esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This study shows that Mu-MIMO channels formed by links with Nakagami-\(m\) fading, when assisted by large intelligent surfaces, have an SINR with Gamma distribution and can considerably reduce their bit error probability by increasing the number of reflectors or the efficiency of the phase optimization algorithm (related to the \(\kappa\) parameter of the phase error distribution).  
The worst case, considering the Nakagami-\(m\) fading channel, is the Rayleigh fading (for \(m=1\)); this study shows that Rayleigh channels can be converted to Gamma fading channels by applying the phase shift matrix of the LIS. It was also shown that creating a line of sight in scenarios where a LoS did not exist was possible. This study presented the probability distributions of SINR compared to the PDF of the Gamma distribution. It showed that the exact formula for the probability density is very close to the one generated by the histogram obtained from the channel simulation.  
It was possible to show that the LIS can enable a low secrecy outage probability, even for a few reflecting units and low bit error probability values, when increasing the number of LIS elements or improving the quality of phase correction. Our study assumes complete knowledge of the channel state information. It considers phase errors due to the behavior of the reflecting panel when attempting to correct the phase of many signals simultaneously (and not being able to optimize this for all) or due to the influence of the superposition of electromagnetic signals.  
Conceptualization, R.C.F., G.F., F.A.P.d.F. and E.R.d.L.; Formal analysis, R.C.F., G.F., F.A.P.d.F. and E.R.d.L.; Writing--original draft, R.C.F., G.F., F.A.P.d.F. and E.R.d.L. All authors have read and agreed to the published version of the manuscript.  
Figure 8: Secrecy Outage Probability.  
**Funding:** This work was partially funded by CNPq (Grant Nos. 302077/2022-7, 403612/2020-9, 311470/2021-1, and 403827/2021-3), by the Sao Paulo Research Foundation (FAPESP) (Grant No. 2021/06946-0), by the Minas Gerais Research Foundation (FAPEMIG) (Grant No. APQ-00810-21) and by the project \"Resource-aware Machine Learning Model Optimization for Edge Computing\", supported by xGMobile - EMBRAPII-Intel Competence Center on 5G and 6G Networks, with financial resources from the PPI IoT/Manufatura 4.0 from MCTI grant number 052/2023, signed with EMBRAPII.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Results and Discussions
### Effect of Surcharge Magnitudes
#### 4.2.1 Pile Deflection  
Figure 14 illustrates the variations of non-dimensional pile deflection with depth under different surcharge magnitudes (\(p\) = 34.375, 84.375, 134.375, and 184.375 kPa). There are significant positive relationships between the non-dimensional pile deflection and surcharge magnitudes. For the case of \(\beta\) = \(-10^{\circ}\), the non-dimensional pile head deflection increases from 0.005 to 0.022, 0.099, and 0.169, while the surcharge load varies from 34.375 to 84.375, 134.375, and 184.375 kPa, respectively. Again, some type-I abnormal points for the case of \(\beta\) = \(-20^{\circ}\) during the loading (Figure 14a) are observed as a result of the same aforementioned reason.  
Moreover, instead of a continuing decrease in non-dimensional pile deflections, the non-dimensional pile deflection maintains zero below the non-dimensional depth of \(z_{\alpha}\) = 1.25 in the cases of \(p\) = 34.375 and 84.375 kPa. The decrease in these two cases can be explained by the possible occurrence of negative non-dimensional pile deflection, indicating that the pile may be rotating about the point of \(z_{\alpha}\) = 1.25. In this scenario, the data below the non-dimensional depth of \(z_{\alpha}\) = 1.25 are displayed as zero and are considered type-II abnormal points. In addition, the deflections at the pile tip in the cases of \(p\) = 134.375 and 184.375 kPa are not equal to zero, indicating that both translation and rotation would occur under large surcharge loads.
## 4 Techniques Used in Academia to Improve Performance Metrics
### Frequency Diversity
#### 4.1.3 Multipath Routing  
Another technique to increase the reliability of the system is multipath routing. There are two interpretations of multipath routing [35]. The first is to construct multiple paths for each node to reach a particular destination in the network. In this approach, multiple next hops are specified at any particular node to forward the packet. If communication between a node and its next hop is disrupted due to interference, an alternative path can be used to transport the data. In [36], [PERSON] _et al_. define a mechanism that constructs multiple paths, whereby one is used as a primary and the second as an alternative path.  
In the second interpretation, several paths are defined from source to destination and the source sends the same packet through each of them. This approach requires a large amount of network resources. In order to use the network resources more efficiently, a mechanism is introduced in [37] that splits the data packet into \(n\) subpackets, _i.e_., by using erasure or FEC codes, and then transmits it instead of the whole packet. In the destination, only \(k\) subnets (\(k<n\)) are required to reconstruct the original information. ReInForM [38] simultaneously sends redundant copies of a packet along multiplepaths in order to increase the end-to-end probability of data delivery. The number of paths and the degree of redundancy are determined based on channel error rate and desired reliability. Reference [39] uses multiple paths in order to deliver packets to the sink for QoS provisioning, in terms of reliability and delay.
## 4 Excitation Characteristics of Mistuned Stators and Its Effect in Reducing Excitation Amplitude
### Sectional Non-Uniform Mistuned Stators and Their Effect on Excitation towards Bladed Disks
Referring to the resonance conditions in Table 1, with mistuned stator case III, no new component corresponding to resonance condition was created. Additionally, with \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Excitation** & **Tuned** & **Mistuned stator Case III** & **Mistuned stator Case IV** \\
**Order** & **Tuned** & **IGV = 12(3/4,5\({}^{\circ}\))** & **IGV = 12(6/2,5 mm)** \\ \hline \hline \(EO=24\) & 1313/Pa & 529/Pa & 925/Pa \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the excitation force amplitudes of three stator structures. mistuned stator case IV, \(EO=30\) and \(n=8\), corresponding to the twelve-order modal resonance of bladed disks appearing. For the Rotor67 bladed disk in this study, if the number of stator blades was set as \(N_{s}=12\), the sectional non-uniform mistuned stator case III: \(\text{IGV}=12(3/4,5^{\circ})\) could significantly reduce the amplitude of excitation force at the resonance point without introducing a new resonance frequency.
## 2 System Model and Atmospheric Error Analysis
### Signal Model of the GNSS-BSAR System
The target echo can be expressed as:  
\[S_{r1}(t) =S_{r}(t)\times D\bigg{[}t-\frac{R_{d}(t)}{c}\bigg{]}\times\exp[ -j2\pi(f_{c}+f_{dc})t] \tag{4}\] \[S(t)=\sum_{n=0}^{N}\text{rect}\Big{(}\frac{t-nT_{p}}{T_{p}}\Big{)}C\big{(}t-nT _{p}\big{)}D(t)\exp(j2\pi f_{c}t) \tag{1}\]0 \[S(t)=\sum_{n=0}^{N}\text{rect}\Big{(}\frac{t-nT_{p}}{T_{p}}\Big{)}C\big{(}t-nT _{p}\big{)}D(t)\exp(j2\pi f_{c}t) \tag{1}\]1  
where \(f_{dc}=-\frac{1}{\lambda}\left.\frac{dR_{d}(t)}{dt}\right|_{t=l_{c}}\) represents the Doppler center frequency of the satellite relative to the receiver, and \(l_{c}\) represents the center time of radar observation. \(R_{d}(t)\) can be calculated by the positions of the receiver and satellite. \(\lambda\) represents the wavelength. Figure 3 shows the process of two-dimensional echo rearrangement. Clearly, the signal of the imaging channel always lags that of the reference channel, so there is a misalignment between the starting edge both ranging code and data code. Take the situation where the data code has a sign flip between the \((n-1)\)-th and \(n\)-th ranging codes in reference channel as an example. For the \((n-1)\)-th ranging code of the imaging channel, some of the signals located on both sides of the dotted line in Figure 3, resulting in a phase jump of \(\pm\pi\). However, for ground-based receiving end, the signal delay of the target echo relative to the reference channel is generally shorter. And the chip length of the data code is 20 ms. So, during multi-pulse long-time integration, the loss of SNR due to this phase jump can generally be ignored. Let \(\tau=t-nT_{p}\) in Equation (4), the two-dimensional echo matrix can be obtained:  
\[S(t)=\sum_{n=0}^{N}\text{rect}\Big{(}\frac{t-nT_{p}}{T_{p}}\Big{)}C\big{(}t-nT _{p}\big{)}D(t)\exp(j2\pi f_{c}t) \tag{1}\]2where \(t\in\left[0,NT_{p}\right]\) is called slow time or azimuth time, \(\tau\in\left[0,T_{p}\right]\) is called fast time or range time. Since the Doppler center frequency caused by satellite motion has been eliminated, the \"stop-go\" model was used in the equation, and the Doppler phase modulation within the equivalent pulse was ignored. As shown in Figure 3, with the nth pulse starting edge of the reference channel as a reference, the data with length \(T_{p}\) in the imaging channel are read as the nth row of the two-dimensional echo matrix. The data in the nth row are a combination of the second part of the \((n-1)\)-th pulse and the first part of the nth pulse. Due to the periodicity of the ranging code, when pulse compression of the ranging code is finished in the frequency domain, the peak value will appear correctly at the starting edge of the nth pulse.
# Casualty Analysis of the Drivers in Traffic Accidents in Turkey: A CHAID Decision Tree Model
## 2 Literature Review
Turning was also obtained as a factor increasing the possibility of serious accidents in windy/foggy conditions [19]. In a study conducted in Palestine, CHAID and CART decision trees were used to determine the severity of pedestrian accidents. The study found that pavement type (paved, unpaved), land use (residential, commercial, or educational), light conditions (daylight or nighttime), and driver gender did not have much effect on pedestrian accident severity. On the other hand, pedestrian gender, area classification (urban, rural), and pedestrian age category (>65, <15) were obtained as the most important factors [20]. [PERSON] et al. conducted a study by analysing screening data for 345 drivers in terms of their cognitive and personality measures, driving behaviours, and attitudes along with fitness levels to drive. The study put forward the related sociodemographic and psychological profiles of the drivers. The researchers established models to predict the probability of being included in motor vehicle crashes (MVC) with regard to important factors such as the personality trait of disconstraint and motor skills by using the Classification and Regression Tree method (CART) [21]. A study carried out in Norway through principal component analysis with iteration and varimax rotation investigated the effects of risk behaviours and the influence of attitude on accidents. The study stated that engaging in risky behaviours and attitudes in traffic were related to age and gender, violating rules, and excessive speed, which had an effect on traffic accidents and near-accidents [22]. Table 1 summarizes the factors affecting accident severity and the methods used in the literature. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Author** & **Significant Attributes** & **Analytical Methods** \\ \hline [PERSON] and [PERSON] [3] & Vehicle type, collision type, driver-vehicle-ped, action, & \\  & contributing circumstance & \\ [PERSON] and [PERSON] [4] & Cause of crash, seatbelt, road surface, location time, & \\ [PERSON] and [PERSON] [4] & collision type, weather cond., lighting cond., occurrence, & \\  & gender, shoulder type & \\ [PERSON] et al. [5]. & Road type, crash type, PTW type, lighting, involved cars & CT, ARM \\ [PERSON] and [PERSON] [6] & Collision type, vehicle type, fixed objects, road division type, & MNL, RF \\  & speed limit, age of the person, vehicle age & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of related work. As explained, there are several studies in the literature regarding the wide range of factors affecting the severity of the accidents. This study, on the other hand, made a noteworthy contribution to the literature by developing prediction models for automobile-related accidents only through analyzing the characteristics of the drivers, as given in Tables 2 and 3, and their effects on the severity level of the accidents.
# Genetic Algorithm-Based Optimization of Clustering Algorithms for the Healthy Aging Dataset
## 2 Materials and Methods
### Clustering Algorithms
#### 2.4.4 Agglomerative  
Agglomerative clustering is also a hierarchical clustering method that repeatedly combines the nearest pairs of clusters to create a hierarchy of clusters. The process begins by considering each data point as an individual cluster and then calculates the distance or similarity between every pair of clusters. The algorithm subsequently combines the two clusters that are closest to each other, forming a unified cluster, and adjusts the distance matrix accordingly to represent the updated clustering structure. This procedure is iterated until there is only one cluster left, or until a predetermined condition for stopping is satisfied. The outcome is a dendrogram, in which the terminal nodes symbolize the individual data points, while the internal nodes symbolize groups at various levels of the hierarchy. Agglomerative clustering is a method that does not necessitate the pre-specification of the number of clusters. It can identify clusters of different forms and sizes within the data.
# Impact of Audio Delay and Quality in Network Music Performance +
## 5 Evaluation Results  
We carried out NMP sessions with 22 musicians, organized into 11 duets. They performed using a diverse range of instruments, such as piano, acoustic and electric guitar, electric bass, violin, and flute, as well as traditional instruments such as the lute, toumberleki, santouri, and oud, in a variety of genres, including folk, rock and classical. Table 3 presents the musical genre selected by each duet, followed by the instruments played by the musicians. It is worth noting that the musical pieces were chosen by the musicians, allowing their performances to be aligned with their preferences and expertise.  
The remainder of this section includes a representative selection of result graphs from both scenarios. Each figure shows circular dots representing the answers for each variable against the controlled variable in each case (delay for Scenario A and quality for Scenario B); the size of these dots is proportional to the number of answers we received for each value, with larger circles indicating more responses. In addition, we show the linear regression results (using the ordinary least squares method) with a blue line in each graph and a grayarea indicating the 95% confidence intervals for the predicted mean, with the adjusted R\({}^{2}\) given in the caption. The regression is performed by treating Likert scores as numeric values (1-5), which is a common practice in QoE studies; although, strictly speaking, these values are not ordinal, hence the need to show how the values are scattered using the size of the dots. Unless otherwise indicated, each graph reflects the results from all participating musicians. The graphs were created by RStudio Desktop, version 2023.03, by Posit PBC (free downloadable version), including the calculation of the linear regression lines and confidence intervals.
## 3 PS-DHI Applications  
As described in the previous section, PS-DHI can measure the parameters \(\beta\) and \(\Delta\varphi\), thus allows to retrieve the SoP of a beam that interacts with a specimen. The modification of the SoP in the transmittedor reflected beam gives information about the structure, the composition, or the optical properties of the specimen under study. Basically, the following two physical properties of the matter can alter the polarization state of a wave [18]:  
* Birefringence--a material is considered birefringent if its refractive index depends on the polarization and propagation direction of the incoming light, i.e., it shows an optical anisotropy. When these samples are crossed by a polarized light, the amplitudes are unchanged but a modification in the relative phase occurs. Birefringence can be linear (that is, there is one axis of symmetry, called the optic axis) such as in optical wave plates/retarders and many crystals, or circular (that is, in which for an incident linearly polarized light, the corresponding outgoing polarization plane will be rotated) such as chiral fluids.
* Dichroism--a material, typically crystalline, is considered dichroic if it absorbs more light along a preferential incident plane of polarization than another plane (absorption anisotropy); as a result, when the optical beam propagates within this material, its polarization state undergoes a modification. The ratio of amplitudes of the orthogonal components of the light emerging from the sample under test provides a measurement of its linear dichroism property.  
The study of the polarization state covers different applications and research fields, such as measurement of stress, geology, chemistry, display technologies, medicine and medical diagnosis, etc. Currently, it has been demonstrated that the PS-DHI technique can be used for noninvasive quantitative imaging of live cells or the evaluation of the dynamic phase difference induced by the birefringence of liquid crystals. In the following, a state of the art of the PS-DHI applications is reported in two subsections, dividing the biological from microelectronics and micro photonics applications.
## 3 Case Study
### Flood Control Operation Dataset for the Rizhao Reservoir  
Based on the historical operation data, 44 floods in 1970, 1974, 1975, 1976, 1998, and 2001-2021 were used as operation data, all of which required discharge by the spillway shuices. As the Rizhao Reservoir has high regulation performance and ensures downstream safety, the flood volume plays a major role in flood operation. In contrast, according to the climate characteristics of the reservoir basin, precipitation is unevenly distributed throughout the year. The flood season lasts from June to September, during which approximately 80% of annual precipitation is received; approximately 60% of annual precipitation is received in July and August alone. The non-flood season lasts from November to April of the following year, during which little precipitation is received. Considering the above characteristics, the flood occurrence time, reservoir water level, cumulative net rainfall, and discharge were taken as the attributes in the flood control operation dataset; the first three of these attributes were defined as conditional attributes and the last attribute was defined as the decision attribute.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Flood Occurrence Time**} & \multicolumn{4}{c}{**Classification/mm**} \\  & **Grade 1** & **Grade 2** & **Grade 3** & **Grade 4** \\ \hline June and September & [0,170) & [170,275) & [275,340) & \(\geq\)340 \\ July to August & [0,153) & [153,248) & [248,306) & \(\geq\)306 \\ October to May of the next year & [0,191) & [191,310) & [310,382) & \(\geq\)382 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification of cumulative net rainfall.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Grade**} & \multirow{2}{*}{**Flood Occurrence Time**} & **Initial Water** & **Cumulative Net** & **Discharge/m3/s** \\  & & **Level/m** & **Rainfall/mm** & **Discharge/m3/s** \\ \hline Grade 1 & June and September & \(\leq\)Z\({}_{\text{limit}}\) & & \(\leq\)100 \\ Grade 2 & July to August & (Z\({}_{\text{limit}}\)42.91) & & As show in & (100,1000) \\ Grade 3 & October to May of the next year & \(>\)42.91 & Table 2 & (1000,2000) \\ Grade 4 & & & & (2000,2500) \\ Grade 5 & & & \(>\)2500 \\ \hline \hline \end{tabular} Note: Z\({}_{\text{limit}}\) represents the flood limited water level.  
\end{table}
Table 3: Classification of all attributes.  
Figure 2: DT-based operation rules.
## 1 Introduction
### Problem Definition  
In a given crowd sensing environment with an opportunistic transmission paradigm, there are three categories of mobile users. \(M_{u1}\) is set of mobile users who can support very sensitive applications that requires the highest quality level of QOI. \(M_{u2}\) is the set of mobile users who can support medium range applications, in which middle levels of QOI must be satisfied. \(M_{u3}\) is the set of mobile users, who can support smaller range applications in which the QOI level is expected to be in the range between medium and lower levels of QOI. In each of these categorizations, it is designed and considered that the more effective parameters are reliability based on distance \(R_{bld}\), transmission range to sink \(T_{ri}\), time that requires to send to sink/destination \(T_{rt}\), and quality of sensor devices used in mobile devices \(T_{rt}\).
# Integrated Precise Orbit Determination of Multi-Gnss and Large LEO Constellations
## 3 Integrated POD Method and Strategy
### Integrated POD Method
\begin{array}{c}X^{s}=\left[O_{0}^{G},O_{0}^{R},O_{0}^{E},O_{0}^{C}, \overline{\delta}^{R},\overline{\delta}^{E},\overline{\delta}^{E},\overline{ \delta}^{C}\right]^{T}\\ X_{g}=\left[\overline{\delta}t_{g},Z_{g},\overline{N}_{g}^{G},\overline{N}_{g }^{E},\overline{N}_{g}^{E},\overline{N}_{g}^{C},ISB_{g}^{E},ISB_{g}^{C},IFB_{ g}^{R_{g}}\right]^{T}\\ X_{l\omega}=\left[O_{l\omega,0},\overline{\delta}_{l\omega},\overline{N}_{l \omega}^{G},\overline{N}_{l\omega}^{R},\overline{N}_{l\omega}^{E},\overline{ N}_{l\omega}^{C},ISB_{l\omega}^{E},ISB_{l\omega}^{E},ISB_{l\omega}^{R_{l \omega}},IFB_{l\omega}^{R_{l\omega}}\right]^{T}\end{array} \right. \tag{7}\]  
where superscript G, R, E, and C represent GPS, GLONASS, Galileo, and BDS satellites respectively.