esa-sceva/satcom-chunk-collection huggingface dataset:
# SURROGATES: Virtual OBUs to Foster 5G Vehicular Services
## 1 Introduction
Although recent approaches follow a similar approach in new application areas such as bicycles [7], the research community has recently focused on the expansion of Internet of Things (IoT) in the vehicular domain, in which it is known as the Internet of Vehicles (IoV) [8; 9]. 5G advances such as network function virtualization (NFV), MANO or MEC, together with new 3 GPP radio advances and the hybridization with other vehicular and IoT communication technologies, are now found as useful enablers to cope with the IoV vision and cover data gathering and processing without the need for such clustering solutions. SURROGATES follows this approach to go a step further in IoV and outperforms currently available cloud-based schemes. In this paper, we propose the SURROGATES solution, which aims at virtualizing physical OBUs into VNFs in which computationally-intensive tasks and data caching are performed. The virtual OBU (vOBU) becomes the source of pre-processed information to be used by cloud services or other data analytics modules, which finally feed user applications in the areas of traffic efficiency or even safety. vOBUs are instantiated in the access network domain, implementing an MEC approach, and MANO features are used to scale the platform automatically. Apart from computing performance improvements, the MEC layer with vOBUs offers a proxy point where data requests from services are solved without the need to access the actual OBUs and, hence, consume radio resources of the wireless vehicular network. This is done transparently to the services or users, which only perceive better performance in the access. Additionally, this approach extends the life of battery-powered physical OBUs such as the ones installed in light vehicles [10], since energy-consuming tasks regarding processing and communications are limited. Thereby, the main contributions of this work are the following: (i) the integration of virtualization techniques in vehicular environments in order to improve the system efficiency and adaptability is explored; (ii) an end-to-end architecture based on NFV and MEC paradigms is proposed and analyzed; and (iii) a real implementation and deployment of the aforementioned architecture is presented and the results extracted from a series of validation tests are discussed. The remaining of the paper is organized as follows: Section 2 places the work in the literature and presents the advances beyond the state of the art; Section 3 presents the general architecture of the NFV-powered edge computing solution; Section 4 details the implementation details of the system; Section 5 includes the evaluation of the platform in terms of validation and performance indicators; and, finally, Section 6 concludes the paper and introduces the future research directions.
## 3 Results and Discussion
### Milk
\begin{table}
\begin{tabular}{c c c c c|c c c} \hline \hline \multirow{2}{*}{**Experiment**} & \multicolumn{4}{c|}{**Organic milk**} & \multicolumn{4}{c}{**Conventional milk**} \\ \cline{2-9}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline \multicolumn{9}{c}{_Temperature steps_} \\ \hline Equilibration at 21 \(\,\)C & 0 & 0 & 0 & 90 & 0 & 0 & 0 \\ Equilibration at 40 \(\,\)C & 1 & 1 & 30 & 30 & 5 & 5 & 5 \\ Holding at 21 \(\,\)C & 0 & 60 & 0 & 0 & 0 & 0 & 0 \\ Adsorption at 40 \(\,\)C & 60 & 60 & 60 & 60 & 60 & 30 & 60 \\ Holding at 21 \(\,\)C & 0 & 0 & 0 & 0 & 0 & 30 & 60 \\ \hline \multicolumn{9}{c}{_Compound_} \\ \hline \multicolumn{9}{c}{_Relative abundance (thousands)_} \\ \hline
2-Methylbutanal & 3.4 & 0 & 0 & 0 & 0 & 0 & 19 & 27 \\ Pentanal & 3.8 & 26 & 46 & 29 & 122 & 0 & 0 & 0 \\ Dimethyl sulfide & 4.5 & 0 & 9 & 0 & 22 & 0 & 0 & 0 \\ Methyl propionate & 4.6 & 0 & 0 & 0 & 0 & 45 & 111 & 155 \\
1-Pentanol & 4.9 & 0 & 0 & 14 & 33 & 0 & 0 & 32 \\ Hexanal & 5.5 & 0 & 0 & 0 & 22 & 10 & 17 & 41 \\ Butanoic acid & 5.6 & 0 & 18 & 14 & 22 & 0 & 0 & 0 \\
2-Heptanone & 7.1 & 78 & 64 & 87 & 156 & 4 & 11 & 18 \\ Heptanal & 7.3 & 0 & 0 & 0 & 0 & 0 & 0 & 23 \\ Dimethyl sulfone & 7.7 & 13 & 18 & 7 & 11 & 0 & 0 & 0 \\ \(\alpha\)-Pinene & 8.0 & 39 & 32 & 29 & 33 & 0 & 0 & 0 \\ Hexanoic acid & 8.6 & 317 & 59 & 108 & 133 & 0 & 0 & 0 \\
2-Nonanone & 10.6 & 100 & 91 & 123 & 155 & 0 & 0 & 0 \\ Nonanal & 10.8 & 0 & 0 & 87 & 56 & 7 & 13 & 18 \\ Octanoic acid & 11.8 & 74 & 68 & 159 & 244 & 12 & 28 & 82 \\ Nonanoic acid & 13.2 & 9 & 5 & 14 & 33 & 0 & 0 & 0 \\ Decanoic acid & 14.6 & 48 & 32 & 36 & 89 & 0 & 11 & 50 \\
2-Methylpropanoic acid & 14.8 & 22 & 9 & 7 & 0 & 4 & 4 & 14 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Relative abundance of volatile compounds in milk, and variations with temperature conditions. In a separate experiment, samples from one carton of conventional whole milk were prepared at the same time and held in the autosampler at room temperature before they were sequentially exposed to the SPME fiber and injected. The relative abundance for each of the major compounds increased from 2.50 to 5.67 h and, with the exception of the aldehydes, remained steady or slightly decreased afterwards (Table 3). The initial increases, and the continued increases by octanal and nonanal, were apparently due to compound formation. The later decreases may have been caused by breakdown into other products. The results indicate that extending the analysis time of milk samples will increase the number of compounds detected. Some relative abundance numbers were elevated due to enhanced compound formation whereas other numbers fell as further reactions occurred. Moreover, extracting at a high temperature may concentrate higher molecular weight compounds on the fibers and displace lower molecular weight volatiles due to an SPME film capacity effect [18]. It must be emphasized that if the analysis of the compounds present in unheated refrigerated milk is the goal, its analysis times should be kept to a minimum.
## 4 Numerical Simulations of Underwater Rock-Plug Blasting
### Simulation Results
#### 4.3.2 Comparison of Penetration Effects  
Figure 16 shows the experimental and simulation results under the three conditions with an opening angle of 30\({}^{\circ}\) to compare the distribution of block particles flowing out from the upstream and downstream end faces and the penetration effect after blasting. Whether considering the upstream or the downstream end faces, the numerical simulation results for the block distribution agreed with the experimental results, and the penetration effect in the rock-plug model was improved with the increase in the head of water in the upstream water tank. A few block particles remained on the downstream end face in the rock-plug model with different heads of water. This finding indicated that whether the rock plug was penetrated or not depended on whether the block particles from the upstream end face remaining in the rock plug after blasting could fall to the downstream water tank by overcoming the frictional resistance under the effects of water pressure, water scouring, and gravity. The blast load mainly played a role in two aspects of the underwater rock-plug blasting experiments: one was the formation of a blasting crater on the downstream end face of the rock plug, the other was the application of a loosening or weak throwing effect on block particles at the upstream end face.
# Development of Additive Fibonacci Generators with Improved Characteristics for Cybersecurity Needs
## 5 Results  
### Research of Repetition Periods of AFG and MAFG Pseudorandom Sequences  
The following research was conducted using an abstract model considering proven identity hardware and abstract generators models. It is necessary to speed up the simulation process.  
Table 1 presents the received results of AFG and MAFG repetition periods, \(Tp\), for a few small module \(p\) values that determined on the whole set of possible values of the initial number, \(X(0)=0\div p^{6}-1\).  
In this case, for MAFG, the output signal value a of the logic circuit LC (Figure 1) was determined, according to Equation (5), as the sum for the module 2 for all bits of number \(x_{i}\) in the register Pr1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Some \(p\) Values**} & \multicolumn{2}{c}{**Max and Min Repetition Period Values**} \\ \cline{2-3}  & **AFG** & **MAFG** \\  & **(without Logic Circuit LC)** & **(with Logic Circuit LC)** \\ \hline
2 & 63 & 10 \\
3 & 728 & 2 \\  & 3124 & 14,409 \\
5 & 4 & 5 \\  & 2400 & 105,833 \\
7 & 24 & 11,360 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Repetition periods of AFG and MAFG output sequences for \(p\) value on the whole set of possible values, \(X(0)=0\div p^{6}-1\).  
Table 1 shows the maximum and minimum values of the period \(Tp\). It should be noted that when \(p=2\) and \(p=3\) on the whole set, \(X(0)=0\div p^{6}-1\) fixed only one value \(Tp=p^{6}-1\). It coincides with the known theoretical results presented in Ref. [25].  
Where for larger values of module p, determination of repetition period, \(Tp\), on the whole set of values, \(X(0)=0\div p^{6}-1\), requires a lot of machine time, all the following research was conducted for a fixed value, \(X(0)=1\). Table 2 shows the repetition period, \(Tp\), for some \(p\) values and fixed values, \(X(0)=1\).  
Based on the research results of the output sequences, repetition periods of the AFG and proposed MAFG, in which the modules of the recurrent equations are prime numbers, such a conclusion can be made. When \(p>3\) the repetition periods MAFG is significantly greater than the AFG. When \(p=2\) and \(p=3\), the repetition periods of AFG reach, theoretically, the maximum value, \(Tp=p^{6}-1\), for all possible values, \(X(0)\).
# Discretly Exploiting Inter-Session Information for Session-Based Recommendation
## 4 Methodology
### Inter-Session Module
#### 4.2.2 Weight Update with Stability  
In this paper, stability is calculated by intra-session information and refers to the consistency of user interests. When a user's interest in a certain factor remains stable over all short-term interactions, their interest in this factor is deemed more stable. Our analysis suggests that higher stability indicates that the user's preference in this factor is easier to be discerned from the intra-session data, without requiring additional information to infer their preference at that time.  
We propose a scheme to measure the stability according to the interest bias of the interest units. The architecture details for weight update with stability are shown in Figure 2. In the Units Splitter, the \(i\)-th interest unit \(U_{(s,i)}\) is defined to be a sub-sequence of session \(s\) as in Equation (5). Obviously, there are \(n\) interest units split from \(s\), where the \(i\)-th interest unit contains the first \(i\) items in \(s\), and the next interest unit has one more item \(v_{(s,i+1)}\) than the previous interest unit.  
\[U_{(s,i)}=\left\{v_{(s,1)},...,v_{(s,i)}\right\} \tag{5}\]  
To measure the stability, the factor-level embeddings of these interest units are required. Similarly, we calculate the factor-level embeddings using DRL based on the initial average-pooling embeddings. Let UnitEncoder be the function Equation (6) for short.  
\[\text{UnitEncoder}(U_{(s,i)})=\left\{U_{(s,i)}^{(f,1)},...,U_{(s,i)}^{(f,K)}\right\} \tag{6}\]  
Then, the stability of session \(s\) on factor \(k\) can be measured with Equation (7), which accumulates the cosine similarities of the embeddings of all interest units as the divergence on factor \(k\). For ease of subsequent analysis, we provide the following formula for the reciprocal of stability and employ the Softmax function to regulate its magnitude:  
\[\frac{1}{\textit{Sta}_{s}^{(f,k)}}=\sum_{i=1}^{n}\sum_{j\
eq i}^{n}\frac{ \mathbf{U}_{(s,i)}^{(f,k)}\mathbf{U}_{(s,i)}^{(f,k)}}{||\mathbf{U}_{(s,i)}^{(f,k)}||\cdot ||\mathbf{U}_{(s,i)}^{(f,k)}||}, \tag{7}\]  
\[[\frac{1}{\textit{Sta}_{s}^{(f,1)}},...,\frac{1}{\textit{Sta}_{s}^{(f,K)}}]= \text{SoftMax}([\frac{1}{\textit{Sta}_{s}^{(f,1)}},...,\frac{1}{\textit{Sta}_ {s}^{(f,K)}}]), \tag{8}\]where \(Sta_{s}^{(f,k)}\) is the stability of session \(\mathbf{s}\) on factor \(k\). With the introduction of \(Sta_{s}^{(f,k)}\), the weight of \(\mathcal{A}_{S}^{(g,k)}\) is updated as in the following Equation (9):  
\[a_{(S,s,j)}^{(g,k)}(\mathbf{new})=\sqrt{\frac{a_{(S,s,j)}^{(g,k)}(\mathbf{ old})}{Sta_{s}^{(f,k)}}}, \tag{9}\]  
where \(a_{(S,s,j)}^{(g,k)}(\mathbf{old})\) and \(a_{(S,s,j)}^{(g,k)}(\mathbf{new})\) denote the previous weight and updated weight inside \(\mathcal{A}_{S,s}^{(g,k)}\) in the optimization step. Note that, due to the difference in stability, sessions have their own factor-level inter-session graph \(\left\{\mathcal{G}_{(S,s)}^{(g,k)},...,\mathcal{G}_{(S,s_{M})}^{(g,k)}\right\}\) evolved from the factor-level inter-session graph \(\mathcal{G}_{S}^{(g,k)}\).
## 2 Materials and Methods  
Figure 1 presents our technical roadmap, outlining the content and procedures of our research. In this study, we integrated Geographic Information Systems (GIS) with Computational Fluid Dynamics (CFD) to focus on the Tianshan Mountain range as our model area. Our objective is to investigate how geographical features affect wind patterns. Additionally, we gathered extensive data, including winter air quality readings from five monitoring stations, to provide a practical basis for validating our simulation results. Leveraging GIS allows for more efficient model generation. Coupling this with fluid simulation software (ANSYS 2022 R1) significantly boosts model accuracy, reduces processing time, and enhances computational efficiency.
# An Intelligent Error Correction Algorithm for Elderly Care Robots
## 4 Experimental Results and Analysis
### Experiment Results
#### 4.3.2 Algorithm Feasibility Verification  
To explore whether the error-recognition gesture-correction algorithm in this paper achieves the effect of finding and correcting errors, after the experiment, taking 01 gesture as an example, the gray images of the elderly wrong gesture and the gesture that was mistakenly recognized during the experiment are selected, as shown in Figure 8.  
Through experimental analysis, gesture 01 can be recognized as gesture 00, 01, 02, and 03, and the recognition probability is 17.6%, 76.4%, 5%, and 1%, respectively. It can be found that the probability of recognizing 01 is still the largest. If we look at the gray image of gesture, 00 and 01 have high similarities. 01 is similar to 02 and 03, but the similarity is low. This paper aims to find out these easily misunderstood gestures and use the intelligent error-correction algorithm to correct them. This process includes two tasks: one is to find errors and the other is to correct errors, so the experiment finally summarizes the number of errors found and corrected by the two algorithms into a histogram, as shown in Figure 9.  
As can be seen from Figure 8, in the process of 200 01 gesture recognitions, the correct recognition is 152 times and the wrong recognition is 48 times. The number of errors corrected is 34.
# Accuracy of Dynamic Modulus Models of Asphalt Mixtures Containing Reclaimed Asphalt (RA)
## 1 Introduction
[11] used various machine learning-based algorithms such as support vector machines (SVMs), artificial neural networks (ANNs), ensemble boosted trees (Boosted), and Gaussian process regression (GPR) to predict the dynamic modulus of warm mix asphalt with different contents of reclaimed asphalt. They found that the Boosted algorithm provided the best simulation of \(|E^{*}|\) compared to the other above-mentioned algorithms. The Witczak statistical model [5] and the Hirsch model [12] were among the first models used to evaluate the dynamic modulus and phase shift angle at specific temperatures and stresses, taking into account the characteristics of the mix. The Witczak 1-37A [13] and Witczak 1-40D [5] models are the most popular models for predicting the dynamic modulus of a conventional virgin asphalt mixture. These two empirical regression models are based on a sigmoidal function. The good prediction  
Figure 2: Graphic representation of the dynamic modulus components including the phase angle \(\Phi\). ability of these methods has led to their incorporation into the Mechanistic-Empirical Pavement Design Guide (MEPDG) for pavement design. \[|E^{*}|=\frac{\sigma}{\varepsilon} \tag{1}\]  
where \(\sigma\) represents the dynamic stress applied to the material and \(\varepsilon\) represents the dynamic strain or deformation of the material. [PERSON], [PERSON], [PERSON] (2S2P1D), Huet-Sayegh, and Prony series present the linear viscoelastic behaviour of bituminous materials and model the dynamic modulus as a composition of springs and the elastic elements and linear dampers as the Newtonian viscous elements. All the aforementioned models proved to have some limitations depending on the mixture properties and temperature level (especially at high and low temperatures). In this context, it is necessary to study the accuracy of some of these predictive models on different asphalt mixtures [14; 15; 16]. This research was therefore dedicated to studying the accuracy of some of the predictive models: Witczak and generalized Huet-Sayegh models to predict the master curve on asphalt mixtures (asphal concrete--AC) containing different contents of reclaimed asphalt.
## 2 Related Works
Bayesian network random consensus (BANSAC) [21] utilizes Bayesian networks to update the probability of individual points, continuously iterating and optimizing the model. [PERSON] et al. [26] proposed a depth-based method to estimate the fundamental matrix through the relative depths between corresponding points. [PERSON] et al. [27] started from anchored objects, leveraging the power of the pre-trained large-scale 2D foundation model, and adopted a framework with hierarchical feature representation and 3D geometry principles to estimate the relative camera pose between object prompts and the target object in new views. At the same time, ref. [28] proposed a random epipolar constraint loss function and a lightweight optical flow estimation framework to constrain the neural network and enhance the model's generalization ability. Parallel sample consensus (PARSAC) [29] predicts multiple sets of sample and inlier weights to determine the model parameters for each potential instance in parallel. Naturally filtered sample consensus (NeFSAC) [10] firstly introduced the concept of poor conditioning. NeFSAC holds that even if each point in the minimal point set has particularly small errors, certain combinations can still lead to significant errors in model estimation due to the inconsistent spatial relationships among some points, thus preventing the estimation of models using bad minimal samples. Our work follow the concept of NeFSAC and introduces a novel end-to-end neural network. Instead of solely relying on datasets to constrain the neural network training process, we incorporate a specific positional-aware module called spatial encoder to address this problem.
# Research on Tracking Technique Based on BPSK-CSK Signals
## 2 Materials and Methods
### A BPSK-CSK Signal Tracking Method Based on Time-Division Multiplexing
#### 2.3.1 Carrier-Tracking Loop  
The carrier loop of the CSK signal is designed using Phase-Locked Loop (PLL). The carrier-tracking loop used in the CSK-tracking algorithm is shown in Figure 5.  
Figure 4: Flowchart of the BPSK-CSK tracking method.  
Figure 3: BPSK-CSK time division multiplexing diagram.  
When the coherent integration time is \(T_{\rm p}\), the cumulative values of \(I_{k}(I_{p}(k))\) and \(Q_{k}(Q_{p}(k))\) in the \(k\)th segment are shown in Equation (3), where \(A\) is the amplitude, \(T\) is the sampling interval, \(N\) is the number of samples,\(f\) is the tracking frequency deviation, and \(N_{I_{k}}\) and \(N_{Q_{k}}\) are the I-channel and Q-channel noise, respectively.  
The PLL discriminator used in this paper is a two-quadrant arctangent discriminator. Its discriminator equation is shown in Equation (4), where \(n_{k}\) is the noise. The PLL corresponding to the tracking frequency range is calculated as Equation (5).  
\[I_{k}+jQ_{k}=\frac{A}{2}\frac{\sin(\pi\Delta fTN)}{\sin(\pi\Delta fT)}\exp(j( \pi\Delta fT(N-1)+2\pi\Delta fTNk))+N_{I_{k}}+jN_{Q_{4}} \tag{3}\]  
\[\phi_{e}=\arctan\frac{Q_{k}}{I_{k}}=\Delta fT(N-1)+2\pi\Delta fTNk+n_{k} \tag{4}\]  
\[\mid\phi_{k}-\phi_{k-1}\mid=2\pi\mid\Delta f\mid T_{p}<\frac{\pi}{2} \tag{5}\]  
In Figure 5, the carrier-tracking loop in this paper mainly adopts a phase-locked loop, which mainly consists of a carrier phase discriminator, a carrier loop filter, and a carrier NCO, and detects the phase difference between the carrier replicated by the carrier NCO and the carrier of the input digital intermediate frequency (IF) signal, then adjusts the phase of the replicated carrier accordingly according to the feedback loop. The phase of the two is kept consistent in order to strip out the carrier in the received signal.
# Stochastic Modeling and Optimal Time-Frequency Estimation of Task-Related HRV
###### Abstract  
In this paper, we propose a novel framework for the analysis of task-related heart rate variability (HRV). Respiration and HRV are measured from 92 test participants while performing a chirp-breathing task consisting of breathing at a slowly increasing frequency under metronome guidance. A non-stationary stochastic model, belonging to the class of Locally Stationary Chirp Processes, is used to model the task-related HRV data, and its parameters are estimated with a novel inference method. The corresponding optimal mean-square error (MSE) time-frequency spectrum is derived and evaluated both with the individually estimated model parameters and the common process parameters. The results from the optimal spectrum are compared to the standard spectrogram with different window lengths and the Wigner-Ville spectrum, showing that the MSE optimal spectral estimator may be preferable to the other spectral estimates because of its optimal bias and variance properties. The estimated model parameters are considered as response variables in a regression analysis involving several physiological factors describing the test participants' state of health, finding a correlation with gender, age, stress, and fitness. The proposed novel approach consisting of measuring HRV during a chirp-breathing task, a corresponding time-varying stochastic model, inference method, and optimal spectral estimator gives a complete framework for the study of task-related HRV in relation to factors describing both mental and physical health and may highlight otherwise overlooked correlations. This approach may be applied in general for the analysis of non-stationary data and especially in the case of task-related HRV, and it may be useful to search for physiological factors that determine individual differences.  
Keywords:locally stationary chirp processes; non-stationary signals; optimal time-frequency estimate; regression analysis; task-related HRV; Wigner-Ville spectrum +
Footnote †: journal: Applied
## 3 Methodology
### Validation Framework  
#### 3.5.1 Reference Data Sources  
The validation methodology employs two independent reference sources to assess the accuracy of height estimations. The primary reference consists of high-resolution drone measurements providing precise height information within the study area. This is complemented by Global Human Settlement Layer (GHSL) data, which offers an additional independent source for height validation. The validation focuses on a selected area within the Hanyang district of Wuhan, chosen for its representation of diverse urban structures and vertical development patterns.