esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Influence Zone and Critical Boundary Equations
#### 4.4.2 Influence Zone in the Intermediate Soils  
The coefficients \(p\) and \(q\) for the intermediate soils are shown in Table 5. Figure 11 shows the critical lines for intermediate soils based on the RPFEM results and the proposed Equation (7) using the coefficient shown in Table 5. It shows a good enough agreement and evaluates the critical lines on the safe side although a small discrepancy is observed. It can  
\begin{table}
\begin{tabular}{c c} \hline
**Coefficients** & **Values** \\ \hline \(p\) & \(-1.5\) \\ \(q\) & \(2.68\) \\ \hline \end{tabular}
\end{table}
Table 4: Coefficients \(p\) and \(q\) for cohesive soils.  
Figure 10: Critical line in cohesive soil (\(c=50\) kPa).  
be expressed from the comparison of these coefficients that the influence zone is influenced more by the cohesions and less by the shear resistance angle.  
Based on the above discussion, the RPFEM-obtained solutions can help determine the influence zone of the cavity on the BC/BC\({}_{\rm NV}\) of the footing resting on various types of soils, depending on the shear strength. In addition, it is possible to determine the necessary depth and radius to investigate the presence of the cavity in practical engineering and it also helps to predict the failure mechanism of the footing-cavity system.
## 3 Materials and Methods
### Data Collection Process  
This section describes the procedures for independent data extraction, the methods for gathering data from the included papers, the roles played by the authors during the reviewer process, and any other methods applied to validate and check the accuracy of the data. The data used in this study were gathered from reputable online repositories on IoT-based transformer condition monitoring publications. IoT-based transformer condition monitoring publications from various countries were the subjects of this systematic review. The technique for extracting data is comparable to that employed in [26; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92; 93; 94; 95; 96; 97; 98; 99; 101; 102; 103; 104; 105; 106; 107; 108; 109; 110; 111; 113; 114; 115; 116; 117; 118; 119; 120; 121; 122; 123; 124; 125; 126; 127; 128; 129; 130; 131; 132; 133; 134].  
Figure 3: Search results of selected online repositories.  
A standardized data extraction checklist was created specifically for this review. This checklist was utilized to gather data from published papers of the included studies. The data extraction process was carried out by three separate writers and reviewers. Data were gathered by each reviewer independently from all relevant research. To verify data accuracy and consistency, a comparison of the received data was performed amongst the three reviewers/authors. Any contradictions discovered during this comparison process were handled by conversation, and the final decision was reached by a consensus. Where necessary, an additional reviewer was consulted to establish an agreement. Where information from the studies was ambiguous or lacking in completeness, we highlighted the missing data and recorded them appropriately. No automation techniques were used during the data collection procedure. When numerous reports referred to the same study, established decision methods were used to select the most relevant data. To ensure the integrity of the data included in the evaluation, any conflicts across recorded data were addressed using a systematic validation technique.
# On Parameter Estimation for Bandlimited Optical Intensity Channels
## 5 Numerical Results
For comparison purposes, the FF results are shown in Figure 6. We can see immediately that the FF variant exhibits no floor effect, simply because the receiver filter does not distort the orthogonality of the SRC/SDJ function. On the other hand, it is to be noticed that the jitter performance is only close to the MCRLB for higher values of \(\alpha\) and \(\gamma_{s}>10\) dB; otherwise, the performance degrades compared to MF, mainly because significantly more noise passes the flat filter. Additionally, as a function of the average electrical SNR, Figures 7 and 8 show the evolution of the normalized jitter variance of amplitude estimates using MF and FF scenarios, respectively, again  
Figure 5: Normalized jitter variance for timing estimates (MF receiver). MCRLB is the modified Cramer-Rao lower bound. Figure 6: Normalized jitter variance for timing estimates (FF receiver). operating the link with SRC or SDJ shapes. In contrast to the recovery of the symbol timing, the MCRLB is more or less the same, irrespective of the chosen excess bandwidth. This is also confirmed in Figure 7 by numerical results achieved in the lower SNR range, where they are close to the MCRLB. For larger SNRs, we observe an error floor caused by the violation of the Nyquist condition; again, this sort of degradation is less severe for increasing values of \(\alpha\). Interestingly, given the same roll-off factor, the floor effect is more pronounced for SDJ than for SRC. Not surprisingly, the jitter floor vanishes by application of the FF method, whose jitter performance is visualized in Figure 8. Due to the larger amount of noise at the FF output, we face a higher jitter variance compared to results in Figure 7 by taking the MCRLB as a benchmark; solely for smaller values of SNR and \(\alpha\), the error performance worsens somewhat due to an increasing number of outliers caused by a failure of the estimator algorithm.
# Link and System-Level NOMA Simulator: The Reproducibility of Research
## 3 The NOMA System-Level Simulator
### Features of the System-Level Simulator
#### 3.3.1 Seven-Cell Scenario  
This scenario is illustrated in Figure 5a where seven cells share the boundary(s). Six cells surround the center cell, each of which follows a hexagonal cell structure. Each cell comprised of one BS and two users paired together. These two users are paired together based on the channel gain difference and their corresponding resources are assigned to them accordingly. Each user in a cell experiences interference from its neighboring cell's BSs, and this interference model is considered for each cell. The link performance model in this scenario considers the interference experienced by a user from its six neighboring cells and the consequent BER is calculated.
## 3 BDS Precise Orbit and Clock Products Generation
### Quality of the BDS WHU Orbits
#### 3.2.3 Quality of BDS WHU 1 s Clocks  
In order to evaluate the precision of BDS WHU 1 s clocks, the double-difference approach has been employed. In the first step, the differences of other satellite clocks with the reference clock (BDS C06) from the same ACs have been calculated to remove the clock systematic biases. We choose C06 as a reference because it could be tracked with a relatively long visible arc, considering the coverage of the 1 s regional tracking network (13 stations marked by yellow dots in Figure 1). Actually, the clock comparison results are independent of the choice of the reference satellite clock if the satellite clock is stable. Secondly, the clock differences of the same satellite pairs from different ACs were measured. It is noted that only the epochs of satellites' elevations with respect to LASA station higher than 7\({}^{\circ}\) are used. The corresponding STD of the double-difference clocks were used as indicators of clock quality. The STD of clock differences have been calculated for all available satellites using the following formula:  
\[\mathrm{STD}=\sqrt{\frac{\sum_{i=1}^{n}\left(\Delta_{i}-\overline{\Delta} \right)\left(\Delta_{i}-\overline{\Delta}\right)}{n}}\]  
where \(\Delta_{i}\) is the double-difference of the WHU 1 s clock with the others clock (i.e., WHU 30 s, GBM, COM) at epoch \(i\), \(\overline{\Delta}\) is the average bias, and \(n\) refers to the number of epochs.  
To assess the precision of WHU 1 s clocks, we conducted external and internal evaluations, respectively. For the external evaluation, we compared WHU products with COM and GBM. Since they only have 300 s clock products (see Table 1), we resampled our 1 s clocks to 300 s intervals before comparison.  
Figure 3 shows the STD values of satellite-specific clock differences of BDS WHU 1 s products with respect to COM and GBM products. The clock errors were also calculated in terms of three satellite types--GEO, IGSO, and MEO--and listed in Table 5. The clock errors of GEO are relatively large compared with the errors of IGSO and MEO. The RMS values of GEO clock errors could reach up to 0.63 ns, whereas the RMS of IGSO and MEO clock errors are about 0.2-0.3 ns and 0.1 ns, respectively.  
For the internal evaluation, the WHU 1 s clock was compared at 30 s intervals with WHU 30 s clock, similarly. As is shown in last column of Table 5, WHU 1 s and WHU 30 s products are almost the same, with STD of differences less than 0.1 ns. The comparison not only proves that WHU 1 s clocks show a high degree of consistency with WHU 30 s clocks, but also indicates that 13 regional stations could achieve the same accuracy as 68 global stations in the China-Australia area.  
Figure 3: Comparisons of BDS WHU 1 s clocks with respect to COM, GBM, and WHU 30 s products.
# Security Challenges of Location Privacy in VANETs and State-of-the-Art Solutions: A Survey
## 2 Background
### Attacks in Location-Based Security (LBS)  
This portion identifies various types of attacks that are important to the position of privacy in the literature of Mobile Services and Vehicular Networks. Furthermore, we classify the attacks related to VANETs security requirements namely: Availability, Confidentiality, Authentication and Integrity [29], which are shown in Figure 3.  
1. _DoS_--Denial of service (DoS) is one of the most anticipated attacks in VANETs. This can be carried out by an internal or external malicious vehicle. The purpose of an adversary is to jam the communication between authorized communicating vehicles. The attack can also be launched in distributed manner called distributed denial of service (DDoS).
2. _Trace Analysis Attack_--In this attack, the adversary connects the traditional cloaking area to the user's mobility sequence. The chance can be calculated from the user's LBS registry at a separate position in the cloaked region [20].
3. _Colluding Attack_--Proof of the wrong location is created when two nodes collide with each other. For example, if one malicious node wants to show that it is in a wrong location, it may have an additional colluding node to jointly produce false location evidence to support it [30].
4. _Location Link_--An attack in which the location details used in the user query is used as a quasi-identifier to re-identify the identification of the consumer is defined as a role connecting the assault in [31].
5. _Query Sampling_--In the Query sampling the opponent uses the user's position details to relate the user's location to a specific application.
6. _Snapshot Location Attack_--The writers in [25] address inference attack close to trace research assaults. The user's potential path is tracked by inference, which depends on the user's past position.
7. _Request Tracking Attack_--In continuous LBS, the existence of the client enables queries to be tracked as the requests have a lifespan. A consumer who has been disguised with other users on several occasions during the query's lifespan is susceptible to this attack [27].
8. _Trajectory Attack_--If a given trajectory is used by an LBS-server recipient to deduce the trajectory [32]. Trajectory assaults will also be carried out even though the user identity has been removes.
9. _Transformation Attack_--Previous results are used by the adversary to measure the transformation likelihood for each potential intersection turn [23].
10. _Reconstruction Attack_--The adversary tries to recreate the real trace by applying probability to incidents that could be linked to the user's path [33].
11. _Inference Attacks_--Similar to trace investigation assaults, Adversaries map past movements to assess potential locations [34; 35].
# An Overview on the Applications of Matrix Theory in Wireless Communications and Signal Processing
###### Abstract  
This paper overviews the key applications enabled by matrix theory in two major fields of interest in electrical engineering, namely wireless communications and signal processing. The paper focuses on the fundamental role played by matrices in modeling and optimization of wireless communication systems, and in detection, extraction and processing of the information embedded in signals. Among the major applications in wireless communications, the role of matrix representations and decompositions in characterizing multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM) communication systems is described. In addition, this paper points out the important contribution made by matrices in solving signal estimation and detection problems. Special attention is given to the implementation of matrices in sensor array signal processing and the design of adaptive filters. Furthermore, the crucial role played by matrices in representing and processing digital images is depicted by several illustrative applications. This paper concludes with some applications of matrix theory in the area of compressive sensing of signals and by outlining a few open research problems for future study.  
matrix; signal processing; wireless communications; image processing; compressive sensing +
Footnote †: journal:  
0000-0001-5000-5000-5000-5000
## 3 The \(\alpha\)-\(\beta\)-\(\eta\)-\(\theta\) Filter
### Relationship to Kalman Filters  
Kalman filters are optimal tracking filters and are based on the adaptive calculation of a gain matrix. The \(\alpha\)-\(\beta\)-\(\eta\)-\(\theta\) filter (and the \(\alpha\)-\(\beta\) filter) is equivalent to steady state Kalman filters [31]. Thus, we derive the optimal gains for the motion model under consideration from the Kalman filter equations [4]:  
\[\tilde{\mathbf{x}}_{k} =F\hat{\mathbf{x}}_{k-1}, \tag{7}\] \[\tilde{\mathbf{P}}_{k} =F\hat{\mathbf{P}}_{k-1}F^{\mathrm{T}}+\mathbf{Q},\] (8) \[K_{k} =\tilde{\mathbf{P}}_{k}H^{\mathrm{T}}(H\tilde{\mathbf{P}}_{k}H^{\mathrm{ T}}+\mathbf{B})^{-1},\] (9) \[\hat{\mathbf{x}}_{k} =\tilde{\mathbf{x}}_{k}+\mathbf{K}_{k}(\mathbf{z}_{k}-H\tilde{\mathbf{x}}_{k}),\] (10) \[\tilde{\mathbf{P}}_{k} =\tilde{\mathbf{P}}_{k}-\mathbf{K}_{k}H\tilde{\mathbf{P}}_{k}, \tag{11}\]  
where \(\mathbf{x}\) is a state vector, forecasts and estimates are denoted by tildes and hats, respectively, superscript \"T\" and \"\(-1\)\" denote transpose and inversion, \(\mathbf{z}\) is a measurement vector, \(\mathbf{F}\) is the transition matrix, \(\mathbf{P}_{k}\) is the error covariance matrix at time \(kT\), \(\mathbf{Q}\) is the covariance matrix for process noise, \(\mathbf{K}_{k}\) is the optimal gain (Kalman gain) at time \(kT\) and \(\mathbf{B}\) is the covariance matrix for the measurement noise.  
The \(\alpha\)-\(\beta\)-\(\eta\)-\(\theta\) filter is obtained by substituting into Equations (7) and (10) vectors \(\tilde{\mathbf{x}}_{k}=(x_{\mathrm{p},k}\ v_{\mathrm{p},k})^{\mathrm{T}}\), \(\hat{\mathbf{x}}_{k}=(x_{\mathrm{s},k}\ v_{\mathrm{s},k})^{\mathrm{T}}\), \(\mathbf{z}_{k}=(x_{\mathrm{o},k}\ v_{\mathrm{o},k})^{\mathrm{T}}\), and matrices:  
\[\mathbf{F} =\begin{pmatrix}1&T\\ 0&1\end{pmatrix}, \tag{12}\] \[\mathbf{H} =\begin{pmatrix}1&0\\ 0&1\end{pmatrix},\] (13) \[\mathbf{K}_{k} =\begin{pmatrix}\alpha&T\eta\\ \beta/T&\theta\end{pmatrix},\] (14) \[\mathbf{B} =\begin{pmatrix}B_{\mathrm{x}}&0\\ 0&B_{\mathrm{v}}\end{pmatrix} \tag{15}\]  
(see [31]), where \(B_{\mathrm{x}}\) and \(B_{\mathrm{v}}\) are the error variances of \(x_{\mathrm{o},k}\) and \(v_{\mathrm{o},k}\), respectively. With \(k\to\infty\) for \(\mathbf{K}_{k}\), the appropriate gains of the \(\alpha\)-\(\beta\)-\(\eta\)-\(\theta\) filter are calculated as the Kalman filter predicts the state with the minimum error for the assumed target model.
# Improved Dragonfly Optimization Algorithm for Detecting IoT Outlier Sensors
## 5 Simulation Results  
In the implementation and evaluation, we used an Intel(r) Core(tm) i5-4460 CPU @3.20 GHz processor, 12 gigabytes of memory, and a Windows 10 (64-bit) system. The efficiency of the proposed method was evaluated using MATLAB software. The algorithm of the proposed method was implemented in this software, and simulations were carried out at different stages. MATLAB optimization functions were used in the process. The simulation work was repeated up to 2500 steps until the results reach a stable point.  
In this study, we compared the proposed method with three methods from the literature, including the LEACH [35], RED-LEACH [36], and gravitational search algorithm (GSA)-based methods [37]. The LEACH method is one of the first clustering methods in sensor networks. In the proposed method, four parameters are used to evaluate the efficiency:  
* The quantity of nodes in the exposure zone: The coverage area includes the area where the nodes are located.
* Network lifetime: The network lifetime includes the time from the beginning of the first node to the time when the first node dies.
* Network capacity: The ratio of the number of packets received by the well to the quantity of packets sent by the nodes.
* Residual energy: The remaining energy in the battery of the sensor nodes is used to analyze the energy consumption in each method.  
In this scenario (Figure 5), the sink is placed in the center of a WSN.  
Nodes are clustered, and each cluster head sends data directly to the sink without the use of an auxiliary node. The objectification parameters of this scenario are explained in the following information: The size of the grid in this study is set to 100 \(\times\) 100 m\({}^{2}\). The main station location is (60, 120). The sensor node quantity is 110. The size of the data package is set at 500 bytes, \(E_{el}\) is set 50, the \(\mathcal{E}_{fs}\) is set 10, the value of the \(\mathcal{E}_{amp}\) is considered 0.0010, and the initial energy is set at 0.5 joules.  
Figure 5: Coverage scenario in a WSN using the suggested algorithm.
# Technologies for the Production of Fertilizable Mammalian Oocytes
## 2 The Production of Fertilizable Oocytes
### Folliculogenesis
However, FSH may affect the size of primordial follicles and population of early secondary follicles, which challenges the gonadotropin-independent theory regarding the early folliculogenesis [51; 52]. As soon as follicles start growing, theca cells (TC) are recruited from surrounding stroma to form two distinct layers: internal theca, which secretes androgens, and externa theca, which contains smooth muscle cells and collagen fibers (Figure 1) [43]. The formation of TC layers is regulated by both the oocyte (via the production of growth differentiation factor-9, GDF-9, a member of the transforming growth factor \(\beta\), TGF\(\beta\), superfamily), and GC (via the production of Kit ligand, KL). Concomitantly with theca formation, receptors for Luteinizing Hormone (LH) becomes detectable [53]. The establishment of a complex regulative network between oocyte, GC and TC requires either the formation of gap junctions or the production of regulative paracrine factors and is fundamental to control whole folliculogenesis and fertility [54; 55]. GC produce activins and inhibits, which regulate not only GC differentiation but also oocyte maturation and androgens synthesis by TC [56; 57]. In addition, the oocyte increases in size, and secretes the glycoproteins of zona pellucida (ZP), an extracellular matrix that will play a key role in the block of polyspermy after fertilization. The contribution of follicle cells to the production of zona components is species-specific. While mouse oocytes are capable of synthesizing all zona proteins [58], in humans or large mammals, both the oocyte and the follicle cells contribute to their synthesis [59]. The transition from secondary to antral stage is characterized by the FSH-dependent appearance of a fluid-filled cavity, the antrum, which determines the differentiation of GC into two distinct populations, the cumulus cells (CC) and the mural GC (MGC). These cells have not only a different morphology, but also different functions and gene expression [53]. The final steps of development are completed by few \"privileged\" follicles, selected in the pool of growing ones. This phenomenon is called dominance. A dominant follicle has more GC compared to other follicles and produces more estradiol (E2) to induce negative feedback on FSH secretion [60]. Consequently, when FSH secretion decreases, only the dominant follicle(s) survive. Recently, some microRNAs (miR), such as miR-144, miR-202 and miR-873, have been found to be upregulated in healthy follicles in comparison with atretic ones [61]. The mature (Graafian) follicle has reached the max size (in humans \(>\) 10 mm), and the enclosed GV-stage oocytes has reached its final diameter (about 120 \(\upmu\)m for humans).
## 4 Discussion
In addition, this paper also demonstrated the use of a model reduction technique which significantly benefit the parametric studies in design and optimization. The predictive model was established based on the POD and the Kriging method. The predictive capability of the reduced-order model was good and the calculation time was drastically reduced compared to the corresponding finite element simulation. The discrepancy was within \(\pm 5\%\). With a wide range of helmet parameters, the linear and rotational accelerations of 5124 cases were predicted within 20 min. The optimization process could be performed effectively with a large amount of data. The presented study has the following limitations. The head-helmet finite element model was only validated against the DOT drop test. It has not yet been validated against the 45\({}^{\circ}\) inclined drop test. Strain-based injury metrics are desirable for assessing brain injury, this study only looked into kinematics-based metrics (acceleration). However, there have been some studies investigating the correlation between head rotation and brain strain measures [23]. Another limitation is that this work only considered the half helmet type, and the results from this study may vary for other helmet types.