases 1 and 2 was \(f_{n}=38\) Hz. The frequencies were the same as in [29] for cases 0 and 1a.  
Figure 4: SJ velocity vs. the actuator frequency at power 3 W.  
The change of the resonant frequency in the case of the different numbers of chevrons is important in the case of natural frequency modeling. The different theoretical models take into account different parameters of actuators. The model proposed by [PERSON] et al. [42] does not include the influence of the orifice on the natural frequency. [PERSON] and [PERSON] [40] and [PERSON] [43] propose the model included the length and diameter of the orifice. The differences in these models are described in more detail in [44].  
According to the model of [PERSON] and [PERSON] [43] the natural frequency is inversely proportional to the mass of the air in the orifice. This is especially noticeable when the natural frequency for the cases 0, 2, 5, and 6 will be compared--they are equal consecutively \(f_{n}=36;38;40;42\) Hz. The increase of the number (case 2) or the height (cases 5 and 6) of the chevrons affects the fluid mass in the actuator orifice and causes the increase of the natural frequency value. It is consistent with the model proposed by [PERSON] and [PERSON] [43]. In case 1 the number of the chevron is too small. The change of the orifice volume is small compared to case 0 and a frequency change was not detected during the measurements (frequency step was equal to 2 Hz). The number of chevrons is equal to 10 in case 3 and 14 in case 4. In these cases, the height of cutouts is small compared to other cases and despite a large number of chevrons the natural frequency change was also not detected. In other words, the change (compared to case 0) of the orifice volume in cases 1, 3, 4 was too small to notice, for the adopted research methodology.  
The impact of the orifice dimensions of the Helmholtz resonant frequency was described in [45]. [PERSON] et al. [46] included in their model the mass of air in the orifice. [PERSON] et al. [45] also include the mass of the air in the orifice but presented them as a function of length and diameter of the orifice.  
[PERSON] and [PERSON] [43] and [PERSON] et al. [45] proposed the most extensive models taking into account many of SJA parameters. One of them is the mass of the air in the orifice. Both of the models were designated for the circular orifice. This paper showed that the chevron orifice has an impact on the natural frequency. In my opinion, it is caused by the change of the mass of the air oscillated in the orifice. Therefore, a way has to be found to determine this mass. This may be the subject of further research.
## 4 Results
### PPP Time Transfer in the Kinematic Model Using HAS Service
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time-Links** & **GPS** & **Galileo** & **Galileo/GPS** \\ \hline BRUX-PTBB & 0.37 & 0.33 & 0.23 \\ NIST-PTBB & 1.26 & 1.05 & 0.58 \\ OP71-PTBB & 0.35 & 0.35 & 0.19 \\ TWTF-PTBB & 1.09 & 1.57 & 0.76 \\ USN7-PTBB & 1.55 & 1.08 & 0.58 \\ USN8-PTBB & 1.46 & 1.10 & 0.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The STD values of GPS, Galileo, or Galileo/GPS PPP time transfer in the kinematic model. 4.06 \(\times\) 10\({}^{-14}\), respectively. In comparison with the static PPP time transfer, the dynamic time transfer exhibits considerably poorer frequency stability. Consequently, there remain a multitude of challenges to be addressed and investigated in the pursuit of high-precision timing in dynamic environments.
# Reputation and Trust Approach for Security and Safety Assurance in Intersection Management System
## 3 Trust and Reputation Approach Description
### Truth, Trust and Reputation Models
#### 3.1.2 Reputation  
Calculation of \(R\) value can be conducted as:  
\[R_{e_{i_{t}}}^{S}=\begin{cases}R_{e_{i_{0}}}+\sum\limits_{i=1}^{t}Truth_{i}, Truth_{t}\geq\alpha\\ R_{e_{i_{0}}}+\sum\limits_{i=1}^{t}Truth_{i}-(R_{t-1}-e^{-(1-Truth_{i})t}),Truth _{t}<\alpha\end{cases} \tag{8}\]  
where \(Truth_{e_{i_{t}}}\) is the truth value, received from agent \(e_{i}\) by agent \(e\) at the current time moment \(t\), \(R_{j}\) is the reputation value of agent-object in time moment \(j\), and \(\alpha\) is the threshold for a positive or negative decision on agent's reputation value.  
In this case, the reputation value does not satisfy previously introduced Assumption 2. On this basis, we consider \(R_{e_{i_{t}}}^{S}\) as an intermediate step in calculating the reputation value. To calculate \(R\) value, we need to normalize value \(R_{e_{i_{t}}}^{S}\) over the time period. In this case, the reputation value corresponds Assumption 2.  
At the initial time of the group operation, the reputation value can be taken equal to 0.5, i.e., with \(t=0\), \(R_{e_{i_{t}}}^{S}=0.5\). The value of \(\alpha\), at which the \(Truth\) level is correct, is chosen empirically. In general, \(\alpha=0.5\).  
Evaluation \(R\) of the one agent based on transferred data and other agents' opinions can be calculated according to Equation (9).  
\[R_{e_{i_{t}}}=\frac{\sum_{t=1}^{|E|-1}R_{e_{i_{t}}}}{|E|}, \tag{9}\]  
where \(e_{j}\in E,e_{j}\
eq e_{i}\). In this case, the reputation is calculated not only on the basis of agent's own assessment of the data received from agent-object, but also on the basis of other agents' valuations. This approach allows clarifying the value of the indicator for the agent-object based on the calculated values of the reputation indicator from other agents, presumably having the ability to directly interact with the agent-object.  
The introduced calculus results reveal \(R\) growth slower than a possible decrease. It means that the saboteur is unable to immediately increase its reputation level.
# 5G V2X Performance Comparison for Different Channel Coding Schemes and Propagation Models
## 2 Proposed V2X Communication System Model  
### 3 GPP 4G-LTE and 5G-NR Traffic Scenarios and Channel States for V2X Communication Models  
A V2X communication system model simulates the data transmission between two moving entities. Two main traffic scenarios are considered both for 4G-LTE [7] and 5G [8]. In the first scenario, called urban, the vehicles are moving in an urban area that may have many obstacles and scatterers. In the second scenario, called highway, the vehicles are moving in a freeway-highway area, where the obstacles and scatterers affect the propagation path less. According to 3 GPP [7; 8], a V2X communication channel link between two vehicles can have three different states:  
* LOS: A V2V link is in LOS state if the two vehicles are in the same street and the propagation path is not blocked by other vehicles nor other objects.
* NLOSv: A V2V link is in NLOSv state if the two vehicles are in the same street and the propagation path is blocked by other vehicles.
* NLOS: A V2V link is in NLOS state if the two vehicles are in different streets and the propagation path is blocked by blockages such as buildings. Obviously, the NLOS state cannot be implemented in the highway scenario, as stated in [7], because the vehicles are always moving in the same street.
###### Abstract  
With the increasing complexity of radar jamming threats, accurate and automatic jamming recognition is essential but remains challenging. Conventional algorithms often suffer from sharply decreased recognition accuracy under low jamming-to-noise ratios (JNR).Artificial intelligence-based jamming signal recognition is currently the main research directions for this issue. This paper proposes a new radar jamming recognition framework called Diff-SwinT. Firstly, the time-frequency representations of jamming signals are generated using Choi-Williams distribution. Then, a diffusion model with U-Net backbone is trained by adding Gaussian noise in the forward process and reconstructing in the reverse process, obtaining an inverse diffusion model with denoising capability. Next, Swin Transformer extracts hierarchical multi-scale features from the denoised time-frequency plots, and the features are fed into linear layers for classification. Experiments show that compared to using Swin Transformer, the proposed framework improves overall accuracy by 15% to 10% at JNR from \(-16\) dB to \(-8\) dB, demonstrating the efficacy of diffusion-based denoising in enhancing model robustness. Compared to VGG-based and feature-fusion-based recognition methods, the proposed framework has over 27% overall accuracy advantage under JNR from \(-16\) dB to \(-8\) dB. This integrated approach significantly enhances intelligent radar jamming recognition capability in complex environments.  
radar jamming recognition; vision transformer; diffusion model; time-frequency analysis 2023
# Atmospheric Density Inversion Based on Swarm-C Satellite Accelerometer
###### Abstract  
We used the Swarm-C accelerometer data to invert the orbital atmospheric density in this study. First, the Swarm-C satellite mission data were obtained from the ESA/s public data platform, and preliminary data error correction was performed. This paper refers to the calibration method of GRACE-A satellite accelerometer data. It adds linear temperature correction on the original basis. Moreover, this study's accelerometer data correction results were compared with the data correction results published by the ESA. In order to explore the influence of light radiation on the accelerometer, we established a geometric model of Swarm-C to simulate the physical shape of the satellite surface. The light radiation pressure model and the shadow area judgment model were established, the change in the light radiation acceleration during the transition process of the satellite from the umbra area to the penumbra area and then to the shadowless area was studied, and the state transition during the transition process was analyzed. Finally, the atmospheric drag coefficient was calculated based on the Sentman model. Atmospheric density inversion calculations were performed using the above data. We show the spatial distribution of atmospheric density at a fixed latitude, testing our results during geomagnetic storms. We compared the density results with existing research data, demonstrating the effectiveness of our approach.  
Swarm-C; atmospheric density; illumination radiation pressure; shadow area judgment; drag coefficient +
Footnote †: journal: Applied  
1
Footnote 1: Department of Geography and Anthropology, Louisiana State University, Baton Rouge, LA 70803, USA 1  
[PERSON]; [PERSON]; [PERSON] Atmospheric Density Inversion Based on Swarm-C Satellite Accelerometer. _Appl. Sci._**2023**, 13, 3610. [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
# Cloud-Based Single-Frequency Snapshot RTK Positioning
## 2 Methodology
### Measurement Generation  
From the previous section we can see that the acquisition outputs actually include four values for each satellite: code delay \(\hat{\tau}\), Doppler offset \(\hat{F}_{D}\), carrier phase value \(\hat{\varphi}\) and the the correct data bit or secondary code shift hypothesis \(u\). There are 3 challenges to be addressed before these results can be used in the SRTK navigation filter. Namely, (1) the code delays are fractional values. That is to say, their values are always between 0 and 1 of the satellite signal's primary code period, thus a set of full pseudoranges need to be computed by augmenting these fractional values with a proper number of full code periods. Fortunately, this can be achieved thanks to the assistance data as explained in Section 2.3.1; (2) the carrier phase measurements are also fractional values between the 0 and 1 cycle, although this is not a problem for the RTK navigation filter since a single-epoch IAR is to be performed. However, the real challenge for carrier phase measurements is caused by the fact that the so called \"data bit ambiguities\" can not be determined for all satellites in a consistent way. It is necessary to compensate for a half cycle bias for some satellites in order to ensure a constant common phase bias that could yield a valid RTK fix. This problem can be tackled by making use of data sources distributed by external organizations [22]; and (3) the timing accuracy plays an important role. For high precision applications like RTK, it is important to know exactly when the signals are transmitted in order to have satellite positions computed correctly. The following subsections give more details about the methodologies used to address these challenges.