esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Complexity Analysis of the VVC Decoder
### Complexity Analysis of the Inter Prediction  
In this section, the detailed profiling of inter prediction is presented using all the sequences with the RA sequences over the HGPP and EGPP. In the decoder profiling, timestamps were placed before and after the following inter prediction modules: inter texture (ITEX), sub-prediction unit MC (SPUM), sub-prediction unit bio (SPUB), unidirectional prediction (UDP), decoder side motion vector refinement (DMVR), weighted prediction (WP). In turn, other (OT) part was calculated as the difference between the global EP measurement and the sum obtained from previous modules.  
Table 5 represents the average consumed time distribution (in %) for different parts of the inter prediction over the HGPP and EGPP. The DMVR and UDP were the most computationally heavy modules in the inter prediction for both GPPs. Both DMVR and UDP together accounted for more than average 58% of the total time required for the inter prediction. The other modules consumed amounts individually ranging from 1% to 28% of the total time. The total average time distribution presents a broader view of the inter prediction profiles.  
A detailed view of the inter prediction over the HGPP is shown in Figure 7, which shows the average time distribution for different parts of the inter prediction (in %) over the HGPP with QP 22, 27, 32 and 37. It was found that with the change in QP from 22 to 37, the average computational load of the DMVR increased from 19% to 56%, and the average computational load of the UDP decreased from 24% to 13%. The average computational load for the other inter prediction parts varied by 1% to 11% for different QP values.  
The inter prediction profiling results over the EGPP are shown in Figure 8, where the average time distribution for the different parts of the inter prediction (in %) with QP 22 to 37 is presented. Here, the DMVR and UDP functions followed the same pattern as the profiling of the inter prediction over the HGPP. The average computational load of the DMVR increased by 35%, and the average computational load of the UDP decreased by 13% with the change in QP from 22 to 37. However, the DMVR represents approximately 5% more average computational load over the EGPP than over the HGPP, while the average computational loads for the other functions were slightly lower, except for the SPUM function.. Finally, considering this fine-grained analysis, the EP profiling results show that the DMVR and UDP are the most interesting functions to consider for acceleration, regardless of which platform is targeted.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **ITEX** & **SPUM** & **SPUB** & **UDP** & **DMVR** & **WP** & **OT** \\ \hline HGPP & 8.2 & 4.8 & 5.9 & 18.2 & 40.1 & 6.1 & 16.7 \\ EGPP & 5.5 & 4.9 & 6.9 & 17.4 & 45.1 & 5.9 & 14.3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Average processing times (in %) for each inter prediction block profiled over the HGPP and EGPP.  
Figure 7: Average time distribution for different parts of the inter prediction (in %) over the HGPP.
## 4 Results and Discussion  
### Prediction of the Monthly Electricity Demand in Zambia  
Figure 8 shows the time series of electricity demand for the period of 2006-2019, indicating clear seasonality in the dataset. First, the log transformation was performed to stabilize the slight increase in the variance with the level. The Box-Jenkins time series procedure explained in Figure 4 was applied to the time series data to find the best SARIMA model, considering four criteria of Akaike criterion, Ljung-Box statistics (Qc), maximum likelihood, and standard error. The SARIMA model was first fitted to the first N-12 values after the appropriate transformation of the raw data. As can be observed from Figure 9, both seasonal and non-seasonal differenced data have significant spikes at lags 12, 24, and 36 in the partial autocorrelation function (PACF), which suggests a seasonal order of 3 and a non-seasonal order of 2 (the value of \(p\) and P in Equation (1)). Similarly, the autocorrelation function (ACF) analysis reveals one spike at lag 12 for seasonal and 2 spikes at the same lag for non-seasonal.  
Based on the above discussion, the suggested best SARIMA models are given in Table 6.  
Figure 8: Monthly electricity demand time series decomposition.  
Figure 9: ACF and PACF plots of seasonal and non-seasonal differenced demand.  
To validate the model, the ACF and PACF of the residual portmanteau test and the distribution of errors were checked using the Ljung-Box. The spikes should be within the significant limits, so they appear as white noise (as shown in Figure 10). The chosen SARIMA model rejected a null hypothesis on the independent distribution of errors. The residuals ACF and PACF of the selected SAMIRA model are within the 95% confidence intervals. The \(p\)-value was greater than 0.05 (\(p\)-value = 0.66). Other statistical methods were calculated, such as the mean absolute percentage error (MAPE) obtaining a value of 2.75% with an R\({}^{2}\) at 0.95, confirming the high accuracy of the predicted model.  
The developed SARIMA model was used to predict the future electricity demand in Zambia, which is shown in Figure 11.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**MODEL** & **AIC** & **BIC** & **LOG-LIKELIHOOD** & **RMSE** & **MAPE** & **MAE** & **AICc** \\ \hline ARIMA (2,1,2) (3,1,1) & \(-459.04\) & \(-432.38\) & 238.52 & 38,187.99 & 2.751 & 26,963.92 & \(-457.69\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Selection of ARIMA model for Zambia’s electricity demand.  
Figure 10: ACF and PACF of the residual.
## 2 Materials and Methods
### Type-Road Tangential Force Model  
In the literature, we can find many models used in simulation studies of the dynamics of motor vehicles, describing the unit tangential force \(\mu\) (ratio of the tangential force T to the normal reaction R) in the tyre-road contact. They differ in the area of application and the way of mapping contact forces. There are simple physical models as well as very complex models reflecting the actual structure of the tire and the surface cooperating with it [26; 53; 54; 55; 56]. An interesting proposal can be found in the mentioned work [37] where the authors present an integrated tire-vehicle model to evaluate vehicle braking performance based on [PERSON]'s friction theory with a tyre hydroplaning finite element (FE) model, and a vehicle dynamic analysis. The use of the FE method for tire modeling can also be found, for example, in [57; 58] (the impact of thermal phenomena is considered here). The approach presented in [59] is also interesting, where, based on the third LuGree model, a fuzzy model for determining the adhesion coefficients is proposed. However, the most often, they are the so-called semi-empirical models such as Magic Formula [60], the model of [PERSON], [PERSON], [PERSON] [61], the TM-Easy model [62] or UniTire [63]. They vary in the degree of complexity. The author's modification of the Burckhardt model [64; 65] is used in the study presented here. The advantage of the classic Burckhardt model is a simple analytical form with a reasonable degree of accuracy and ease of modification [66]. Based on this model, a modified version was created better to reflect the unit tangential force in the tyre-road contact, depending on the normal force point and wheel axle velocity, and to adjust its characteristics to the experimental test results. The segment of [PERSON]'s original formula containing the \(c_{4}\) (see [64; 65]) has been replaced with new coefficients. The unit longitudinal tangential force in contact of the wheel with the road surface is expressed as:  
\[\mu(s)\ =\ \big{[}c_{1}\big{(}1-e^{-s\cdot c_{2}}\big{)}-s\cdot c_{3}\cdot G_{ p}\big{]}\cdot G_{s}\cdot\Big{(}1-c_{5}\cdot F_{z}^{2}\Big{)} \tag{20}\]  
The following formulas define the \(G_{p}\) and \(G_{s}\) coefficients:  
\[G_{p}\ =\ \frac{e^{-c_{p3}\cdot v}}{c_{p2}} \tag{21}\]  
\[G_{s}\ =\ c_{p1}\cdot v-0.5\cdot arctan\big{(}-c_{p4}\cdot s\cdot v\big{)}+1 \tag{22}\]  
where: \(c_{1},c_{2},c_{3}\)--set of coefficients depending on the road surface and the tyre (see [64; 65]), \(c_{5}\)--the additional coefficient for the dependence of the unit tangential force on the normal force \(R\), \(c_{p1},c_{p2},c_{p3},c_{p4}\)--set of coefficients for the dependence on the vehicle speed, \(s\)--tyre slip ratio, \(v\)--vehicle speed.  
The tyre slip ratio results from the wheel kinematic state (Figure 3). For the \(i\)-th wheel, it can be expressed as:  
\[s_{i}\ =\ \frac{v_{s}-v_{ki}}{v_{s}}\,\ \ v_{ki}\ =\ \omega_{ki}\cdot r_{di} \tag{23}\]  
The tangential force at the point of contact of the wheel of the \(i\)-th axis with the road surface can be written as:  
\[T_{i}\ =\ \mu(s_{i})\cdot R_{i} \tag{24}\]
## 3 Results and Discussion
### Effect of HP Treatment on Shelf Life of Meat Products  
The shelf life was defined based on both the microbiological growth of LAB and the sensory deterioration of the samples. The time in which the microbial counts reached 10\({}^{8}\) CFU/g or when the sensory scoring was lower than 5 defined the shelf life of the products. For HP treated Bratwurst sausages, the shelf life was estimated as 113, 51, 28, and nine days for storage at 0, 5, 10 and 15 \({}^{\circ}\)C, respectively. At all temperatures, the HP treated samples showed more than a two-fold shelf life increase compared to samples with nitrites (Table 5). For HP treated (600 MPa) sliced cooked ham, the shelf life was estimated as 286, 187, 45, and 30 days for storage at 0, 5, 10, and 15 \({}^{\circ}\)C, respectively (Table 5). The most pronounced shelf life increase was observed for the extremely perishable ham cubes. For ham cubes stored at 5 \({}^{\circ}\)C, the shelf life of HP treated samples is more than two months, compared to the corresponding value obtained for control samples that is less than 10 days. In all temperatures, the HP-treated samples' shelf life increased by two-fold up to 10-fold compared to the shelf life of control samples. This demonstrates the effectiveness of HP in pack cold pasteurization for the shelf-life extension of meat products.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Meat Products Organileptic Determination Rates**} \\ \cline{3-10}
**Storage** & \multicolumn{3}{c}{**Sliced Cooked Ham**} & \multicolumn{3}{c}{**Sliced Cooked Turkey**} & \multicolumn{3}{c}{**Cooked Ham Cobos**} & \multicolumn{3}{c}{**Beatwurst Sausages**} \\ \cline{2-10}
**Temperature** & **Control** & **HP** & **HP** & **Control** & **HP 600 MPa** & **Control** & **HP 600 MPa** & **Control** & **Nitirite** & **HP 600 MPa** \\ \hline
0 \({}^{\circ}\)C & 0.116 \(\pm\) 0.007 & 0.080 \(\pm\) 0.011 & 0.018 \(\pm\) 0.0
# Numerical and Experimental Investigations of a Micromixer with Chicane Mixing Geometry
## 3 Results and Discussion
### Mixing Index  
In order to investigate a mixing process in detail, the mixing index should be analyzed. In Figure 8, the dependency of the Reynolds number and the average mixing index within the outlet plane is indicated.  
The lowest mixing index value is observed by \(\mathrm{Re}=13\). This value divides the whole diagram into two parts, which indicates different flow regimes: stratified and vortex. Within the stratified flow regime, the mixing process obeys Fick's law and depends on the mixing time. The mixing quality shrinks to 1 as the volume flow rate is reduced due to a squaring of the mixing time. If the volume flow rate is between Q = 0.5 mL/min and Q = 1 mL/min, the decreasing residence time is compensated by the increasing convective influence on the mixing. Consequently, the flat may be observed with the value IM = 0.15. It can be assumed that the convective mixing term in the micromiker plays a significant role, starting from Q = 1 mL/min when its role exceeds the diffusive term. As a result, the degree of mixing grows for an increasing volume flow rate.  
Figure 7: Simulated velocity magnitude field and normalized velocity vectors: (**a**) \(\mathrm{Re}=2.6\); (**b**) \(\mathrm{Re}=156\); and (**c**) \(\mathrm{Re}=259\).  
To understand the entire mixing process, the micromiker was geometrically divided into six different zones: the Y-mixer, four meander elements and the outlet channel. These zones are shown with borders as vertical dashed lines in Figure 9, and for each border the mixing degree was calculated for both stratified (Q = 0.1 mL/min) and vortex flow (Q = 10 mL/min). The results obtained show how the mixing changed along the micromiker for Q = 0.1 mL/min and Q = 10 mL/min. In the Y-mixer, there is no significant difference, but in the cells the degree of mixing increases significantly and reaches the value of IM = 0.8 for vortex flow. This indicates that the influence of the meander elements on mixing is actually only under the vortex flow regime. For the stratified flow the increase in mixing index is linear depending on the mixing time and reaches 0.2 at the outlet. At the same time the vortex flow allows for a mixing index of 0.8 for Q = 10 mL/min. Moreover, the mixing time is 100 times smaller in comparison with Q = 0.1 mL/min. This indicates the greater effectiveness of the vortex flow in comparison with the stratified flow.  
Figure 8: Variations of the mixing index with the volume flow rate.  
Figure 9: Mixing index along length of the micromiker.
# Fiber Bragg Grating-Based Optical Signal Processing: Review and Survey
## 2 Review of Design Methods for Fiber Bragg Gratings
### Approximate Methods
#### 2.1.2 Space-to-Frequency-to-Time Mapping  
Another approach for the design of FBG-based optical signal processors based on the Born approximation was later presented, leveraging the space-to-frequency-to-time mapping [39].  
This technique consists of designing the apodization function of a linearly chirped FBG, working in the weak-coupling regime (i.e., \(|\kappa|L\ll 1\)). If the grating's chirp induced dispersion \(\tilde{\Phi}(\text{s}^{2})\), defined as \(\tilde{\Phi}=\partial^{2}\phi_{R}(t)/\partial t^{2}\), accomplishes that \(\tilde{\Phi}\gg\Delta t_{1}{}^{2}/8\pi\), with \(\Delta t_{1}\) being the temporal duration of the impulse response for the optical signal processor, a time-to-frequency mapping occurs. Then, the amplitude of the impulse response in reflection, \(h_{R}(t)\), is proportional to the amplitude of the reflective field coefficient of the filter, \(H_{R}(f)\). Additionally, by operating under the Born approximation, there is a space-to-frequency mapping process, in which the magnitude of \(H_{R}(f)\) is approximately proportional to \(\Delta n(z)\).  
Consequently, in this situation, the amplitude of the grating apodization profile directly determines the magnitude of both the grating's impulse response and the spectral transfer function. A waveform proportional to the target temporal-or spectral- response only needs to be spatially \"recorded\" in the apodization mask to be employed in the grating writing process. Since it works under the Born approximation, this grating design technique presents the same limitation in energy efficiency (related to the required low reflectivity) as the previous one here described. Besides, the use of a linearly chirped FBG imposes a quadratic phase in the reflective spectral response, limiting the applicability of this approach to amplitude-only optical signal processors. On the other hand, the processing bandwidth is restrained to tens of GHz, limited by the physically attainable fiber-grating length, which is typically shorter than \(\sim\)40 cm.  
The general design of FBGs as optical signal processors, including high-reflectivity gratings (leading to increased energy efficiency), involves the use of more exact analysis and synthesis algorithms, which are described in what follows.
# Hybrid Particle Swarm Optimization Genetic LQR Controller for Active Suspension
## 2 System Model  
### Random Road Input Model  
The car will generate vibration when driving on rough roads; thus, the suspension systems are designed to stabilize the body movement and improve the performance of the vehicle. In order to investigate the active suspension and control strategy, the input road model variables of the suspension system should be considered at first. The common road model in actual vehicle traffic can be divided into two categories: the random model and impact model.  
Random uneven pavement is the most common road model in vehicle dynamics simulations, and also a common pavement in actual vehicle driving. Uneven pavement can be classified into several classes according to the roughness and unevenness of the pavement. According to the international standard ISO/TC108/SC2N67, the power spectral density function can be used to fit the simulation of pavement unevenness.  
In this paper, the integration of white noise signals is used as the random road input model. The expression of the random road input model [30] is shown as:  
\[q(t)=2\pi f_{0}q(i)+2\pi w(t)\sqrt{G_{0}v} \tag{1}\]  
where \(q(t)\) is the road displacement; \(w(t)\) is the random white noise signal; \(
# Resource Allocation for GBR Services in D2D-Enabled Communication
## 5 Conclusions  
We proposed an efficient resource allocation scheme for GBR services in D2D-enabled communications, with the aim of minimizing the total channel bandwidth while guaranteeing the target data rate. This is a joint optimization problem of transmit power, channel sharing, channel bandwidth, and 3-D matching of the CU-DU-channel. To mitigate the computational complexity of the 3-D matching problem, Lagrangian relaxation was used, and two 2-D problems were solved iteratively with a sub-gradient-based bounding procedure.  
Through intensive simulations, the proposed scheme was found to guarantee the target rate with the minimum channel bandwidth, unlike the sum rate-maximization scheme. When each channel is pre-assigned to a CU, the matching problem is simply 2-D matching of the CU-DU excluding channel, but it requires about a 33% wider average channel bandwidth than the proposed scheme with \(D_{max}=50\) m and 30 CUs. The impacts of the maximum D2D distance, the number of CUs, and the number of DUs were also investigated.  
Since the importance of real-time traffic such as GBR services is expected to grow, a resource allocation scheme with a rate-guarantee constraint is inevitablely required for future cellular communications. This work can be extended to a mixture of traffics with different QoS requirements and to V2X environments.  
**Author Contributions:** Conceptualization, J.H.P., D.K.K.; formal analysis, J.H.P., D.K., D.K.K.; data curation, J.H.P.; validation, J.H.P., D.K., D.K.K; supervision, D.K.K.; writing--original draft preparation, J.H.P.; writing--review and editing, J.H.P., D.K.K.; visualizations, J.H.P; project administration, D.K.K.; funding acquisition, D.K.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by Institute for Information and Communications Technology Promotion (IITP) grant funded by the Korean government (MSIT) (No. 2017-0-00316, Development of Fundamental Technologies for the Next Generation Public Safety Communications).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Evaluating MPTCP Congestion Control Algorithms: Implications for Streaming in Open Internet
### MPTCP Framework Explained  
The MPTCP extends the capabilities of the standard TCP by enabling the use of multiple network paths to convey data between communicating parties, as illustrated in Figure 1. When a data transfer request is initiated, the standard TCP handshake process is employed to establish a connection. During this phase, both parties determine if they support a compatible version of the MPTCP protocol [5; 11].  
If the multipath extension is available, the protocol attempts to open multiple transmission channels. A component known as the Path Manager then decides on the number of channels and which network paths to utilize. This multipath approach enhances resilience, as communication can continue even if some channels fail [8; 10].  
The data generated by the application first pass through the Master Controller, which shapes the overall data transfer characteristics [21]. From there, it moves to the Scheduler, which distributes the data among the active paths established by the Path Manager. Each path then employs a Single Path TCP (SPTCP) controller for data transfer [12; 22]. The Scheduler can be adjusted to achieve specific objectives, such as minimizing power consumption or delay [23; 24; 25]. However, it does not directly influence the traffic intensity; it responds to signals from the standard SPTCP, which governs each path's data stream separately.  
The Master Controller, SPTCP controllers, and Scheduler work in a union, interacting in a complex manner to meet the communication objectives. Even though TCP was initially designed to maximize throughput, the choice of SPTCP and MPTCP CC algorithms can shift this focus toward minimizing delay, thereby affecting the quality of a streaming service [6; 7].
## 3 Physical-Layer Network Coding Based on Block Markov Superposition Transmission
### Iterative Decoding and Computing Algorithm
Thus, the channel capacity \(C_{1}\) between node \(N_{1}\) and relay node \(N_{R}\) is given by  
\[C_{1}=\frac{I(\mathbf{x}_{1};\mathbf{y}|\mathbf{h}_{1},\mathbf{h}_{2})+I(\mathbf{x}_{1};\mathbf{y}| \mathbf{x}_{2},\mathbf{h}_{1},\mathbf{h}_{2})}{2}. \tag{12}\]  
Similarly, the channel capacity \(C_{2}\) between node \(N_{2}\) and relay node \(N_{R}\) is given by  
\[C_{2}=\frac{I(\mathbf{x}_{2};\mathbf{y}|\mathbf{h}_{1},\mathbf{h}_{2})+I(\mathbf{x}_{2};\mathbf{y}| \mathbf{x}_{1},\mathbf{h}_{1},\mathbf{h}_{2})}{2}. \tag{13}\]
# Experimental Test of Continuous Wave Frequency Diverse Array Doppler Radar +
## 2 Data Collection I: Far-Field Beampattern
### Angular Scan with Time  
The expression for the time to scan through an angular range is [24]  
\[\Delta t=\frac{(d/\lambda_{0})}{\Delta f}(\sin\theta_{1}-\sin\theta_{2}), \tag{6}\]  
where \(\theta_{1}\) and \(\theta_{2}\) are the angles through which the scan takes place. Figure 4 shows the results of a scan over \(\theta_{1}=-10^{\circ}\), \(\theta_{2}=10^{\circ}\). The expected scan time for this angular spacing with \(\Delta f=12\,\mathrm{kHz}\) is \(|\Delta t|=14.47\,\mathrm{\SIUnitSymbolMicro s}\). The measured average angular scan time of \(15.20\,\mathrm{\SIUnitSymbolMicro s}\) falls within the measured values in [24].  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\Delta f\) (kHz) & **Measured (\(\mathrm{\SIUnitSymbolMicro s}\))** & **Theoretical (\(\mathrm{\SIUnitSymbolMicro s}\))** \\ \hline
10 & 99.33 & 100.00 \\
12 & 83.62 & 83.33 \\
20 & 49.89 & 50.00 \\
50 & 20.04 & 20.00 \\
100 & 9.95 & 10.00 \\
250 & 4.04 & 4.00 \\
500 & 2.00 & 2.00 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hardware bench test of temporal magnitude periodicity.  
Figure 1: General block diagram of the FDA radar setup used in Collection I and Collection II with arrows indicating direction of wave propagation, where blue arrows indicate propagation from the transmit antenna and red arrows indicate propagation towards the receive antenna. Note the left (L) and right (R) probe labels corresponding to the collection shown in Figure 4.  
Figure 2: Photograph of the general setup for Data Collection I.  
Figure 4: Angular scan with time of the FDA far-field pattern generated using an FDA transmit architecture with operational parameters \(f_{0}=2\) GHz, \(\Delta f=12\) kHz, and \(N=3\).  
Figure 3: Overlayed probe captures at \((R_{0},\theta_{0})=(4.57\) m,\(0^{\circ})\) showing temporal periodicity generated by an FDA transmit architecture with operational parameters \(f_{0}=2\) GHz, \(\Delta f=10,20\) kHz, respectively, and \(N=3\).