esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A List of Renovation Measures
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Windows**} \\ \hline
**Solution** & **Glazing \& Frame Systems** & **D** & **U** & **Price** \\
**(cm)** & **(W/m\({}^{2}\)K)** & **(C/m\({}^{2}\))** & **(C/m\({}^{2}\))** \\ \hline
1/ & Existing building facade composition (description on Section 3.1 of this paper) & & & \\ \hline
2/ &
\begin{tabular}{c} Aluminium frame 2 folding leaves with thermal break system \\ Double glazing low emissive coating + solar protection 6/12/6 mm \\ U: 1.8 W/m\({}^{2}\)k \\ Luminous transmittance: 49\% \\ G: 0.38 \\ \end{tabular} & 135 \(\times\) 120 & 2.25 & 104.27 \\ \hline
3/ &
\begin{tabular}{c} Aluminium frame 2 folding leaves with thermal break system and \\ airtightness joint. \\ Double glazing guardian sun 6/12/6 mm \\ U: 2.8 W/m\({}^{2}\)k \\ Luminous transmittance: 42\% \\ G: 0.49 \\ \end{tabular} & 135 \(\times\) 120 & 3.94 & 122.20 \\ \hline
4/ &
\begin{tabular}{c} Aluminium Monoblock frame 2 folding leaves with thermal break system, \\ airtightness joint. Shutter. \\ Double glazing guardian sun 6/12/6-6 mm with acoustic film \\ U: 2.6 W/m\({}^{2}\)k \\ Luminous transmittance: 42\% \\ G: 0.40 \\ \end{tabular} & 135 \(\times\) 120 & 2.9 & 163.38 \\ \hline
5/ &
\begin{tabular}{c} Aluminium monoblock frame 2 folding leaves with thermal break system, \\ airtightness joint. \\ Double glazing green tinted guardian sun 6/12/6 mm with dehydrated air \\ U: 2.8 W/m\({}^{2}\)k \\ Luminous transmittance: 61\% \\ G: 0.59 \\ \end{tabular} & 135 \(\times\) 120 & 2.98 & 114.86 \\ \hline
6/ &
\begin{tabular}{c} Aluminium monoblock frame 2 folding leaves with thermal break system, \\ airtightness joint. \\ Double glazing guardian sun 6/12/6 mm with dehydrated air chamber \\ U: 2.8 W/m\({}^{2}\)k \\ Luminous transmittance: 61\% \\ G: 0.59 \\ \end{tabular} & 135 \(\times\) 120 & 3.1 & 118.25 \\ \hline
7/ &
\begin{tabular}{c} PVC frame, 2 folding leaves, with thermal break system. No air tightening \\ reinforce need. \\ Triple glazing of clear glass panels 8/16/6/16/8 with 2 air chambers argon \\ filled. \\ U:0.60 W/m\({}^{2}\)k \\ G: 0.62 \\ \end{tabular} & 135 \(\times\) 120 & 0.61 & 194.10 \\ \hline
**External Doors** & & & \\ \hline
**Solution** & **Glazing \& Frame Systems** & **D** & **U** & **Price** \\
**(cm)** & **(W/m\({}^{2}\)K)** & **(C/m\({}^{2}\))** & **(C/m\({}^{2}\))** \\ \hline
1/ &
\begin{tabular}{c} Existing building facade composition (description on Section 3.1 of this paper) \\ \end{tabular} & 135 \(\times\) 120 & 0.61 & 194.10 \\ \hline
2/ &
\begin{tabular}{c} Aluminium sliding door frame system 2 leaves with thermal break system, \\ airtightness joint. \\ Double glazing low emissive coating 6/12/6 mm \\ U: 1.6 W/m\({}^{2}\)k \\ Luminous transmittance: 66\% \\ G: 0.41 \\ \end{tabular} & 210 \(\times\) 220 & 2.97 & 148.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Proposed renovation measures for openings: windows and external doors.
# Statistical Method Based Waveform Optimization in Collocated MIMO Radar Systems
## 5 Simulation Results and Discussions
### Validation of Closed-Form Expressions  
For the purpose of validating the theoretical results, we set Monte Carlo simulation runs as \(10^{5}\). Moreover, we investigate the performance of MIMO radar system and quantify the effect of number of transmitter and receiver antenna elements and filter order. The validation of our work and its analysis is given in the following three ways.  
1. Validation of the outage probability expression in (21) is showcased using Figure 1(a). Therein, the values of \(\mathbf{f}\) and \(\mathbf{h}\) are obtained using the match filter bound. We increase the order of \(N_{T}\) and \(N_{R}\) from single-input single-output (SISO) cases to MIMO cases of 2 and 8. It is observed that a great degree of gain is achieved by increasing the antenna order of transceiver units, e.g., there is a 16.7% reduction in outage probability by moving from SISO to MIMO case by setting antenna order of 8 at \(\gamma=1\). From the validation prospective, it is observed that a perfect degree of match exits across the \(\gamma\) range for all the considered antenna diversity orders;
2. Validation of clutter signal PDF, i.e., \(f(\zeta)\) in (3) is presented using Figure 1(b). The aforementioned network configurations are considered herein as well. Due to the large values at low \(\zeta\), a log scale axis is used. Again, a perfect match is observed between the analytical and closed-form expression;
3. Validation of (21) by altering the transmit and receive filter order is demonstrated using Figure 3. Herein, we consider the case \(N_{T}=N_{R}=2\). Initially, we consider a single order filter at transmitter and receiver ends. There is a marked decrease of about 10% in the outage probability if the filter order of 2 is used as compared to 1 at \(\gamma=1\). Furthermore, increasing the filter order further to 4 results only in a marginal change. Hence, in the remainder of the work, we consider a filter order of 2. Again, the Monte Carlo simulation ascertains the theoretical results across the \(\gamma\) range.  
Figure 2: Validation of analytical expression: (**a**) the outage probability expression, i.e., \(P_{out}(\gamma)\) as a function of threshold \(\gamma\), for different antenna diversity order; (**b**) the PDF for clutter, i.e., \(f(\xi)\) as a function of threshold \(\xi\) in a log-log scale, for different antenna diversity order.
## 2 Materials and Methods
### Training  
Data was randomly split into a training set (80%) and a test set (20%). The training and testing processes were repeated for 50 random splits of the original dataset to investigate data variance and for better performance robustness.  
In the NSCLC-Radiogenomic dataset, the _EGFR_ mutant is under-represented, resulting in classifiers with poor predictive performance for this minority class. To overcome the class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied for each fold. This oversampling approach creates new random synthetic minority class instances between the lines that connect each one of the \(k\) nearest neighbors of each minority class sample [46]. This data augmentation technique made it possible to obtain a class-balanced training set (consisting of the same number of mutant and wild-type samples), which was then used to train the classifiers.  
The classifier's hyperparameters were tuned through a 5-fold cross-validation randomized search on the training data. The range of hyperparameters considered for the six classifiers are presented in Tables S1-S6 of the supplementary material.  
Since Elastic Net is a regression model and not a classification model, predictions for this algorithm were performed considering a decision threshold equal to 0.5. This means that normalized predicted probabilities less than 0.5 were assigned to the wild-type class _EGFR_ and probability values greater than or equal to 0.5 were mapped to the mutant class _EGFR_. On the other hand, for the classification models, the optimal threshold was calculated from the Receiver Operating Characteristic (ROC) curve using the threshold-moving method. This method uses the original training set to train the model and then moves the decision threshold such that the minority class examples can be more easily correctly predicted.
# A Bayesian Nonparametric Learning Approach to Ensemble Models Using the Proper Bayesian Bootstrap
## 3 Bootstrap Techniques in Nonparametric Learning
### Proper Bayesian Bootstrap  
A generalization of the Bayesian bootstrap introduced by [PERSON] is proposed in [19; 25], with the main advantage of introducing prior knowledge represented by a distribution function \(F_{0}\). Following [21], the prior of \(F\) is defined as a Dirichlet process \(D(kF_{0})\) where \(F_{0}\) is a proper distribution function and \(k\) represents the level of confidence in the initial choice \(F_{0}\). The resulting posterior distribution for \(F\), given a sample \(x_{1},\dots,x_{n}\) from \(F\), is still a Dirichlet process with parameter \((kF_{0}+nF_{n})\). As a special case when \(k=0\) the procedure is equivalent to the [PERSON]'s one. This bootstrap method allows to introduce explicitly prior knowledge on the data through the choice of \(F_{0}\) and \(k\). It is important to remark that, since \(\phi\) is a function of F, an informative prior on F is an informative prior on \(\phi\).  
When \(k>0\) it is often difficult to derive analytically the distribution of \(\phi(F)\). When \(F_{0}\) is discrete with finite support one may produce a reasonable approximation on the distribution of \(\phi(F)\) by a Monte Carlo procedure obtaining i.i.d. samples from \(D(kF_{0})\). If \(F_{0}\) is not discrete, in order to compute the posterior distribution \(D(kF_{0}+nF_{n})\), a possible way proposed by [19] is to first approximate the parameter \(kF_{0}+nF_{n}\) through \((n+k)F_{m}^{*}\), where \(F_{m}^{*}\) is the empirical distribution of an i.i.d. bootstrap resample of size \(m\) generated from:  
\[G_{0}=\frac{k}{n+k}F_{0}+\frac{n}{b+k}F_{n} \tag{7}\]  
The bootstrap resample is generated from a mixture of the empirical distribution function of \(X_{1},\dots,X_{n}\) and \(F_{0}\). Let now define \(G_{m}^{*}\) as a random distribution which, conditionally on the empirical distribution \(F_{m}^{*}\) of \(X_{m}^{*}\), is a Dirichlet process \(D((k+n)F_{m}^{*})\). Thus, since \(G_{m}^{*}\) is given by a mixture of Dirichlet processes, using [26], when \(m\rightarrow\infty\) the law of \(G_{m}^{*}\) weakly converges to the Dirichlet process \(D(kF_{0}+nF_{n})\).  
Algorithm 1 shows how to estimate (4).  
The conditional distribution expressed in (3) is approximated deriving the empirical distribution function generated by \(\phi^{1},\dots,\phi^{B}\), where \(B\) is the number of bootstrap resamples.  
The Bayesian nonparametric approach based on Proper Bayesian bootstrap can be introduced in ensemble models as explained in Section 4.  
```
Proper Bayesian bootstrap Input: Observations \(x_{1},x_{2},\ldots,x_{n}\) for\(b\) in 1:\(B\)do  Generate m observations \(x_{1}^{*},\ldots,x_{m}^{*}\) from \((k+n)^{-1}(kF_{0}+nF_{n})\)  Draw \(w_{1}^{b},\ldots,w_{m}^{b}\) from \(D(\frac{n+k}{m},\ldots,\frac{n+k}{m})\);  Get \(\phi^{b}=\phi(\mathbf{w^{b}},\mathbf{x^{*}})\)
```  
**Algorithm 1**
## 3 Materials and Methods
### Data Analysis  
Eye tracking data underwent preprocessing through the utilization of custom scripts in Matlab (Version 2016a). For the analysis of pupil size, only those trials were considered where the pupil sizes remained stable and were continuously recorded for 1000 ms prior to and 1000 ms following the visual stimulus. To obtain the mean pupil size change curve, pupil areas were averaged across trials aligned to 200 ms prior to visual presentation. Notably, it observed that monkey pupils began to construct approximately 200 ms post visual presentation, reaching minimum levels between 400 and 500 ms. The mean pupil sizes were calculated during three phases: early (1-200 ms), middle (201-500 ms), and late (501-1000 ms) after stimulus onset.  
In conducting the gaze duration analysis, data from the free viewing interval, specifically between 1000 ms and 3000 ms following visual stimulation, were extracted for evaluation. To assess the relationship between pupil size and gaze duration for each image, we first determined the average pupil size during three distinct phases and recorded the gaze duration for each image presented to the four monkeys. Subsequently, we computed the z-scores for both pupil size and gaze duration within the context of the experiment. Finally, the Pearson product-moment correlation coefficient was calculated to quantify the correlation between the z-scores of gaze duration and pupil size. All figures were produced using Prism (Version 8.0).
# Augmenting Environmental Interaction in Audio Feedback Systems+
## 1 Introduction  
Audio feedback is an acoustic phenomenon that occurs when sound played by a loudspeaker is received by a microphone to create a persistent loop through a sound system. While audio feedback is generally regarded as an undesired situation, for example when a public address system manifests an unpleasant howling tone, there have been numerous artistic examples and compositions that make use of its tone-generating nature. [PERSON] is an oft-cited example of how electric guitar players create feedback-based tones by holding their instruments close to the amplifiers, and [PERSON]'s _Pendulum Music_ (1968) [1] features phasing feedback tones generated by suspending microphones above loudspeakers.  
A modern approach to audio feedback in experimental improvisation and compositional works utilizes computer-based control over sound generation and organization: [PERSON] introduced various examples [2]. [PERSON]'s _Audio Ecosystems_[3] is a prominent example in which a self-feeding feedback loop interconnects a digital system with its acoustic environment.  
By inserting a network of low-level components, represented by a chain of transducers and other acoustic components inserted into the loop, these audio feedback systems can lead to nonlinear behaviours [3], since specific performances cannot be accurately predicted in advance. Many audio feedback systems mostly have focused on design of the low-level relations to generate and organize the feedback sounds while paying less attention to control over the overall sonic shape [4].  
We previously proposed a new concept of audio feedback systems [5] that supports intentional control through tendency design, while preserving other attractive nonlinear features of feedback systems, which could open up new possibilities of musical applications combining nonlinearity and interactivity. In this paper, we explore this concept further by taking account of the relation between system and room acoustics.  
Room acoustics are an essential yet under-examined factor in the shaping of audio feedback. Our work is designed to augment the interaction between system and room acoustics. This context-based control supports the intentional control of audio feedback through the generation of long-term sonic behaviours that respond appropriately to the acoustics of the environment. Our prototypes map signal-inferred properties of room reverberation, ambient noise level and resonances of the acoustic environment to tempo, amplitude and timbre characteristics of the acoustic feedback, respectively. In this paper, these mappings are validated through simulations and evaluated experimentally in different acoustic conditions.
# Optimization of Mass Concrete Construction Using a Twofold Parallel Genetic Algorithm
## 3 Optimization Problem of the Construction Phase of a Massive Concrete Structure Using a GA
### Fitness Function  
The fitness function \(F(\mathbf{X},t_{g})\) can be written as:  
\[F(\mathbf{X},t_{g})=f(\mathbf{X})+P(\mathbf{X},t_{g}) \tag{7}\]  
where \(f(\mathbf{X})\) stands for the normalized cost and \(P(\mathbf{X},t_{g})\) is the penalty function introduced to penalize cracking. It should be noticed that it is a dynamic penalty function, as it depends on the current generation, \(t_{g}\), of the Genetic Algorithm.  
Considering that the geometry of the structure is given, and consequently its volume, the cost function can be understood as a volumetric construction cost expressed by:  
\[f(\mathbf{X})=\omega_{conc}c_{conc}(Ty_{c})+\omega_{cool}c_{cool}(P_{T})+ \omega_{oper}c_{oper}(H_{l},P_{f}) \tag{8}\]  
where \(c_{conc}\), \(c_{cool}\), and \(c_{oper}\) are normalized volumetric costs (\(c\in[0,1]\)) for, respectively, concrete, cooling, and operation; and \(\omega_{conc},\omega_{cool},\omega_{oper}\) are weigths, such that \(\omega_{conc}+\omega_{cool}+\omega_{oper}=1\). According to [PERSON] et al. [15], the values of these weights depend on factors, such as, the type of construction, local conditions, variations in the unitary costs, etc. However, regardless of the uncertainty of these unitary costs, it is always possible to estimate them when the construction is being planned. The cost function is bounded by the limits:  
\[f(\mathbf{X})\in[f_{min},1]\quad;\quad 0<f_{min}<1 \tag{9}\]  
where \(f_{min}\) is the minimum value that can be reached by \(f(\mathbf{X})\), regardless if the structure cracks or not, and can be easily calculated using an adequate set of variables \(\mathbf{X}\).  
The cost of the concrete can be computed by summing the costs of its components, such as binders, water, aggregates, and chemical additives.  
The cost for cooling the concrete depends on many factors, such as the type of building, the construction site, and the availability of the facilities to produce ice. The higher the placing temperature, the lower the cost, and this cost is zero when the placing temperature is the ambient temperature.  
The operational cost is an increasing function of the construction time \(t_{cons}\). The evaluation of this function is not an easy task and should be done on the basis of the experience of the contractor. For the sake of simplicity, the following linear function was used to represent the normalized operational cost:  
\[c_{oper}=\frac{t_{cons}}{t_{cons,max}} \tag{10}\]
## 4 Method for Implementing a 4 \(\times\) 4 Correlation Matrix  
### Corellation Characteristcs of the BS  
Radio waves that arrive at the BS from the MIMO mobile terminal have a narrow angular spread and are incident from various directions depending on the position of the MIMO terminal [29]; thus, the BS correlation matrix is determined by the arrangement of the BS antennas, the direction of the incident wave, and the angular spread of the incident waves. In a 4 \(\times\) 4 MIMO system, the realized 4 \(\times\) 4 correlation matrix has six correlation values (\(\rho_{12}\), \(\rho_{13}\), \(\rho_{14}\), \(\rho_{23}\), \(\rho_{24}\), and \(\rho_{34}\)). These correlation values are converted into the six distances between the virtual point sources, as explained in Section 3, and it is necessary to properly arrange the four virtual point sources to achieve the six BS correlations.  
For generality, Figure 5 shows a model in which four BS antennas are arranged arbitrarily. In the figure, the wavelength numbers denote the distances between the antennas. The four BS antennas are isotropic antennas. When the angular spread of the incident wave is sufficiently narrow, the spatial correlation (absolute value of the complex correlation) between the antennas can be calculated as follows [31]:  
\[\rho=\left|\exp\left(j\frac{2\pi d}{\lambda}\cos\phi_{s}-\frac{4\pi^{2}d^{2} \sin^{2}\phi_{s}\sigma^{2}}{2\lambda^{2}}\right)\right|, \tag{5}\]  
where \(d\) is the distance between the BS antennas, \(\phi_{s}\) is the angle of the incident wave relative to the line that connects the individual BS array, and \(\sigma\) is the angular spread of the incident wave. In this paper, the 4 \(\times\) 4 correlation matrix was calculated assuming \(\sigma=1.5^{\circ}\)[29].  
Figure 4: Virtual point source separation vs. desired BS correlation.  
In the case where the incident wave angle for the \(x\)-axis \(\phi\) is \(0^{\circ}\), the spatial coefficient between BS #1 and BS #2, \(\rho_{12}\), is 0.987 (using Equation (5)), and the distance between the virtual point sources of BS #1 and BS #2 to realize \(\rho_{12}\) = 0.987 is 0.037 \(\lambda\) (using the bisection method mentioned in Section 3). Similarly, \(\rho_{13}\), \(\rho_{14}\), \(\rho_{23}\), \(\rho_{24}\), and \(\rho_{34}\) are 0.614, 0.805, 0.713, 0.885, and 0.947, then \(d_{13}\), \(d_{14}\), \(d_{23}\), \(d_{24}\), and \(d_{34}\) are 0.209 \(\lambda\), 0.144 \(\lambda\), 0.177 \(\lambda\), 0.109 \(\lambda\), and 0.074 \(\lambda\), respectively.
## 6 Evaluation
### Evaluation of Overhead Introduced by SA-UBA  
#### 6.3.1 Evaluation of User-Access Process  
In the User-Access Process, the network bandwidth overhead is commonly used to evaluate the user's experience of accessing resources on the cloud. This experiment compares the network overhead of direct accessing (connecting resources with mstsc.exe) and accessing through Privileged Account Management subsystems. Three types of user  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Behavior Type** & **Number of Operations** & **Correct Recognition** \\ \hline System Operations & 100 & 78 \\ File Modifications & 100 & 73 \\ Resource Access & 100 & 70 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy of user behavior recognition.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Process** & **Minimum Time** & **Maximum Time** & **Average Time** \\ \hline Text detection & 0.09 s & 2.63 s & 1.06 s \\ Text recognition & 0.23 s & 9.67 s & 1.53 s \\ Whole process & 0.32 s & 11.74 s & 2.59 s \\ \hline \hline \end{tabular}
\end{table}
Table 5: The time cost of user behavior recognition.  
Figure 9: The accuracy of the original model and the improved model.  
behaviors are used in this test: (1) Edit: User opens a word processor (such as notepad, Microsoft Word, etc.) and enters text; (2) Browse: User visits some web pages through the browser on cloud hosts; (3) Video: The user uses video applications or plays video files. The researchers repeat each operation 10 times and calculate the average of the network bandwidth overhead in 30 s.  
The results of this experiment are shown in Figure 10; this figure compares the peak, valley, and average values of the bandwidth overhead when users perform three operations. The peak values gap between direct accessing and accessing with the Privileged Account Management subsystem is within 0.04 Mbps, and the valley values gap is within 0.03 Mbps. These gaps come from factors such as network instability or bandwidth changes. The gap of the mean network bandwidth overhead is not more than 0.06 Mbps. In other words, the additional bandwidth overhead brought by the Privileged Account Management subsystem is less than 5.7%. In practice, this additional overhead will not affect users' normal access to cloud resources.  
As a secure access system for cloud platforms, the ability to handle multi-user access requests is crucial. The resource occupation of the Privileged Account Management subsystem is shown in Figure 11. This experiment tested the CPU and memory usage of the secure server during multi-user access. The experimental results show that a server with a dual-core CPU and 4.0 GB RAM can support 50 users to access at the same time. When 20 users access at the same time, the CPU and memory usage is about 60%, which is the normal load of the security server.