esa-sceva/satcom-chunk-collection huggingface dataset:
# Eye Tracking and Human Influence Factors' Impact on Quality of Experience of Mobile Gaming
## 3 Experimental Setup
### Subjective Assessment  
The subjective assessment was conducted using an Android application specifically designed to collect additional user information alongside video playback and quality ratings in our previous studies [20]. For this experiment, the application was modified to record timestamps for every user input. The application was run on a Redmi K60 Ultra mobile phone, equipped with a Dimensity 9200+ processor and 12 GB of RAM. As the users were wearing a Pupil Core headset and providing ratings on a mobile application as shown in Figure 1, it was not possible to adhere to all the recommendations provided in ITU-T P.910 [47] and ITU-R BT.500 [57]. However, the methodology for subjective assessment regarding the number of users, rating scales, etc., adhered to the ITU-T P.910 [47] recommendations. Before each assessment, users participated in a training session during which they received detailed instructions both verbally and in writing. Additionally, the subjective assessments adhered to the research ethics principles established by the Swedish Research Council [58].  
Subsequently, users were shown one video clip from each game and asked to indicate whether they liked the game content on a binary scale ('Yes' or 'No'). They were also requested to rank their delight towards the game content on a 1-9 point Likert scale. After the delight ratings, the users were shown 4 videos from one game in a randomized order to obtain quality ratings. The MOS values were obtained using the five-point ACR scale using the Single-Stimulus method.  
A total of 40 stimuli were presented to the subjects, with the option for playback available in the mobile application in cases of indecision. However, due to the time required for the training session and particularly for calibrating the Pupil Core headset to accommodate different head sizes and eye positions to achieve a confidence value close to 1, the total duration for individual sessions was approximately 13-15 min excluding the calibration process. Thirty-nine users participated in the assessment, the majority of whom were either bachelor's or master's students in Computer Science and familiar with quality ratings. The ratings of one subject were discarded due to inconsistency between the binary and Likert scales regarding delight; specifically, the subject selected 'Yes' on the binary scale but gave a rating lower than 4 on the Likert scale. Additionally, two outliers were detected based on MOS scores and subsequently excluded from the final calculations, resulting in a total of 36 users. Among these 36 participants, there were 24 males and 12 females, with a mean age of 26 years and a standard deviation of 11.21.
# HORSIC+: An Efficient Post-Quantum Few-Time Signature Scheme
## 5 Conclusions  
In this paper, we proposed HORSIC+, an efficient post-quantum few-time signature scheme. HORSIC+ differs from HORSIC in that HORSIC+ does not apply \(f\) as a plain function to the signature key, but uses a member of a function family which is second  
\begin{table}
\begin{tabular}{l r r r r r r} \hline \hline
**Scheme** & **Key Gen.** & **Signing** & **Verification** & **Sig. Size** & **V. K. Size** & **Security Level** \\ \hline HORS(\(1^{n},t,k\)) & \(t\) & \(1\) & \(k+1\) & \(kn\) & \(tn\) & \(k(\log_{2}\left(t/k\right))\) \\ HORST(\(1^{n},t,k\)) & \(2t-1\) & \(1\) & \(k(\log_{2}t+1)\) & \((k+\log_{2}t)\)\(n\) & \(n\) & \(k(\log_{2}\left(t/k\right))\) \\ HORSIC+(\(1^{n},t,k,z,w\)) & \(wt\) & \(kw\) & \(kw\) & \(kn\) & \((1+w+t)n\) & Equation (34) \\ \hline HORS(\(1^{128},2^{10},16\)) & 1024 & 1 & 17 & \(16\times 128\) & \(1024\times 128\) & 96 \\ HORST(\(1^{128},2^{10},16\)) & 2047 & 1 & \(16\times 11\) & \(26\times 128\) & 128 & 96 \\ HORSIC+(\(1^{128},2^{10},10,22,13\)) & 13,312 & \(10\times 13\) & \(10\times 13\) & \(10\times 128\) & \(1038\times 128\) & \(\min\left(96,111\right)\) \\ \hline HORS(\(1^{256},2^{16},32\)) & 65,536 & 1 & 33 & \(32\times 256\) & \(65,536\times 256\) & 352 \\ HORST(\(1^{256},2^{16},32\)) & 131,071 & 1 & \(32\times 17\) & \(48\times 256\) & 256 & 352 \\ HORSIC+(\(1^{256},2^{16},26,35,10\)) & 655,360 & \(26\times 10\) & \(26\times 10\) & \(26\times 256\) & \(65,547\times 256\) & \(\min\left(353,233\right)\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of HORS, HORST, and HORSIC+.  
Figure 3: Normalized security level of HORSIC+ and HORS/HORST for multiple messages.  
preimage resistant, undetectable, and one-way. Moreover, HORSIC+ uses the chaining function \(c^{\varepsilon}(x,\vec{r})\) similar to W-OTS\({}^{+}\). These enable the strict security proof without the need for the used function family to be a permutation or collision resistant. We proved HORSIC+ is existentially unforgeable under chosen message attacks, if the used function family is a second-preimage resistant family of undetectable one-way functions and \(H\) and \(G\) are cryptographic hash functions in the random oracle model. HORSIC+ reduces the signature size by as much as 37.5% or 18.75% compared to HORS and by as much as 61.5% or 45.8% compared to HORST for the same security level. Future work includes further analysis of HORSIC+ and integration of HORSIC+ in SPHINCS.  
Conceptualization, J.L.; methodology, J.L.; validation, J.L. and Y.P.; investigation, J.L.; Writing--Original draft preparation, J.L.; Writing--Review and editing, J.L. and Y.P.; funding acquisition, Y.P. Both authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT), grant number 2020R1F1A1048443. This research was also supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government(MSIT) (No. NRF-2017R1C1B5076925).  
Not applicable.  
The authors declare no conflict of interest.
# Improving Multi-Instance Multi-Label Learning by Extreme Learning Machine
## 3 The Proposed Approach MIMLELM
### ELM Ensemble Based on GA
#### 3.4.3 Crossover  
We use the normal single point crossover. A crossover point is selected randomly between one and 1 (length of the chromosome). Crossover probabilities are computed as in [44]. Let \(f_{max}\) be the maximum fitness value of the current population, \(\overline{f}\) be the average fitness value of the population and \(f^{\prime}\) be the larger of the fitness values of the solutions to be crossed. Then, the probability of crossover, \(\mu_{c}\), is calculated as:  
\[\mu_{c}=\begin{cases}k_{1}\times\frac{f_{max}-f^{\prime}}{f_{max}-f^{\prime}},& \text{if }f^{\prime}{>}\overline{f},\\ k_{3},&\text{otherwise}.\end{cases} \tag{10}\]  
where the values of \(k_{1}\) and \(k_{3}\) are kept equal to 1.0 as in [44]. Note that when \(f_{max}=\overline{f}\), then \(f^{\prime}=f_{max}\) and \(\mu_{c}\) will be equal to \(k_{3}\). The aim behind this adaptation is to achieve a trade-off between exploration and exploitation in a different manner. The value of \(\mu_{c}\) is increased when the better of the two chromosomes to be crossed is itself quite poor. In contrast, when it is a good solution, \(\mu_{c}\) is low so as to reduce the likelihood of disrupting a good solution by crossover.  
_Mutation_: Each chromosome undergoes mutation with a probability \(\mu_{m}\). The mutation probability is also selected adaptively for each chromosome as in [44]. That is, \(\mu_{m}\) is given below:  
\[\mu_{m}=\begin{cases}k_{2}\times\frac{f_{max}-\overline{f}}{f_{max}-\overline{ f}},&\text{if }f{>}\overline{f},\\ k_{4},&\text{otherwise}.\end{cases} \tag{11}\]where the values of \(k_{2}\) and \(k_{4}\) are kept equal to 0.5. Each position in a chromosome is mutated with a probability \(\mu_{m}\) in the following way. The value is replaced with a random variable drawn from a Laplacian distribution, \(p(e)\infty e^{-\frac{|e-\mu|}{\delta}}\), where the scaling factor \(\delta\) sets the magnitude of perturbation and \(\mu\) is the value at the position to be perturbed. The scaling factor \(\delta\) is chosen equal to 0.1. The old value at the position is replaced with the newly-generated value. By generating a random variable using a Laplacian distribution, there is a nonzero probability of generating any valid position from any other valid position, while the probability of generating a value near the old value is greater.  
The above process of fitness computation, selection, crossover and mutation is executed for a maximum number of generations. The best chromosome seen up to the last generation provides the solution to the weighted classifier ensemble problem. Note that sum \(w_{i}\) should be kept during the evolving. Therefore, it is necessary to do normalization on the evolved \(w\). Thus, we use a simple normalization scheme that replaces \(w_{i}\) with \(w_{i}/\sum\limits_{i=1}^{N}w_{i}\) in each generation.
## 4 Results  
### Preliminary Phase  
In the preliminary phase of the project, we utilized the 2019 CSLAP data to explore different in situ parameters and their thresholds, varying time windows (concurrent, one-day, and two-day time windows), and the impact of several lake characteristics (the trophic states of lakes, ecoregions in which lakes are located, dominant land cover in the surrounding watershed, and month of data collection) on model results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & **Label** & **Equation 1** & **Citation** \\ \hline Two-band Ratio & 2 BDA & \(\frac{R(705)}{R(665)}\) & [38] \\ \hline Three-band Ratio & 3 BDA & \(\left(\frac{1}{R(665)}-\frac{1}{R(705)}\right)\times R(740)\) & [63] \\ \hline Normalized difference chlorophyll index & NDCI & \(\frac{R(705)-R(665)}{R(705)+R(665)}\) & [64] \\ \hline Cyanobacteria index & CI & \(-\left[\left(R(665)-R(560)\right)-\left(\frac{665-560}{705-560}\right)\times \left(R(705)-R(560)\right)\right]\) & [65] \\ \hline Maximum chlorophyll index & MCI & \(\left[R(705)-R(665)\right]-\left(\frac{705-665}{740}\right)\times\left[R(740)- R(665)\right]\) & [66] \\ \hline Maximum peak height & MPH & \(\left[R(705)-R(665)\right]-\left(\frac{705-665}{865-665}\right)\times\left[R(865 )-R(665)\right]\) & [67] \\ \hline Surface algal bloom index & SABI & \(\frac{\left(R(865)-R(665)\right)}{\left(R(860)+R(560)\right)}\) & [68] \\ \hline \end{tabular}
\end{table}
Table 1: Different indices derived from Sentinel-2 imagery for retrieval models.
###### Abstract  
Sex differences in response to hypovolemia are still an open issue, which is readdressed here by exploiting the potential of near-infrared spectroscopy (NIRS) to monitor the response of lower body negative pressure (LBNP) in upper and lower limbs. In 28 subjects in a supine position, non-invasive arterial blood pressure was continuously monitored along with NIRS parameters from the forearm and thigh during randomized 90 s exposure to LBNP at \(-10\), \(-20\), \(-30\), and \(-40\) mmHg, followed by one 5 min exposure to \(-30\) mmHg. LBNP did not affect arterial blood pressure, slightly increased the heart rate, and affected blood volume in both limbs (\(p<0.005\)). Slopes of tissue oxygenation and deoxy-hemoglobin revealed pressure (\(p<0.001\)) and sex (\(p<0.05\)) dependences for the vasoconstrtive response to LBNP in both arms and legs, with some evidence of larger vasoconstriction in legs. Most variables reached a stable value within 90 s in the arm, while longer time courses were observed in the leg. NIRS is a valuable methodology to detect early LBNP-induced hemodynamic changes, providing that blood volume and blood flow contribution are discriminated. A comparative analysis of time courses proved useful in revealing stronger vasoconstrtive responses in males than in females and in lower limbs than in upper limbs. The same approach could be applied to other experimental contexts.  
blood flow; blood volume; lower body negative pressure; near-infrared spectroscopy; tissue oxygenation 2020
## Materials and Methods
### Methods
#### 2.2.2. Charges Density Assessment  
Both Z potential and conductometric titration were used to monitor charges density and colloidal stability of water-suspended cellulose nanocrystals. Zeta potential (mV) and conductivity (mS cm\({}^{-1}\)) of the CNCs in the diluted water-suspension CNCs 0.5 wt% at pH 6 were determined by electrophoretic light scattering (ELS), using the PALS technology (mod. Litesizer 500, [PERSON], Graz, Austria). Measures were replicated 5 times, at 25.0 \(\pm\) 0.1 \({}^{\circ}\)C, by means of a 35-mW diode laser (\(\lambda\) = 658 nm) and at a 15\({}^{\circ}\) detection angle. Conductometric titration was used to evaluate weak and strong acids in the three suspended samples: CNC\({}_{\text{COOH}}\), CNC\({}_{\text{COOR}}\) and CNC\({}_{\text{SO3H}}\). A total of 15 mg of CNCs were suspended in 200 mL of distilled water and sonicated for 5 min. The pH of the suspension was then brought to 3 with HCl 0.1 M before the titration. The apparatus is constituted of a pHmeter and conductivity meter (Multi3620 IDS) and a titrator (Si Analytics Model Titronic 300, YSI, 1725 Brannum Lane, Yellow Springs, OH, USA). NaOH 0.01 M was dispensed in diluted CNCs at 100 ml increments, and stirred for 1 min, before collecting the electric conductivity (uS/cm). While the sodium hydroxide (NaOH) is added through the titration, the conductivity first decreases to reach a constant and stable plateau briefly and after, it increases to reach a constant value of electric conductivity. The charges density of weak and strong acids was calculated via following equations:  
\[\text{Strong acid }(\text{mmol/kg})=\frac{M\,\text{x }(V1-V0)}{m}\, \tag{1}\]  
and  
\[\text{Weak acid mmol/kg}=\frac{M\,\text{x }(V2-V1)}{m}\, \tag{2}\]  
where, \(M\) is the concentration of the NaOH solution (mmol/L); \(m\) is the dry weight of the CNCs samples (kg); \(V0\) is the theoretical NaOH volume used for neutralization of the added free acid (\(L\)); \(V1\) is the volume of NaOH solution consumed at the first intersection point (\(L\)); \(V2\) is the volume of NaOH solution consumed at the second intersection point (\(L\)).
# Scheduling Optimization of Prefabricated Construction Projects by Genetic Algorithm
## 3 Problem Statement and Mathematical Formulation
### Formulation of Mathematical Model  
In this research, the prefabricated construction project scheduling problem will minimize the project completion time as the goal, while considering the main constraints that affect the prefabricated construction project scheduling problem, including the predecessor's logical relationship constraint, the renewable resource limit and the impact of prefabricated component supply. Representing the problem with some math models, the established scheduling mathematical model is as follows:  
\[Mint\ F_{n} \tag{1}\]  
\[S_{i}+d_{i}\leq S_{j}\quad i\in Prec_{j} \tag{2}\]\[\sum_{j\in A_{t}}r_{jk}\leq R_{k}^{t} \tag{3}\]  
\[LT_{j}\leq S_{j} \tag{4}\]  
In the model established above, Formula (1) is the goal to be achieved by the problem, which is to minimize the duration of the project, expressed in terms of the completion time of the last dummy activity \(n\). Formula (2) is the logical relationship before and after the implementation of the activity, that is, the activity cannot start before the end of its predecessor activities. Formula (3) represents that at the same time, the demand of renewable resources (such as manpower, machinery, equipment, etc.) required by the activities being implemented cannot exceed the maximum supply of resources. Formula (4) converts the production and transportation constraints of prefabricated components in prefabricated building construction into the supply time limit of prefabricated components, that is, the maximum on-site delivery time of prefabricated components required for the execution of the activity needs to be less than the start execution time of the activity.
## 2 Theoretical Analysis
### Transmission from the Source to the Target Plane
\[\mathrm{W}_{ij}(\overline{\mathbf{t}})=\frac{A_{i}A_{j}B_{ij}}{\left[\det( \overline{\mathbf{A}}+\overline{\mathbf{B}}\mathbf{M}_{0 ij}^{-1}+\overline{ \mathbf{B}}\overline{\mathbf{P}})\right]^{\frac{1}{2}}}\exp[-\frac{ik_{-}}{2} \overline{\mathbf{t}}^{T}\mathbf{M}_{1 ij}^{-1}], \tag{8}\]\[\mathbf{M}_{1 ij}^{-1}=-\bigg{(}\overline{\mathbf{B}}^{-1}-\frac{1}{2}\overline{ \mathbf{P}}\bigg{)}^{T}\bigg{(}\mathbf{M}_{0}^{-1}+\overline{\mathbf{B}}^{-1} \overline{\mathbf{A}}+\overline{\mathbf{P}}\bigg{)}^{-1}\bigg{(}\overline{ \mathbf{B}}^{-1}-\frac{1}{2}\overline{\mathbf{P}}\bigg{)}+\bigg{(}\overline{ \mathbf{D}\overline{\mathbf{B}}^{-1}}+\overline{\mathbf{P}}\bigg{)}. \tag{9}\]  
In the derivation process of Equation (8), the integral formula \(\int\limits_{-\infty}^{\infty}\exp\Bigl{(}-ax^{2}\Bigr{)}dx=\sqrt{\frac{x}{a}}\) has been used. Here, \(\mathbf{M}_{1 ij}^{-1}\) denotes the partially coherent complex curvature tensors in the target plane, \(\overline{\mathbf{A}},\overline{\mathbf{B}}\), \(\overline{\mathbf{D}}\) and \(\overline{\mathbf{P}}\) are all \(4\times 4\) transfer matrices that describe the optical system,  
\[\overline{\mathbf{A}}\mathbf{=}\Bigg{(} \begin{array}{cc}\mathbf{I}&0 \mathbf{I}\\ 0\mathbf{I}&\mathbf{I}^{*}\end{array} \Bigg{)}\,\overline{\mathbf{B}}\mathbf{= }\Bigg{(} \begin{array}{cc}z\mathbf{I}&0\mathbf{I}\\ 0\mathbf{I}&-z\mathbf{I}^{*}\end{array} \Bigg{)}\,\overline{\mathbf{D}}\mathbf{ =}\Bigg{(} \begin{array}{cc}(1-z/f)\mathbf{I}&0\mathbf{I}\\ 0\mathbf{I}&(1-z/f)\mathbf{I}\end{array} \Bigg{)}\,\,\overline{\mathbf{P}}= \frac{2}{ik\rho_{0}^{2}}\Bigg{(} \begin{array}{cc}\mathbf{I}&-\mathbf{I}\\ -\mathbf{I}&\mathbf{I}\end{array} \Bigg{)}, \tag{10}\]  
\[\rho_{0}=\Big{(}0.545k^{2}C_{n}^{2}\Big{)}^{-3/5}, \tag{11}\]  
where \(C_{n}^{2}\) is the refractive-index structure parameter and \(k=2\pi/\lambda\) is the wave number.\(\rho_{0}\) is the coherence length of a spherical length propagating in weak atmospheric turbulence with Kolmogorov power spectrum.
## 1 Introduction
In practice, these assumptions are frequently violated when experimental datasets include outliers and/or leverage points. As a consequence, a robust regression can be necessary to limit the effects of the anomalous data points in the evaluation of model parameters [15]. Several efforts have been made to overcome the inconsistencies of anomalous observations and, with this aim, several robust approaches have been implemented to obtain the model parameters [16; 17; 18; 19; 20]. Both conventional and robust EPR methods rely on a frequentist estimation framework, which treats model parameters as fixed but unknown quantities. This approach is particularly appropriate when the disturbances are normally distributed and homoscedastic. However, a key limitation of the frequentist approach is that it is not able to quantify the uncertainty around the estimates. To overcome these limitations, a perturbation-based approach could be adopted in order to include uncertainty in the analysis. The issue of the uncertainties inherent in the prediction of the response of structures has been widely discussed in the literature [21; 22; 23; 24; 25]. Within this framework, the direct perturbation method is an effective approach to calculate the effect of parameter uncertainty, by approximating the response as a polynomial in the uncertain parameters [26]. The polynomial form is generally a Taylor series about the mean value of the uncertain parameter. One major advantage of this approach is the possibility to estimate the statistical moments of the capacity model without choosing a specific probability distribution for the uncertain parameters. On the other hand, a higher-order perturbation approach could be necessary to improve the estimation of the statistical properties when the relationship between the output and the uncertain variables is highly nonlinear. Thus, as a further attempt to achieve robust and accurate models, this paper implements a novel EPR technique including uncertainty. The optimal polynomial structure is selected by using GAs. The model parameters are assumed to be Random Variables (RVs), with a normal probability distribution, that are independent from each other. The proposed approach provides an advancement to traditional EPR techniques by introducing a general system identification algorithm capable of accurately identifying the optimal mathematical model. The present research explores, for the first time, the benefits of using the direct perturbation method within an EPR framework. This paper is organized as follows. A technical overview of conventional EPR methodology is firstly furnished. Then, the proposed approach, based on the direct perturbation method, is described in detail. Finally, the robust EPR procedure is applied to predict (i) the shear capacity of RC beams with rectangular cross section without stirrups, starting from an experimental database [13]; (ii) the compressive strength (CS) of cement-based mortar specimens, by adopting an extensive database recently provided in the literature [27].
## 4 Discussion
In this sense, new studies are needed on how the demand profile influences the economic performance of both types of battery. To sum up, in terms of cost effectiveness, no single option has been found, as the two types of batteries currently compete closely in standalone renewable energy systems. This is in accordance with other recent studies, taken as a whole, refs. [3; 27; 28; 30; 31; 32] and it poses tougher competition than just three years ago, when Li-ion batteries were too expensive for power supply systems [17]. Regarding differences between PV and hybrid systems, in every one of the study cases, in the hybrid system a more favorable bias has been obtained for the Li-ion battery than in the PV system. One of the questions of interest is how possible variations in the price of Li-ion batteries are going to affect their profitability, displacing or not lead-acid batteries in standalone renewable energy systems. With the base prices of the study, which correspond to the current market, Li-ion batteries are the option with the lowest NPC in one of the five cases of the PV systems and in two of the hybrid systems, all of them being houses. The analysis has shown a strong sensitivity to the acquisition cost of Li-ion batteries, which with a variation of \(-21\%\) would be the economically optimal choice (in terms of system NPC) in all hybrid systems studied and with a \(-36\%\) in all PV systems. Regarding how variations in the acquisition cost of Li-ion batteries affect the sizing of the economic optimum system, some changes have been observed. In both PV and hybrid systems, the lower the price of Li-ion batteries, the greater the battery capacity. In addition, in optimal PV systems, the lower the price of Li-ion batteries, the lower the power of the PV generator. On the contrary, in optimal hybrid systems, faced with reductions in the price of Li-ion batteries, the power of the PV generator increases slightly while the fuel consumption decreases strongly. Sensitivity analysis on the acquisition cost of the PV generator elements has shown that its reduction favors profitability of Li-ion batteries in PV systems and practically has no effect over hybrid systems. In these, lower inflation in the fuel price favors profitability of Li-ion batteries. Both reductions in the cost of the source of energy (PV or diesel) cause slight reductions in battery capacity of the optimum systems.
# A Virtual Network Resource Allocation Framework Based on SR-IOV
## 2 Related Work
In addition, the bandwidth allocation between multiple VFs of the model is determined by the hardware arbitration of the SR-IOV network card and cannot be dynamically adjusted according to the load of the VM. In addition, [PERSON] et al. [33] also proposed an allocation framework that combines I/O para-virtualization and SR-IOV technology to dynamically sense the load of VMs and assign VFs to network-intensive VMs to improve network performance. However, this model uses weighted averaging method to allocate the bandwidth in VF allocation and does not consider the difference of bandwidth requirements between different loads of VMs.