esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### The Effect of More than Three Factors
#### 4.4.2 The Effect of Factor Quintets  
Lastly, the effect of factor quintets was investigated. As there were five factors, in the end, only one quintet could be examined: Camera FoV & Camera rotation & Display device & Contrast ratio & Shadows. Similarly, regression analysis was used for the investigation. The Intercept variable was 45\({}^{\circ}\) FoV, \(-15^{\circ}\) ROT, DD, 1.5:1 CR, and SH OFF with an estimate of 69.780 s on average and a standard error of 54.784.  
According to the results, 110 significant differences exist among quintets. It can also be seen that there are no significant decreases in completion times. The largest significant increase is when the quintet is 75\({}^{\circ}\) FoV, 45\({}^{\circ}\) ROT, DD, 3:1 CR, and SH ON. In this case, the increase is 420.880 s on average, which is approximately 7 min. If the whole CI is looked at, the increase in completion times can reach approximately 10.5 min. Contrarily, the smallest significant increase is when the quintet is 45\({}^{\circ}\) FoV, 0\({}^{\circ}\) ROT, GVR, 1.5:1 CR, and SH ON. The increase is 112.517 s on average which is almost 2 min. While not significant, there are \(11\) quintets which could decrease the completion times. These can be seen in Figure 5.  
Figure 5: 95% CIs of each factor quintet that can decrease completion times.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Quartet** & **Intercept Levels of Quartets** & **Estimate** & **Standard Error** \\ \hline FoV \& ROT \& Display device \& CR & 45\({}^{\circ}\) FoV \& \(-15^{\circ}\) ROT \& DD \& 1.5:1 CR & 69.780 s & 54.740 s \\ FoV \& ROT \& Display device \& S & 45\({}^{\circ}\) FoV \& \(-15^{\circ}\) ROT \& DD \& SH OFF & 213.313 s & 17.245 s \\ FoV \& ROT \& CR \& S & 45\({}^{\circ}\) FoV \& \(-15^{\circ}\) ROT \& 1.5:1 CR \& SH OFF & 69.780 s & 56.02 s \\ FoV \& Display device \& CR \& S & 45\({}^{\circ}\) FoV \& DD \& 1.5:1 CR \& SH OFF & 125.094 s & 7.686 s \\ ROT \& Display device \& CR \& S & \(-15^{\circ}\) ROT \& DD \& 1.5:1 CR \& SH OFF & 143.453 s & 38.817 s \\ \hline \end{tabular}
\end{table}
Table 5: The investigated quintets, their intercept values, estimates, and standard errors.  
While the quintets in Figure 5 can decrease completion times, they can also slightly increase them. As can be seen, most of these completion times are received using a desktop display, while there is only one with the use of the Gear VR.  
Regarding significant interactions, 14 exist of them. Significant ones only occur among the pairs of FoV & ROT; FoV & CR; and ROT & CR. The most significant interaction is in case of undefined FoV & 30\({}^{\circ}\) camera rotation (\(p\)-value = 6.01 \(\times\) 10\({}^{-5}\)).
# Handover Decisions for Ultra-Dense Networks in Smart Cities: A Survey
## 4 Research Challenges
### Support of High Mobility  
Ultra-high-speed HO mechanisms with almost zero latency are required for high-speed mobility, such as autonomous vehicles and high-speed rail. The motivation is to avoid disconnections. UAVs and drones with different altitudes and velocities further introduce another level of complexity to mobility management [57].
## 3 Results
### Storage Temperature  
Since the loss (fading) of EPR intensity in irradiated samples with the passage of time is affected by the storage of the samples under different temperature [5,27], the temperature dependence of material storage is investigated. The evaluated materials irradiated by 5 Gy photons were grouped. Subsequently, the grouped samples were maintained at three temperatures of \(-\)20 \({}^{\circ}\)C, 4 \({}^{\circ}\)C, and 45 \({}^{\circ}\)C, respectively. EPR recordings were made at three-time points of 1 h, 168 h and 336 h at the same  
Figure 4: Dose response curve of different grain size for the six biological samples.  
Figure 3: Dose response curve for the six biological samples.  
temperature. Representative fading curves for the six materials as a function of storage period up to 14 days are shown in Figure 5. The fading curves show the reduction of 6-25% for the clam shell, 9-15% for the cyclina shell, 2-13% for the tooth, 7-60% for fingernail, 8-38% for chitin, and 9-29% for ox bone. The reduction of fading effect for tooth, fingernail, chitin and ox bone can be considerably reduced at lower temperatures (\(-20\)\({}^{\circ}\)C), but the fading effect for clam shell and cyclina shell shows the opposite results. The fading effect for tooth is relatively small, regardless of the storage temperature range [15]. When storage temperature increases, the signal loss of fingernail, chitin, and ox bone with the passage of time was most severe, conforming to the chemical concepts on reaction kinetics and diffusion mobility of radical species. The background signal in the shells is possibly due to trace manganese ions in the calcium lattice. The opposite trends of the shells may lie in the changes of Mn2+ background signal during the sample preparation between measuring the background EPR signals and measuring the post-exposure EPR signals [28]. It was clearly seen that the fading effect of EPR signal highly depends on the storage temperature and period after irradiation.
# Performance Improvement of an NMP Mini Segway Using Sample and Hold Inputs
## 2 Background: SHIs, DE, and DT Systems  
### SHI and DE System  
A zero-order hold (ZOH) input \(u_{z}(t)\) is defined as  
\[u_{z}(t)=u(t_{k}),\text{ if }t_{k}<t\leq\ t_{k}+\delta,\ k=0,1,2,... \tag{1}\]where \(\delta\) is the sampling period, and \(u(t_{k})\) is the sampled value of \(u(t)\), which is a continuous-time input over the time interval \((t_{k},t_{k}+\delta]\). Similar to ZOH, three additional SHIs were proposed in [12], and they are square pulse sample and hold input (SPSHI), forward triangle sample and hold input (FTSHI), and backward triangle sample and hold input (BTSHI). Note that the BTSHI feasible region (leading to an MP discrete-time system) as a function of sample period and duty-cycle is normally smaller than the other two, and the feasible regions of FTSHI and SPSHI often cover the BTSHI. As a result, the BTSHI provides less freedom in terms of selecting the sampling period and duty-cycle for the later experiment study; therefore, it is not adopted in this study. According to [3], the FTSHI requires a sampling frequency at least three times faster than SPSHI to generate the signal, and the low-cost microcontroller prevents the implementation of the FTSHI signal. As a result, the FTSHI was not used in this paper.  
The SPSHI is defined in the following equation and can also be found in Figure 1, where \(p\in(0,1]\) is the duty cycle of the switched input; \(p\) and \(\delta\) are for SPSHI in this case and are referred to as SHI parameters later.  
\[u_{s}(t)=\left\{\begin{array}{ll}u(t_{k})/p,&t_{k}<t\leq\ t_{k}+p\delta\\ 0,&t_{k}+p\delta<t\leq\ t_{k}+\delta\end{array}\right. \tag{2}\]  
For the continuous time-invariant linear system  
\[\begin{split}\dot{x}(t)&=Ax(t)+Bu(t),\ x(t_{0})=x_{0}\\ y(t)&=Cx(t)\end{split} \tag{3}\]  
with \(u(t)=u_{s}(t)\) and the SPSHI defined in Equation (2), the system becomes  
\[\begin{split}\dot{x}(t)&=\tilde{A}\bar{x}(t)+\bar{B}u _{s}(t),\ \bar{x}(t_{0})=x_{0}\\ \dot{y}(t)&=\tilde{Cx}(t)\end{split} \tag{4}\]  
Systems (3) and (4) are discrete-equivalent (DE) [12] if the state variables of both systems are equivalent at sampling time instants \(t_{k}\) (\(k=0,1,2,...\)). For the rest of the paper, Equation (4) is called the DE system of (3). The DE system matrix with SPSHI has \(\bar{A}=A\) and \(\bar{C}=C\), but the system input matrices \(B\) and \(B\) are  
\[\bar{B}=[I-e^{-\delta A}]^{-1}[I-e^{-\delta pA}]B, \tag{5}\]  
Note that for the DE system, the system matrices \(\bar{A}=A\) and \(\bar{C}=C\). Matrix \(\bar{B}\) depends on the SHI parameter selections. When \(p=1\), matrix \(\bar{B}\) in SPSHI becomes \(B\), and the SPSHI becomes ZOH. Therefore, ZOH is a special case of SPSHI. In order to keep the input energy unchanged, the peak magnitude of SPSHI is scaled by \((1/p)\); see Equation (2). When \(p<1\), the DE system input matrix \(\bar{B}\) is different from the original input matrix \(B\), so the zero locations of the DE system in (4) can be different from those of the original system in (3).  
Figure 1: Signal sampled by SPSHI.
## 6 Conclusions  
* This study aimed to investigate high-production granitic rocks in the Midyan Terrane (northwest Saudi Arabia) through a joint analysis of audio magnetotelluric (AMT) and gravity data. A total number of 80 AMT and 246 gravity stations were measured, analyzed, and interpreted. A special emphasis was given to detect the subsurface structural patterns, detect the thicknesses of the sedimentary cover, identify regional deep and shallow fault systems, and clarify the subsurface geometry/extension of the granitic rocks. The main concluded points of this study can be summarized as follows:
* Geophysical AMT and gravity methods were successfully used to image the subsurface structure of the granitic rocks in the study area. These contributed more to the identification of the subsurface orientation and geometry of these granites, and provided the necessary parameters to enhance the further volumetric analysis of the geothermal potential.
* The area had a good geothermal potentiality, represented mainly by high-radioactive granites (EGS) and hot sedimentary basins.
* High-heat granite can be utilized as a good EGS source for possible energy production upon injecting an energy transfer medium such as CO\({}_{2}\) (supercritical CO\({}_{2}\)). However, more detailed geomechanical measurements are required to understand the mechanical behaviors of these granites.
* Near-shore thick sedimentary basins, with a temperature up to 150 \({}^{\circ}\)C, are a good candidate for conventional geothermal energy (hydrothermal system). Two important  
Figure 8: Final interpretation map showing the most promised zones (blue-filled polygons) for hot sedimentary basins and high-heat granitic rocks (red crossed grid).  
geothermal areas were indicated and mapped; thick sedimentary basins and high-heat granitic rocks.
* Both areas require more exploration activity in order to evaluate the geothermal energy reservoir and estimate its reserves.  
All authors contributed to the literature review, methodology, formal analysis, data interpretation, and writing and reviewing of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Plan for Science, Technology and Innovation (MAARIFAH), King Abdulaziz City for Science and Technology, Kingdom of Saudi Arabia, Award Number 14-ENE704-02.  
Not applicable.  
The authors are grateful to the anonymous reviewers who helped in enhancing the manuscript.  
The authors declare that the research was conducted in the absence of any commercial conflict of interest or financial relationships that could be construed as a potential conflict of interest.
## 4 Spatiotemporal Feature Extraction Techniques
### Feature Extraction Methods Future Research Directions
#### 4.5.1 Optimization of Computational Efficiency and Real-Time Processing  
Research is needed on compression and pruning techniques for neural networks to reduce the computational burden of hybrid models and enhance their real-time processing capabilities [72; 73]. Such intrusion detection techniques are particularly important in network environments with insufficient computing resources [74].
# Evaluation of Multi-Beam Massive MIMO Considering MAC Layer Using IEEE802.11 ac and FDD-LTE
## 4 Computer Simulation  
The results of the computer simulation of the proposed system were verified in terms of the Rayleigh fading and angular spread of quadrature amplitude modulation (QAM) signals [26; 27]. The simulation conditions are listed in Table 1. Figure 4 shows the simulation model, which is a scattering ring model [27] with a specific angular spread. The model applies 101 paths per UT with an angular spread of 1.0 degree. We assume flat fading in narrowband signals and do not consider delay spread. Sixty-four elements are arranged in the horizontal direction at intervals of 0.5 wavelengths, and beam width is approximately 1.60 degrees. As the assumed transmission method involves a single carrier for narrowband signals, delay waves have no influence on signal separation. The signals for each sub-carrier are regarded as narrowband signals to apply them as actual broadband signals through orthogonal frequency division multiplexing. In the UT distribution, the center direction of the BS is set as 0 degrees and individual UTs are placed at random angles within a range of \(-60\) to 60 degrees. A constant distance is set between the BS and all UTs so that a signal-to-noise ratio (SNR) of 20 [dB] per UT is received at the BS.  
Figure 4: Simulation model.  
Figure 3: Relationship between amplitude correlation and signal-to-interference noise ratio (SINR).  
First, the processing of the uplink was simulated transmitting QPSK signals from all 20 UTs to the BS, which recorded the amplitude correlation and received power for each beam. On the downlink, the BS transmitted QPSK 1024-QAM signals to eight users with high reception power. Then, the UTs received signals from the BS calculated bit error rate (BER). We assumed that a modulation scheme yielding a BER of less than \(10^{-2}\) could be applied [28; 29]. We evaluated the applicable modulation schemes in terms of the SINR and received power recorded on the uplink to simulate an adaptive modulation scheme.
## 2 Materials and Methods
### Eddy Covariance-Based Measurements  
A standard open-path eddy covariance (EC) system [52] was implemented at the height of 2.1 m in this site to measure net exchange of CO2 (_NEE_) between atmosphere and vegetation [26]. Due to the occurrence of situations that are inappropriate for EC measurements, including instrument malfunction, rainfall, dew or cobwebs formation, human disturbance, and near-static atmospheric conditions, the EC-based NEE measurements inevitably contain abnormal values [25]. To avoid these potential influences, raw measurements were processed according to the ChinaFLUX data-processing [53; 54], which included despiking, coordinate rotation, air density corrections, outlier rejection, and friction velocity threshold (u*) corrections.  
The processed data were filled by linear interpolation for small gaps (less than 2 h) in one week window. For gaps more than 2 h, two nonlinear empirical models were applied separately for daytime and nighttime data. The daytime CO2 fluxes were estimated using the Michaelis-Menten equation [55; 56].  
\[NEE=-\frac{a\times Amax\times PAR}{a\times PAR+Amax}+Re \tag{1}\]  
where _NEE_ (umol CO2 m\({}^{-2}\) s\({}^{-1}\)) and _Re_ (umol CO2 m\({}^{-2}\) s\({}^{-1}\)) are the day time net ecosystem exchange and ecosystem respiration, respectively. _Amax_ (umol CO2 m\({}^{-2}\) s\({}^{-1}\)) is the maximum ecosystem photosynthesis rate, and \(\alpha\) (umol CO2 umol photons\({}^{-1}\)) is the apparent quantum yield. Both are taken as indicators of plant photosynthetic capacity. The nighttime missing _NEE_ data (_Re_EC_) were filled with the exponential relationship between _Re_ and _Ts_ due to _GPP_ is assumed to be zero at night [57; 58].  
\[Re\_EC=a\times e^{(b\times Ts)} \tag{2}\]  
where _Re_EC_ (umol CO2 m\({}^{-2}\) s\({}^{-1}\)) is nighttime ecosystem respiration. \(a\) (the reference respiration when Ts = 0 \({}^{\circ}\)C) and \(b\) are the regression parameters, which can be used for daytime _Re_ filled. The gap-filled daytime _NEE_ were partitioned into _EC_-based _GPP_ (_GPP_EC_) as CO2 assimilation and _Re_EC_ as CO2 emission during daytime [59].  
\[GPP\_EC=Re\_EC-NEE \tag{3}\]
# Optimal Position and Target Rate for Covert Communication in UAV-Assisted Uplink RSMA Systems
## 2 System Model  
### Communication Scenario  
We consider the covert communication in uplink RSMA system, which consists of a pair of RSMA users (U1 and U2), a BS, and a warden ([PERSON]), as shown in Figure 1. U1 is a UAV deployed as both the communication node and the friendly jammer hovering at the constant altitude \(z_{1}\). [PERSON] wants to transmit covertly detected by [PERSON], who continuously senses whether U2 is transmitting by a radiometer. In order to increase the total transmission rate while protecting [PERSON]'s covert communication, U1 adopts the RS strategy with random transmit power. Without loss of generality, we use a three-dimensional (3D) Cartesian coordinate system to describe locations. Each node is equipped with a single antenna.  
Denote \(\mathbf{q}_{1}=[x_{1},y_{1},z_{1}]\), \(\mathbf{q}_{2}=[x_{2},y_{2},0]\), \(\mathbf{q}_{b}=[0,0,z_{b}]\), and \(\mathbf{q}_{w}=[x_{w},y_{w},0]\) as the coordinate of U1, U2, BS and Willie, respectively. Considering an open area, the communication channel between BS and U2 is modeled as line-of-sight (LOS) links and AWGNchannels. This assumption is based on the fact that in the urban macro, the probability of the LOS path is much higher than that of the non-LOS path when the horizontal distance between BS and terminals is less than 70 m according to 3 GPP specification [19]. In addition, we assume that the channels between UAV and terrestrial nodes are mainly dominated by LOS components and the non-LOS path is negligible (as in e.g., [16; 17; 20]). U2 and Willie are ground users and the channel undergoes the block quasi-static fading, which means that the channel coefficients remain constant in one time slot, and change independently from one time slot to another. The large-scale fading coefficient from node \(i\) to node \(j\) is denoted as \(L_{ij}=\beta_{0}\|\mathbf{q}_{i}-\mathbf{q}_{j}\|^{-\beta}\), where \(i\in\{1,2\}\), \(j\in\{b,w\}\), \(\beta_{0}\) is the fading coefficient at the reference distance of 1 m, \(\beta=2\) is the free space path-loss factor, and \(\|\cdot\|\) denotes the Euclidean norm. And the small-scale fading between U2 and Willie \(r_{2w}\) follows complex Gaussian distribution \(\mathcal{CN}(0,1)\). Therefore, the channel coefficient is denoted as  
\[h_{ij}=\begin{cases}\sqrt{L_{ij}},&ij\in\{1w,1b,2b\},\\ \sqrt{L_{ij}}r_{2w},&ij=2w.\end{cases} \tag{1}\]  
And the channel power gain is expressed by \(g_{ij}=|h_{ij}|^{2}\). Suppose the location information is available for all nodes since [PERSON]'s location can be detected with a radar or camera by U1. This assumption has been also widely adopted in previous research on UAV-assisted covert communication [16; 17; 20]. In addition, we assume that full channel state information (CSI) is available for Willie, while legitimate users only possess statistical CSI between U2 and Willie.
## 2 Materials and Methods
### Determination of Peroxide Values, Protein Carbonyls and Volatiles  
Peroxide values (PVs) were determined according to a ferric thiocyanate method as described by [39]. PV measurement was performed in duplicate from each lipid extract (process replicate). Protein carbonyls were quantified as an indicator of protein oxidation. The widely used protein carbonyl method by [PERSON] et al. [40] was used to quantify protein carbonyls in the pH-shift-processed isolates. Briefly, protein isolates were dissolved in 6 M guanidine hydrochloride, incubated in the dark (RT) for 60 min with 2,4-diphenylhydrazine (DNPH), after which proteins were precipitated using ice-cold trichloroacetic acid. Protein pellet was washed thrice with ethanol:ethyl acetate (1:1) to remove excess DNPH, and after re-solubilization of protein pellet in guanidine-HCl, the absorbance was measured at 374 nm against a blank (protein solution without added DNPH). Protein contents in samples (after precipitation and resolubilization) were quantified by measurement at 283 nm and comparison with a standard curve established by fish protein with known protein concentration.  
Enzymatically produced hydrolyses could not be quantified using the before mentioned protocol, since it involves the precipitation of proteins using trichloroacetic acid, which was impossible for the enzymatically processed samples due to their high degree of hydrolysis and solubility [29]. Protein carbonyls in enzymatically produced hydrolyses were therefore quantified using a simplified method by [PERSON] et al. [41], which does not require protein precipitation and the removal of unbound DNPH, because the absorbance of labeled protein carbonyls is shifted to 450 nm due to the alkaline conditions used in the assay. [PERSON] et al. [41] reported that the simplified method showed high correlation with the original method by [PERSON] et al. [40], but due to the use of different methods the carbonyl results of isolates and hydrolyses were not compared with each other.  
Volatile compounds in protein isolates and hydrolyses were extracted using headspace solid-phase microextraction (HS-SPME) and analyzed using GC-MS. For the analysis, 0.5 \(\pm\) 0.01 g of sample was weighed in 20 mL SPME vials, and 3 mL of water was added.  
In the case of the raw materials, B. herring and roach, 3.0 \(\pm\) 0.025 g of ground fish was weighed, and no water was added. All vials were flushed with nitrogen to limit oxidation during the analysis. HS-SPME-GC-MS analysis was conducted as described previously by our group [37]. In brief, the extraction of volatiles was conducted at 40 \({}^{\circ}\)C for 30 min after incubation at 40 \({}^{\circ}\)C for 20 min, using a DVB/CAR/PDMS-fiber (50/30 \(\upmu\)m film thickness; Supelco, St. Louis, MO, USA). GC-MS analysis consisted of the desorption of volatiles for 6 min at 240 \({}^{\circ}\)C, and their separation using Supelco SPB-624 column (60 m \(\times\) 0.25 mm i.d., 1.4 \(\upmu\)m film thickness; Supelco, St. Louis, MO, USA). MS was operated in electron ionization mode and ions were scanned in the range 50-300 amu.
# Markov-Modulated Poisson Process Modeling for Machine-to-Machine Heterogeneous Traffic
###### Abstract  
Theoretical mathematics is a key evolution factor of artificial intelligence (AI). Nowadays, representing a smart system as a mathematical model helps to analyze any system under development and supports different case studies found in real life. Additionally, the Markov chain has shown itself to be an invaluable tool for decision-making systems, natural language processing, and predictive modeling. In an Internet of Things (IoT), Machine-to-Machine (M2M) traffic necessitates new traffic models due to its unique pattern and different goals. In this context, we have two types of modeling: (1) source traffic modeling, used to design stochastic processes so that they match the behavior of physical quantities of measured data traffic (e.g., video, data, voice), and (2) aggregated traffic modeling, which refers to the process of combining multiple small packets into a single packet in order to reduce the header overhead in the network. In IoT studies, balancing the accuracy of the model while managing a large number of M2M devices is a heavy challenge for academia. One the one hand, source traffic models are more competitive than aggregated traffic models because of their dependability. However, their complexity is expected to make managing the exponential growth of M2M devices difficult. In this paper, we propose to use a Markov-Modulated Poisson Process (MMPP) framework to explore Human-to-Human (H2H) traffic and M2M heterogeneous traffic effects. As a tool for stochastic processes, we employ Markov chains to characterize the coexistence of H2H and M2M traffic. Using the traditional evolved Node B (eNodeB), our simulation results show that the network's service completion rate will suffer significantly. In the worst-case scenario, when an accumulative storm of M2M requests attempts to access the network simultaneously, the degradation reaches 8% as a completion task rate. However, using our \"Coexistence of Heterogeneous traffic Analyzer and Network Architecture for Long term evolution\" (CHANAL) solution, we can achieve a service completion rate of 96%.  
IoT; MMPP; Markov chains; aggregated traffic models +
Footnote †: journal: Applied Sciences