esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Vessel Origin (VO) Feature Selection
#### 2.4.3 Vessel Radiation  
The vessel radiation of a pixel \(p(x,y)\) is defined as:  
\[F_{VR}\left(x,y\right)=\sum\limits_{i=1}^{n}\left|\overline{t}_{i}\cdot\frac{ \vec{a}_{i}}{a_{i}}\right| \tag{5}\]  
Where \(n\) denotes the number of neighbors in the mask region centered at \(p(x,y)\). \(\overline{t}_{i}\) denotes the vessel flux through the \(i^{th}\) neighbor, which is tangent to the vessel segment. \(\vec{\overline{a}}_{i}\) is the vector from the \(i^{th}\) neighbor to the center of mask region.  
\(a_{i}\) is the magnitude of vector \(\vec{\overline{a}}_{i}\) (_i.e._, \(a_{i}=\left|\vec{\overline{a}}_{i}\right|\) ). The dot product of \(\overline{\overline{t}}_{i}\) and \(\vec{\overline{a}}_{i}/a_{i}\) means the component of \(\overline{\overline{t}}_{i}\) in \(\vec{\overline{a}}_{i}\) direction. In Equation (5), size of the mask is \(r\times 2r\), \(r\) denotes the radius of optic disc, and \(n\) equals \(2r^{2}\). The radiation is calculated to get the radiation image \(F_{VR}\), as shown in Figure 4d. The gray level amount in the image \(F_{VR}\) is the value of the radiation feature. The vessel radiation of a pixel \(p(x,y)\) represents the radiating level of blood vessels which emit from \(p(x,y)\). As retinal vessels grow out from vessel origin, vessel radiation around vessel origin is always higher.
# Examining Memory Performance in Senior Adults:
## 2 Materials and Methods
### Sample Description  
A total of 73 people participated. Thirty participants were recruited in two day centres in Vigo, namely, Parque Castrelos and Os Cortizos. An additional recruitment campaign was implemented in the same area through the presentation of informative material, which made possible the inclusion of 43 volunteers not affiliated with these day centres. All participants gave their informed consent prior to their participation in the study, the protocol having been previously approved by the Ethics Committee of Galicia (2023/503), thus ensuring compliance with the respective ethical regulations and the protection of the participants' privacy. The geographical area where participants were enrolled does not possess unique demographic, cultural, or socio-economic characteristics that distinguish it from other urban-rural areas in Western Europe. However, its applicability to other regions worldwide remains uncertain. Nonetheless, the sample reasonably reflects the male and female distribution within the general population of that age group (60 to 99 years) in Western Europe.  
This study was designed as a cross-sectional study in which all participants were assessed in one session by means of the Memory Failures in Everyday questionnaire (MFE-28 [14]), the Lawton and Brody scale [15], MoCA and 15-item Yesavage geriatric depression scale (GDS-15). The individual sessions lasted approximately 30 min. Data collection began in February 2024 and was carried out until the end of March 2024. Participants who did not provide clinical diagnosis details were evaluated by a psychologist with specific training in psychogerontology.  
Participants ranged in age from 60 to 99 years (mean = 75.78 \(\pm\) 9.161). Of the participants, 30% (22) were male and 69.9% (51) female. The percentage of participants with depressive symptomatology and MCI was 28.8% (21), the percentage of people suffering from depressive symptoms without MCI was 13.7% (10), and the group of people with MCI represented 17.8% of the sample (13 participants), while 39.7% (29) were healthy people with neither MCI nor depressive symptoms. The participants' distribution in study groups and the description of the sociodemographic variables are collected in Tables 1 and 2.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline  & & **D+MCI+** & **D+MCI-** & **D\(-\)MCI+** & **D\(-\)MCI-** & **n** & \(p\) \\  & & **n = 21** & **n = 10** & **n = 13** & **n = 29** & **n = 73** & \\ \hline Educational level & \textless{}12 & 95.2\% (20) & 80\% (8) & 69.2\% (9) & 44.8\% (13) & 50 & \\  & \textgreater{}12 & 4.8\% (1) & 20\% (2) & 30.8\% (4) & 55.2\% (16) & 23 & \\ Household & LA 1 & 33.3\% (7) & 20\% (2) & 15.4\% (2) & 31\% (9) & 20 & \\  & LWO & 66.7\% (14) & 80\% (8) & 84.6\% (11) & 69\% (20) & 53 & \\ \hline \end{tabular}
\end{table}
Table 1: Participants’ sociodemographic characteristics.
###### Abstract  
Urogenital infections impact millions of individuals globally each year, with vulvovaginal candidiasis (VVC) being one of the most prevalent conditions affecting women. _Candida albicans_ is the primary pathogen responsible for VVC. The utilization of probiotics as an alternative therapeutic approach to antibiotics in managing such infections has gained increasing attention. This study aimed to evaluate the potential of THY-F51, a lactic acid bacterium isolated from kiwi, as a probiotic to support vaginal health through its antifungal, anti-biofilm, and anti-inflammatory properties against _C. albicans_. The identification of THY-F51 was confirmed through 16S rRNA gene sequencing. A series of evaluations were performed to determine its antifungal efficacy against _C. albicans_, biofilm-inhibitory activity, antioxidant properties, and effects on inflammatory cytokines. Cytotoxicity assays and assessments of bacterial survival under vaginal pH conditions (pH 3.8-4.5) were also conducted. The results demonstrated that THY-F51, identified as _Leuconostoc citreum_, exhibited potent antifungal activity against _C. albicans_, with an MIC of 1.25 mg/mL and an MFC of 2.5 mg/mL. Furthermore, THY-F51 displayed a strong inhibition of _C. albicans_ biofilm formation, as well as notable antioxidant activity in the supernatant. Additionally, THY-F51 demonstrated high survival rates under vaginal pH conditions, an absence of cytotoxic effects, and a significant reduction in _C. albicans_ adhesion to HeLa cells. Moreover, THY-F51 effectively suppressed _C. albicans_-induced inflammatory cytokines, including TNF-\(\alpha\), IL-1\(\beta\), IL-6, and IL-8. These findings suggest that THY-F51, isolated from kiwi, holds substantial promise as a safe and effective probiotic for reducing vaginal inflammation and promoting vaginal health.  
vulvovaginal candidiasis; antifungal; lactic acid bacteria; _Leuconostoc citreum_  
([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Structured Light Patterns Work Like a Hologram
## 1 Introduction  
The transmission of 3D holographic video content along conventional communication channels requires substantial compression of the holographic information, as demonstrated in [1; 2; 3; 4; 5]. It is shown in [6], through the analysis of the modern situation in this area, that the traditional methods of entropy coding are currently far from being able to implement a \(10^{6}\)-fold compression of the holographic information, which is necessary for transmission. For this reason, an algorithm was proposed that provides the transmission of 3D holographic information in the form of two basic modalities of a 3D image, namely, the surface texture of the holographic object (the 3D scene) and its topographic depth map [7], which allows the necessary compression of the holographic information and its transmission similar to the transmission of a radio signal by means of single-sideband modulation (SSB). One of the methods of depth mapping is the technology of structured light lateral projection [8; 9; 10; 11; 12; 13]. The vertical fringes of structured light form a pattern of spatial bands similar to a spatial frequency carrier hologram, and their deformation is similar to deviations containing the information on the 3D object. Light diffraction from a structure of this kind forms several orders so that one of these orders restores the image of the 3D depth map. The numerical superposition of the texture of the holographic object onto this map leads to the creation of an image of the 3D object. Another possible version of the restoration of a 3D image is in texture superposition directly on the structured light pattern in the formof vertical fringes. Diffraction on this structure in the minus-first order will restore the 3D image, as this happens in the case of holograms of focused images when the number of Rayleigh zones is much more than ten. This is similar to the restoration of the initial object by a hologram of focused images and simplifies the computer rendering of dynamically changing scenes restoring the 3D holographic video stream, which possesses continuous parallax, both horizontal and vertical, and high spatial resolution, not worse than Full HD and even 4K standard.
## 3 Numerical Simulations
### Parameter Identification under Gaussian and Non-Gaussian Noises  
Simulation models with Gaussian noises dominate the research field of structural parameter identification. Normally, it is assumed that the noise has a Gaussian distribution, and its rationality can be proved by the central limit theorem. However, considering the real structures are often subjected to  
Figure 5: Damping estimation by three integration methods: (**a**) the 1 st story; (**b**) the 2 nd story; (**c**) the 3 rd story; (**d**) the 4 th story.  
Figure 6: Relative damping errors by three integration methods.  
the complex environment, it contains more or less non-Gaussian noise actually. In the case when the non-Gaussian noise is prominent, the system tends to be unsuitable if the Gaussian hypothesis is still made. Therefore, it is of great theoretical and practical significance to study the structural parameter estimation under non-Gaussian noises.  
The distributions of Gaussian and non-Gaussian noises are shown in Figure 7. The noise is added to the absolute acceleration \(\ddot{x}_{a}\) to simulate the observed acceleration. When the noise has a Gaussian distribution, the observed acceleration \(\ddot{x}_{m}\) is:  
\[\ddot{x}_{m}=\ddot{x}_{a}+a\cdot RMS_{\ddot{x}_{a}}\cdot random_{1} \tag{22}\]  
where \(a\) is the Gaussian noise level, \(RMS_{\ddot{x}_{a}}\) is the root mean square value of the absolute acceleration, and \(random_{1}\) is the standard normal distribution.  
The observed acceleration \(\ddot{x}_{a}\) with the additive non-Gaussian noise can be expressed as:  
\[\ddot{x}_{n}=\ddot{x}_{a}+a\cdot RMS_{\ddot{x}_{a}}\cdot random_{2} \tag{23}\]  
where \(random_{2}\) follows the t distribution with the DOF being 2 and the mean value being 1.
## 4 Discussion
### Key Observations and Insights  
Our study yielded several significant findings and observations:  
Figure 5: A 3D representation of the original phase and the calculated phases FSD, FSD-HSO and FSD-PSO of the examples of Figure 4. The figure illustrates a set of gaussian distributions, each colored with a gradient that starts from a deep, dark blue at the minimal value (base) and transitions to a lighter shade of yellow at the maximum value (top). (**a**) \(F_{xx}=F_{yy}=1\) and (**b**) \(F_{xx}=F_{yy}=10\).  
1. Method Robustness: The results indicate that the FSD technique performs adequately with low-frequency interferograms, successfully recovering the phase in most scenarios. However, some challenges arise, particularly in cases with central peaks and scenarios with multiple Gaussians, Figures 2a and 2d, respectively.
2. FSD-HSO Performance: FSD-HSO exhibits improved performance compared to the standard FSD method, yielding better results and fewer artifacts. Nevertheless, some regions in the interferograms still present noticeable artifacts, affecting the quality of the demodulated phase.
3. Superiority of FSD-PSO: FSD-PSO emerges as the standout performer among the three algorithms. It consistently produces more accurate and faithful results, with reduced artifacts and a closer resemblance to the original phase.
4. Frequency Sensitivity: The experiments with varying Gaussian widths and amplitudes provide insights into frequency sensitivity. Both FSD and FSD-HSO exhibit limitations as fringe frequencies increase, with notable distortions observed in Gaussian peaks. FSD-PSO demonstrates a more stable response, but it also exhibits some sensitivity to higher frequencies.
## 1 Introduction
Meanwhile, combined with the C/N\({}_{0}\)-dependent stochastic model and RKF parameter estimation model, the positioning performance of smartphones in GNSS-degraded environments is effectively improved. In Section 2, we first describe the experimental environments and devices; the number of visible GNSS satellites, PDOP, and C/N\({}_{0}\) observed by Huawei Mate 30 and Mate 10 on the playground and sidewalk are compared and analyzed, and the IMU data of ADIS16405, Huawei Mate 30, and Huawei Mate 10 are compared by cumulative distribution function combined with the specifications of IMU. After that, in Section 3, based on the uncombined PPP model and linearized state equation of INS, we establish a multi-GNSS uncombined PPP/INS coupled model with the C/N\({}_{0}\)-dependent stochastic model and parameter estimation model based on RKF. In Section 4, we show the PPP/INS dynamic experiments and results in multiple GNSS-degraded environments, and the experimental results demonstrate that the proposed PPP/INS mathematical model provides higher positioning accuracy and continuous positioning solutions, compared with the conventional PPP method using smartphones. Finally, we draw conclusions and add remarks in Section 5.
## 3 Principles of Speed Estimation for Inter-Symbol Processing
### Data-Assisted Phase Balancing Based  
Due to the many symbols that are communicated, the signals that are received at the receiving end have an inherent phase deviation. As a result, the MPSK signal is inherently out of phase, which surely adds a large amount of inaccuracy to the process of using phase for speed measurement. It is necessary to balance the signal's initial phase difference before executing the Fourier transform. Many different communication systems use QPSK, a modulation technique with strong anti-interference and good spectrum utilization. For a QPSK signal, there are four possible initial phases due to its special modulation method. Assuming that the \(0^{\circ}\), \(90^{\circ}\), \(180^{\circ}\), and \(270^{\circ}\) phase modulation is chosen, the \(\theta_{k}\) corresponding to the '01' symbol is \(\pi/2\), and the expression can be expressed as follows:  
\[r_{01}(t)=\sqrt{2P_{1}}A_{k}e^{j\frac{\pi}{2}}p(t-kT)e^{j[\omega_{k}t+\theta_{ k}+\theta_{k}(t)-\pi/2]}+n(t), \tag{11}\]The remaining three symbols are 0, \(\pi\), and \(3\pi/2\).  
Next, we need to analyze the initial phase of each symbol. This step involves balancing the initial phases of the different symbols, i.e., subtracting the corresponding initial phase \(\theta_{k}\) within the waveform of each symbol to obtain a phase-balanced signal \(\widetilde{r}_{01}(t)\).  
\[\widetilde{r}_{01}(t)=\sqrt{2P_{t}}A_{k}e^{j\frac{\pi}{2}}p(t-kT)e^{j[\omega_{ t}t+\theta_{d}+\theta_{c}(t)-\pi/2]}e^{j(-\frac{\pi}{2})}+n(t). \tag{12}\]  
The phase information of the signals processed in this way has been corrected, eliminating the effect of the initial phase differences between the different symbols of QPSK on the subsequent utilization of phase speed measurement. At this point, the time domain expression for each symbol waveform can be expressed by Equation (13).  
\[\widetilde{r}(t)=\sqrt{2P_{t}}A_{k}p(t-kT)e^{j[\omega_{t}t+\theta_{d}+\theta_{ c}(t)-\pi/2]}+n(t). \tag{13}\]
## 8 SQI and RAIM Interaction  
The SQI introduced in the previous section has to be integrated within the algorithms of PVT and RAIM in order to improve the accuracy of the estimated position. The simplest integration method could be to set a threshold \(\text{SQ}_{\text{thres}}\), and to exclude the satellites with \(\text{SQI}<\text{SQ}_{\text{thres}}\) from the PVT computation. However, in the case of a low number of satellites in view, the problem of the satellite-user geometry has an impact on the accuracy, and this should be considered in the mechanism of SQI/RAIM/PVT integration. This means that a trade-off has to be found, considering also the effect of the Geometric Dilution Of Precision (GDOP) (or simply Dilution Of Precision (DOP)) [17], on the  
Figure 10: Block diagram of the FDE algorithm.  
estimated position. In fact in some cases the error introduced by the worst DOP (due to a satellite exclusion) could be bigger than the error due to the effect of MPs in some of the satellites in view included in the PVT computation.  
In the case of a bad geometry and of strong distortions, instead of removing degraded satellite signals from the PVT, another possible approach is to penalise satellites with a poor signal quality in the WLS solution of the navigation equations, by changing the diagonal noise covariance matrix \(\Sigma\), as  
\[\Sigma_{k,k}(t)=\sigma_{k}^{2}(t)+\sigma_{k}^{2}(t)\gamma(1-SQI(t))\]  
where \(\gamma\) is a penalty weight. The diagonal entry \(k\) can assume values from \(\sigma_{k}^{2}(t)\), when SQI = 1 (corresponding to a signal in good conditions), to \((\gamma+1)\sigma_{k}^{2}(t)\) when SQI = 0 (corresponding to a strong distortion). In this way, we can reduce the effects of the errors in the PRs projected in the position domain. This method is denoted as P-WLS in the following (where P stands for penalty).
# Enhancing Reactive Ad Hoc Routing Protocols with Trust
## 3 The Proposed Method of Trust-Aware Reactive Ad Hoc Routing
### Delay Functions
#### 3.5.1 Constant Delay Function  
The most straightforward function that we experimented with is the Constant Delay function. The idea is to set the delay value to the maximal delay (_MD_) every time the estimated _PDR_ of the neighbor node drops below the \(THR\). The purpose of the \(THR\) value is not to penalize the nodes with relatively high _PDR_s. The value of \(D\) is calculated as a function of \(THR\), \(MD\), and estimated \(PDR\) (Algorithm 1).  
```
Input:\(THR\in[0,1];MD>1;PDR\in[0,1]\) Output:\(D\geq 0\) if\(PDR<THR\)then \(D\gets MD\) else \(D\gets 0\) endif return\(D\)
```  
**Algorithm 1** Constant Delay function specification  
We expect this function to have the worst results among all metrics. However, it helps us see whether delaying the RREQ messages can be successful at all. We also expect that higher _MD_ values increase the resulting \(PDR_{avg}\) and both _RDD_ metrics.
## 3 Results and Discussion
### Geometric Model  
Since the lunar revolution period is equal to its rotation period, the Moon is always facing the Earth on the same side, which is referred to as tidal locking. Additionally, the nutation, libration, and precession of the Moon will also lead to changes in the relative position and attitude between the Moon and the Earth.  
To better analyze the geometric characteristics of Earth observations, we established a simplified MS-BISAR Earth observation model in the ITRS, with the three assumptions mentioned earlier, as shown in Figure 5. In this model, the Earth is fixed, while the Moon and the satellite rotate around it. Parameters such as the position and velocity of the platform and targets on the Earth can be obtained by reference system transformation as introduced in the previous section.  
Based on the simplified MS-BiSAR Earth observation geometric model, we can derive several parameters, such as the nadir position and local incidence angle, for further analysis.
## 4 Simulation Environment
### MCS Selection  
After the RBs, power, and precoder weights are allocated to the UEs, the proper MCS must be selected. We have implemented an algorithm where a single MCS is selected for all RBs allocated to a particular UE. Each MCS is associated with its spectral efficiency \(SE_{mcs}\), \(\beta_{mcs}\), and minimum \(\text{ESEM}_{i,mcs}\), which are necessary to ensure a Block Error Rate (BLER) below 10%. Additionally, two parameters, \(c_{mcs}\) and \(b_{mcs}\), are required for the BLER calculation. Because parameters suitable for 5G are not easily accessible, we used the values proposed for MCS modeling in LTE. These values are summarized in Table 1[24; 25]. To select an MCS for a given UE, we compute \(\text{ESEM}_{i,mcs}\) for each value of \(\beta_{mcs}\) from Table 1 according to (5), using the expected \(S1\tilde{N}R_{i,r}\) computed during the radio resource scheduling procedure. The selected MCS is the one associated with the highest \(\text{ESEM}_{i,mcs}\) that exceeds the minimum requirement.
## 1 Introduction
This method does not require observing specific targets and has wider applicability. In the following, Section 2 analyzes the antenna pattern errors, including the antenna pattern differences, antenna pattern measurement errors, backlobe contributions, and cross-polar contributions. In Section 3, these errors are classified as multiplicative errors and additive errors, and a two-step correction method is proposed to correct the antenna pattern errors step by step. The first step is to correct the antenna pattern additive errors using the artificial brightness temperature distribution. The second step is to reduce the antenna pattern multiplicative errors by using the average antenna brightness temperature. In Section 4, the simulator was used to verify the effectiveness of the correction method quantitatively. Additionally, flight experiment results also show that the image quality is improved after the two-step correction method is adopted. Section 5 analyses the results. Finally, the conclusions are summarized in Section 6.
# On the Analysis of Coverage Feedback in a Fuzzing Proprietary System
## 1 Introduction
Although the evaluation target is based on open-source software, we believe that the stochastic tendencies we find and analyze in our evaluation should be equally valuable. In short, our study revealed that the quality of initial seed (initially given the test case data for mutation) has a strong correlation to coverage feedback efficacy. When adequate initial seeds were provided, the efficiency in bug reproduction showed minimal performance variation regardless of whether coverage feedback is given or not. Such an observation suggests that investing in quality initial seed data might sometimes be more beneficial than solely on enhancing coverage feedback with significant porting/engineering efforts in fuzzer development. For the efficient fuzzing of proprietary systems, we emphasize the importance of balanced resource allocation between developing sophisticated coverage feedback mechanisms and generating comprehensive initial test cases.
## 3 Experimental Design and Implementation
### Experimental Procedure
#### 3.3.2 Data Cleaning Workflow  
Data cleaning is a crucial step in ensuring data quality, proceeding according to the following workflow:  
1. Redundant data removal: Executing the dropDuplicated() function to remove duplicate records based on the combined uniqueness of vehicle ID, timestamp, and coordinates. During processing, the number of removed records is tracked in real time to assess the degree of data redundancy.
2. Anomalous data identification: Generating road network visualization through the plotRoadnet() function for manual identification of anomalous points obviously deviating from the road network. Based on identification results, spatial range thresholds are set for automatic anomalous data screening.
3. Anomalous data removal: Executing the removeOutlier() function to delete trajectory points with abnormal spatial positions and vehicles with only single trajectory points. Simultaneously, using the sortVidbyTime() function to sort vehicles by time, facilitating subsequent temporal analysis.
4. Data integrity check: Checking processed data for missing values and verifying data completeness.