esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Model Description
### Target Problem and Objective Function  
Generally, an objective function (or a norm) refers to a construction that serves for evaluating the performance of an optimization result. The users may choose their own desired properties of an idealized road traffic flow. Examples include [82] the lowest average travel time, the minimal queuing time, the lowest car density, the maximal average vehicle speed, the number of vehicles transferred, etc. An important challenge is to identify such a setup of signal durations and phasing, which leads to the desired idealized result (assuming that the traffic flow is known).  
Mathematically, the objective function is a formulation that takes a vector of functions at its input and returns a single scalar value. Let us consider a functional \(f\) of the input vector \(\mathbf{x}(t)\) as \(f(\mathbf{x},t)\). Then, if all the state variables and their mutual relations are known as well as the functional is analytically describable and differentiable for all the state variables, the minimum of the functional would be reached under the following condition:  
\[\delta\int_{t0}^{t1}f(\mathbf{x},t)=0, \tag{1}\]  
where \(\delta\) is the variation of the functional \(f(\mathbf{x},t)\). In other words, derivatives of a function with respect to all state variables and time are zero:  
\[\frac{\partial f(\mathbf{x},t)}{\partial x_{i}}=0,\quad\frac{\mathrm{d}f( \mathbf{x},t)}{\mathrm{d}t}=0. \tag{2}\]  
However, this is not the case for the problem at hand, as it is infeasible to describe the relations between all the state variables and their impact on the target solution. We argue that the discussed problem is that of the NP-hard nature [83] and the mentioned solution principle cannot be applied. Moreover, minding the dimensions of the configuration space, any combinatorial approach would be effectively non-applicable either.  
In the scenarios presented below, we consider the average travel time \(t_{\text{avg}}\) as the objective function:  
\[t_{\text{avg}}=\frac{1}{N_{\text{vehicles}}}\sum_{i=0}^{N_{\text{vehicles}}-1} \left(t_{\text{out},i}-\ t_{\text{in},i}\right)\rightarrow\text{min}, \tag{3}\]  
where \(N_{\text{vehicles}}\) is the count of all the vehicles transferred, \(t_{\text{in},i}\) is the entering time of a particular vehicle, and \(t_{\text{out},i}\) is its exiting time. One could also consider using the total sum of vehicle travel times as the norm, since dividing by a constant number of traveling vehicles does not affect the solution point.
# No-Reference Quality Assessment of Transmitted Stereoscopic Videos Based on Human Visual System
###### Abstract  
Provisioning the stereoscopic 3D (S3D) video transmission services of admissible quality in a wireless environment is an immense challenge for video service providers. Unlike for 2D videos, a widely accepted No-reference objective model for assessing transmitted 3D videos that explores the Human Visual System (HVS) appropriately has not been developed yet. Distortions perceived in 2D and 3D videos are significantly different due to the sophisticated manner in which the HVS handles the dissimilarities between the two different views. In real-time video transmission, viewers only have the distorted or receiver end content of the original video acquired through the communication medium. In this paper, we propose a No-reference quality assessment method that can estimate the quality of a stereoscopic 3D video based on HVS. By evaluating perceptual aspects and correlations of visual binocular impacts in a stereoscopic movie, the approach creates a way for the objective quality measure to assess impairments similarly to a human observer who would experience the similar material. Firstly, the disparity is measured and quantified by the region-based similarity matching algorithm, and then, the magnitude of the edge difference is calculated to delimit the visually perceptible areas of an image. Finally, an objective metric is approximated by extracting these significant perceptual image features. Experimental analysis with standard S3D video datasets demonstrates the lower computational complexity for the video decoder and comparison with the state-of-the-art algorithms shows the efficiency of the proposed approach for 3D video transmission at different quantization (QP 26 and QP 32) and loss rate (1% and 3% packet loss) parameters along with the perceptual distortion features.  
quality assessment; stereoscopic video; disparity index; human visual system; no-reference  
2022  
2022  
Published: 7 October 2022
## 4 Discussion
Therefore, we included depression as a potential confounder. Although the temporal lobe is thought to be primarily involved with cognition and emotion regulation [43; 44], several studies have found both structural and functional abnormalities of the temporal lobe among patients with primary insomnia [45; 46; 47; 48]. [PERSON] et al. found that both patients with subthreshold and clinically diagnosed insomnia exhibited increased GMV of the right middle and inferior temporal gyrus relative to healthy controls and that GMV was correlated with emotional score [48]. Our findings suggest that the association between sleep quality and temporal lobe volume may be mediated by sleep-dependent emotional processing. Although our findings show an association between sleep disturbance severity and GMV alterations in CSVD patients, the cross-sectional study design limits inferences of causation. The relationship between sleep disturbance and brain alterations is complex and bidirectional. Sleep disturbance might aggravate the CSVD burden and vice versa. For instance, some basic experiments have demonstrated that sleep facilitates the proliferation of oligodendrocyte precursor cells and myelination [49; 50]. Furthermore, shorter sleep duration influences the microstructural integrity of white matter by impairing oligodendrocyte function [51; 52]. Additionally, sleep disturbance is related to many pathophysiological processes, such as neuroinflammation, metabolite clearance, and oxidative stress, which may affect brain structure [53; 54; 55]. Further, WMH and lacunes may contribute to sleep disturbance through the destruction of sleep-related cortical-subcortical circuits in CSVD patients [56; 57]. In addition to the cross-sectional nature of this study, other limitations should be acknowledged. First, the sample size is relatively small, which may limit our statistical power. Second, sleep quality was measured using a self-report questionnaire instead of objective measures (e.g., polysomnography), which may have resulted in bias. Finally, in order to improve the diagnostic accuracy of poor sleep quality, borderline cases were excluded from the present study. Overall, the current study provides preliminary findings. Future longitudinal studies with larger sample sizes are required to further explore the mechanisms underlying the comorbidity of CSVD and sleep disturbance.
# Spot Market Cloud Orchestration Using Task-Based Redundancy and Dynamic Costing
## 5 Empirical Testing
### Simulation Environment  
To empirically test the performance of our proposed algorithm, we extended the open-source project Cloud Reliability Simulator, which we had previously developed in [6], adding the logic of our proposed algorithm (Algorithm 1) as a new _Orchestrator_.  
Cloud Reliability Simulator is built using Python 3. It uses a discrete time simulation methodology to model the failure of cloud microservice containers [6; 34]. The key components of the software are the _Simulator_, _Cloud_, _Microservice_, _MicroserviceContainer_, and _Orchestrator_ classes (Figure 5).  
The Simulator class is responsible for running the simulation environment, determining when to cause _MicroserviceContainers_ to fail according to their failure probability functions. It also manages the collection and aggregation of data from the simulation. The _Cloud_ class represents a particular cloud configuration being tested, which in our case is the analog of a task. The _Microservice_ and _MicroserviceContainer_ classes represent their namesakes in our model, as per Figure 4. Finally, the _Orchestrator_ class drives the simulation, with its children implementing the desired orchestration functionality. In our case, we implemented the functionality of Algorithm 1 in the _SpotMarketOrchestrator_ class.  
We also added a _SpotMarketProvider_ to represent the dynamic market behavior during the simulation. The _SpotMarketOrchestrator_ queried the _SpotMarketProvider_ regularly to determine the applicable spot price for the simulated cloud computing resource, which in our simulation was modeled as the execution time of the _MicroserviceContainer_ classes.
# A Review of Indoor Air Quality in Social Housing Across Low- and Middle-Income Countries
## 3 Results of the Literature Survey
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Category** & **Reference** & **Year** & **Location** & **Study Aim** \\ \hline [PERSON] and [PERSON] [35] & 2022 & Mumbai, India & & Study low indoor air velocities and their impact on evaporative cooling in Mumbai. \\ \hline [PERSON] et al. [36] & 2024 & Mumbai, India & & Assess densification’s effects on IAQ and health in Mumbai chawls using CFD and wind tunnel experiments. \\ \hline [PERSON] \& [PERSON] [37] & 2019 & Malaysia & Assess DHAPC system’s effectiveness in reducing PM2.5 and PM10 levels. \\ \hline [PERSON] et al. [38] & 2024 & Kampala, Uganda & Explore the relationship between NV, IAQ, and health improvement in Kampala. \\ \hline [PERSON] et al. [39] & 2016 & Bogota, Colombia & & Investigate carbon monoxide levels from gas appliances across income levels. \\ \hline [PERSON] et al. [40] & 2023 & Indonesia & & Link respiratory issues to formaldehyde and TVOC levels in newly constructed affordable housing. \\ \hline [PERSON] et al. [41] & 2019 & Colima, Mexico & & Improve thermal comfort and IAQ in Colima housing using bioclimatic strategies. \\ \hline [PERSON] et al. [42] & 2022 & Ecuador & & Assess building modifications for IAQ and comfort in Ecuador’s diverse climate zones. \\ \hline [PERSON] et al. [43] & 2024 & Brazil & & Assess vegetated walls for improving IAQ and thermal comfort in affordable Brazilian housing. \\ \hline [PERSON] \& [PERSON] [44] & 2024 & Indonesia & & Examine renovations to address ventilation and infrastructure deficiencies in subsidized housing. \\ \hline [PERSON] \& [PERSON] [45] & 2020 & Mumbai, India & & Improve airflow and IAQ through furniture and ventilator arrangement optimization in Mumbai housing. \\ \hline [PERSON] et al. [46] & 2024 & Mexico & & Optimize energy efficiency in Mexican housing with Energy Plus simulations. \\ \hline [PERSON] \& [PERSON] [47] & 2020 & Malaysia & & Evaluate low-cost filter materials in the DHAPC system for reducing indoor pollutants. \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline  
The temporal patterns of the IAQ studies, listed in Table 2, over the period 2013-2024 are shown in Figure 2. IAQ research has steadily increased over time, with notable growth observed in 2024, showing a heightened global focus on IAQ in social housing, particularly in LMICs. The themes of R&M and HC&SeFs accounted for significant contributions in 2024, highlighting the growing interest in architectural and behavioral interventions. However, CS remains underrepresented throughout the timeline, suggesting a need for
# Optimization of Truck-Drone Parcel Delivery Using Metaheuristics
## 4 Results
### Results of the Proposed GRASP Algorithm for TSP-D
For a valid comparison, 160 instances were used for each model. A statistically significant difference was detected between the means of the two models (\(p<0.001\)). Based on this, the HCLS variant was considered to perform better than the SA variant. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Size** & **Cost** & **Runtime** & \(Swap\) & \(TwoOpt\) & \
\begin{tabular}{c} \(Swap\) \\ Success \\ \end{tabular} &
\begin{tabular}{c} \(TwoOpt\) \\ Success \\ \end{tabular} \\ \hline \multicolumn{8}{c}{**Uniform Distribution**} \\ \hline \multirow{4}{*}{\(\alpha=1\)} & 10 & 283.71 \(\pm\) 1.91 & 3.37 & 3185.49 & 2948.50 & 1173.77 & 21.21 \\  & 20 & 366.08 \(\pm\) 0.74 & 15.94 & 12,194.30 & 10,935.50 & 4031.44 & 140.95 \\  & 50 & 561.93 \(\pm\) 58.04 & 87.76 & 25,107.27 & 23,404.50 & 4050.01 & 221.54 \\  & 75 & 656.76 \(\pm\) 94.22 & 282.62 & 42,641.81 & 40,542.60 & 4415.07 & 269.36 \\  & Avg. & 467.12 \(\pm\) 38.73 & 97.42 & 20,782.23 & 19,457.80 & 3417.57 & 163.27 \\ \hline \multirow{4}{*}{\(\alpha=2\)} & 10 & 242.09 \(\pm\) 9.60 & 2.54 & 4597.52 & 4152.01 & 1590.50 & 349.87 \\  & 20 & 321.88 \(\pm\) 5.34 & 12.25 & 15,570.18 & 13,879.04 & 4604.19 & 711.19 \\  & 50 & 568.37 \(\pm\) 94.31 & 60.01 & 30,890.69 & 26,462.18 & 7064.08 & 2919.46 \\  & 75 & 648.30 \(\pm\) 113.28 & 164.62 & 54,327.14 & 52,851.94 & 5357.96 & 4451.74 \\  & Avg. & 445.16 \(\pm\) 55.88 & 59.86 & 26,346.38 & 24,336.29 & 4654.18 & 2108.07 \\ \hline \multicolumn{8}{c}{**Single Center**} \\ \hline \multirow{4}{*}{\(\alpha=1\)} & 10 & 380.46 \(\pm\) 7.74 & 3.29 & 3557.43 & 3272.93 & 1174.02 & 269.90 \\  & 20 & 556.99 \(\pm\) 3.09 & 16.33 & 11,851.55 & 10,657.87 & 3878.31 & 137.46 \\  & 50 & 787.78 \(\pm\) 64.34 & 85.13 & 25,643.55 & 23,858.43 & 4372.77 & 219.97 \\  & 75 & 1058.13 \(\pm\) 89.51 & 244.23 & 42,661.80 & 40,628.15 & 4396.45 & 262.65 \\  & Avg. & 695.84 \(\pm\) 41.17 & 87.24 & 20,928.58 & 19,604.30 & 3455.39 & 222.50 \\ \hline \multirow{4}{*}{\(\alpha=2\)} & 10 & 295.49 \(\pm\) 9.24 & 2.51 & 4231.81 & 3831.19 & 1437.96 & 293.26 \\  & 20 & 428.09 \(\pm\) 12.55 & 12.33 & 15,760.87 & 14,192.32 & 4427.88 & 2114.55 \\ \cline{1-1}  & 50 & 723.88 \(\pm\) 107.93 & 58.21 & 32,542.07 & 31,949.98 & 4799.59 & 4409.37 \\ \cline{1-1}  & 75 & 1030.92 \(\pm\) 104.36 & 154.65 & 55,666.13 & 57,962.20 & 5123.6 & 6297.56 \\ \cline{1-1}  & Avg. & 619.60 \(\pm\) 58.52 & 56.92 & 27,050.22 & 26,983.92 & 3947.26 & 3278.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results obtained with the proposed GRASP approach, SA variant.
## 2 MIMO Antenna Design Approaches
### Diversity Gain (DG)  
Diversity gain indicates the quality and reliability of a MIMO antenna in wireless systems. Hence, the DG of the MIMO antenna must be high (\(\approx\)10 dB) within the acceptable frequency band. The DG is calculated using the ECC value and can be given by Equation (2) [4,11]:  
\[\text{DG}=10\times\sqrt{1-\left|\text{ECC}_{qp}\right|^{2}} \tag{2}\]
## 3 Modelling and Simulation
### Mode #1 Harmonic Supply and Linear R-L Load
#### 3.1.2 5-Phase Network and Linear RL Load  
Parameters for steady states at \(t\in\langle 0.0;0.1\rangle\) and at \(t\in\langle 0.1;0.2\rangle\) are given in the Table 8:  
\(U_{\text{rms}}=230\) V, \(f=50\) Hz, 5-phase system.  
Power components average values, classical calculus are shown in a Table 9.  
Simulation Results, Simulation Step 200 \(\mu\)s  
Measurable quantities i.e., voltages and currents in \(\alpha,\beta\)\((rms)\), and their parameters are given in Table 10.  
Figure 8: Apparent—(**a**), active—(**b**), blind—(**c**), and distortion (**d**) power components during transient states of induction motor.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Load** & \(R\) [\(\Omega\)] & \(L\) [mH] & \(Z\) [\(\Omega\)] & \(\tau\) [ms] & \(\boldsymbol{\varphi}\) [\({}^{\circ}\)el.] & \(\boldsymbol{\cos\varphi}\) [\(-\)] & \(\boldsymbol{\sin\varphi}\) [\(-\)] \\ \hline before & 18.4 & 43.93 & 23 & 2.39 & 36.76 & 0.8 & 0.6 \\ after & 9.2 & 43.93 & 16.59 & 4.78 & 56.33 & 0.5545 & 0.8322 \\ \hline \hline \end{tabular} \(U_{\text{rms}}=230\) V, \(f=50\) Hz, 5-phase system.  
\end{table}
Table 8: Load parameters for steady states before and after load change.  
As can be seen, the calculated and simulated values of power components are nearly the same. Network voltages and currents in \(a,b,c\)- system during transient, Figure 9a,b.  
Apparent, active, blind, and distortion powers during transient states are shown, due to comparison to 5- and 2-phase systems, in Figure 11a-d.
## 2 Methods
### Satellite Remote Sensing Monitoring
Recently, the high spatial resolution and freely available information coming from the Multispectral Imager (MSI) on-board Sentinel-2 satellite has been used to facilitate the two-dimensional (2D) mapping of lava flows [71] through a new Machine Learning (ML) classifier, which discriminates the recent lava flows from pre- and post-eruptive multispectral images acquired by MSI, combined with pre-eruptive digital topography. Bands 2, 3, 4, and 8 at the spatial resolution of 10 m are used as input to the classifier. This ML approach relies on two steps: (i) a k-medoids unsupervised classifier separating input data in clusters whose pixels have the same properties; and, (ii) a Bayesian neural network mapping recent lava flows. In particular, the first step reveals pixels undergoing similar changes in time between pre- and post-eruptive images, adopting the correlation distance as a measure of similarity. Subsequently, in the second step, a small representative subset of each cluster is exploited to train the BNN, so that it provides us with the pixels belonging to the recent lava flow. The advancement of satellite remote sensing techniques also has great potential for what concerns the three-dimensional mapping of volcanic products. Indeed, high spatial resolution data acquired in stereo, tri-stereo, or multi-view configuration (e.g., Pleiades, PlanetScope, ASTER) can be used to frequently update the topography and to estimate volcanic deposits by differencing successive topographies. Such estimates can improve the 2D mapping of lava flows while providing an independent maximum bound to lava flow volume that can be derived from the satellite infrared data [59; 64; 72; 73]. We exploited the Pleiades constellation, which is composed of two optical satellites, Pleiades 1A and 1B, respectively, launched on December 2011 and 2012, in order to retrieve areas, volumes, and thickness distribution of the recent volcanic deposits in Stromboli. These satellites provide images at 50 cm spatial resolution in stereo and tri-stereo mode [74]. The 3D processing of the Pleiades imagery was performed using the free and open source MicMac photogrammetric library (available at [[http://micmac.ensg.eu](http://micmac.ensg.eu)]([http://micmac.ensg.eu](http://micmac.ensg.eu))), in this way 1-m Digital Elevation Models (DEMs) were obtained. We derived two 1-m DEMs to further constrain the volume of volcanic deposits: a pre-eruptive one from the tri-stereo optical imagery acquired on 8 October 2019, and a post-eruptive one from the Pleiades-1 images of 7 April 2020. We differentiated them, so to obtain the thickness distributions of volcanic deposits emplaced between October 2019 and April 2020. The two DEMs were first co-registered using the Nuth and Kaab [75] method in order to avoid any errors that could derive from a misalignment.
## 6 Conclusions  
In this paper, 3.7 kW WCS is designed and modeled. In particular, the coupling coils are designed with detail and verified to guarantee the accuracy of the simulation model. Through simulation and analysis, it is verified that when the coils are completely aligned and in different offsets, the mutual inductance and coupling coefficient are significantly decreased. Along with the vehicle body, magnetic field distributions of the primary coil and the secondary coil at full alignment and five offsets are compared and analyzed at these typical test points. It is found that magnetic field strength of test point A3 located on the left side of the driver exceeds the limit due to the offset. Magnetic field strength at A3 is larger than 30.4 A/m required by EMF exposure standard at 40% offset and 50% offset. Finally, the sideband currents and the odd-multiplied harmonics generated by the power devices in WCS are analyzed, and the influence on the magnetic field distribution and the radiation emission caused by the higher harmonics are proved to show that there remains some risks in the system. The experimental results of magnetic field distribution have good agreement with simulation results.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Offset**} & \multicolumn{4}{c}{**Experimental Results**} & \multicolumn{4}{c}{**Simulation Results**} \\ \cline{2-7}  & **0 mm** & **180 mm** & **Error** & **0 mm** & **180 mm** & **Error** \\ \hline
84.8 kHz & 1.82 & 2.13 & 0.31 & 1.75 & 2.05 & 0.3 \\ \hline
85.2 kHz & 1.81 & 2.02 & 0.21 & 1.76 & 1.95 & 0.19 \\ \hline
255 kHz & 0.54 & 0.58 & 0.04 & 0.54 & 0.58 & 0.04 \\ \hline
435 kHz & 0.11 & 0.13 & 0.02 & 0.10 & 0.13 & 0.03 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Magnetic field strength in the \(xy\) direction of sideband and harmonic currents with 30% offset (A/m).  
Figure 24: Magnetic induction intensity (pT) measurement of A3: (**a**) \(y\)-axis; (**b**) \(z\)-axis.  
**Author Contributions:** [PERSON] and [PERSON] designed the methodology and wrote the manuscript. [PERSON] and [PERSON] conceived and designed the experiment. [PERSON] implemented the experiments. All authors contributed to improving the quality of the manuscript.  
**Funding:** This paper was supported by National Key R&D Projects (2017 YFB0102402) and the National Natural Science Foundation of China for financially supporting this project (51475045).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Signal Separation Based on Knowledge Representation
###### Abstract  
The separation of mixed signals typically requires appropriate prior assumptions, while traditional signal separation methods struggle to describe the differences in separation targets with significant features. This paper proposes a signal separation framework based on knowledge representation, where separation targets are represented with knowledge, guiding the branches of autoencoders for signal separation. Firstly, under the proposed knowledge representation framework, corresponding knowledge representations are obtained based on observed mixed signals. Secondly, the number of branches of the autoencoder is determined based on the number of separation target signals. Then, utilizing the results of knowledge representation, a branch autoencoder network is constructed, with branches guided by knowledge to achieve the separation of target sub-signals. Finally, a self-encoding network architecture is constructed with a combination of observation signal reconstruction error and knowledge-guided error constraints. Through numerical simulations on a layered velocity model, the Marmousi-II geological model, and the MNIST dataset, the proposed method is validated by comparing the numerical energy differences between predictions and ground truths, demonstrating its effectiveness under both limited and ample data conditions.  
s +
Footnote †: journal: Applied sciences  
signal separation; knowledge representation; deep learning