esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Types of Supplementary Dampers and Their Characteristics
### Viscous Dampers
#### 3.1.3 Key Characteristics  
Viscous dampers exhibit several key characteristics that define their behavior and applications. They demonstrate velocity-dependent behavior, meaning their response is directly related to the speed of movement. These dampers produce an out-of-phase response with displacement, typically showing a 90\({}^{\circ}\) phase lag for linear dampers. While they can be sensitive to temperature changes, this can be mitigated through careful fluid selection and the implementation of thermal compensation systems. Linear viscous dampers exhibit frequency independence, making them effective across a wide range of vibration frequencies. Viscous dampers are known for their high energy dissipation capacity, allowing them to efficiently reduce structural vibrations. Importantly, they add minimal stiffness to the structure, which can be advantageous in many design scenarios.
# Robust Beamforming Based on Graph Attention Networks for IRS-Assisted Satellite IoT Communications
## 4 Simulation and Numerical Results
### Simulation Parameter Setting  
We use four attention heads (\(H=4\)) and adopt three FCs (\(D=3\)) in the proposed network. The names of the FC layers are denoted as \(f_{1}\),\(f_{2}\) and \(f_{3}\), respectively. In addition, we set \(a_{k}\) as a 10-dimensional vector (\(F=10\)) that contains the priority information and the location information of user k, IRS and LEO satellites. The parameters of all layers are summarized in Table 1.  
For network training, we use the Adam optimizer with an initial learning rate \(\alpha=0.001\), the number of maximal epochs \(E_{p}\) is set to 350, and for each epoch, we generate \(I_{ite}=100\) iterations to update the weights of the network. The batch size \(N_{b}\) is set to 1000. To accelerate the convergence, the learning rate decays by a factor of 0.3 when the validation loss does not decrease for 5 consecutive epochs. Due to the statistical properties of the channel and the noise in the uplink pilot transmission, all the calculation results are generated based on averaging over 1000 instances.  
For the considered IRS-assisted LEO satellite system, we assume that the LEO satellite altitude is 1000 km and that the satellite is equipped with \(M=8\) antennas. An IRS with 64  passive element locations at (0,0) and height 20 m. IRS is configured as an \(8\times 8\) uniform rectangular array. There are 6 terrestrial users uniformly distributed in a square area of [0, 200] m \(\times\) [0, 200] m. We set the length of the uplink pilots to \(L=20\) for each user, and the user's transmission power to 15 dBm. The uplink channels from users to IRS, from IRS to the LEO satellite, and from users to the LEO satellite are generated according to the channel model discussed in Section 2. The details of the coefficients are given in Table 2.
## 3 DSM Use Case
### Mapping of Use Case to SGAM Layers
In practice, standards like EN 50090, EN 14908, IEEE 1901.2-2013, IEEE 1901-2010 can be found (the list of standards mentioned at this point is not exhaustive). It is considered that there is no information exchange between the Home Automation End-Device and the appliance, since the appliance is not smart and there is only an electrical signal between the two actors defining whether the appliance will be on or off and set by the Home Automation End-Device.
# An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise
## 3 Experimental Validation
### Experimental Setup  
We carried out the experiments using the MyMediaLite RS library [65], which includes an implementation of the IKNN model [14] along with a host of other classical RS algorithms [15; 66; 67; 68; 69; 70]. To test the item-similarity functions presented in Section 2.2, we predicted user ratings for the Star Trek television series episode data presented in Section 2.5. For evaluation, the ratings in the data were randomly divided into two parts: 70% for training the model and 30% for testing it. For the item cold-start scenario, the training-test partitions were drawn by splitting the items (not the ratings) and selecting those partitions covering roughly 30% of the total ratings in the test partition. The selected performance measure is the root-mean-square error (RMSE) as measured between the actual ratings and the predictions produced by the RS method in the test partition. Next, we produced 30 shuffles of such partitions and evaluated the RMSE of each method for each partition. The final reported result for each model is the average RMSE across the 30 training-test partitions. We used the Wilcoxon rank-sum test statistic to assess the statistical significance of the observed differences in performance between methods. The RS methods to be tested are classified into four groups according to the resources they use. The first three categories are variations of the IKNN method presented in Section 2.1, in which we substitute the \(s_{ij}\) of Equation (1) by the different item similarity functions presented in Section 2.2. In practice, each variation produces a text file containing on each line the identifiers of two items and their corresponding similarity score. Then, the MyMediaLite application is instructed to use this file as source for computing the similarities between items. For the sake of a fair comparison, the system (\(\mu\)), item (\(b_{i}\)), and user (\(b_{i}\)) biases (see Equation (2)) are used in all the IKNN algorithm variations tested. The fourth category comprises an assortment of CF methods and baselines that are used to ensure for a comprehensive comparison. Below, we present a summary of the methods used in the experiments: \begin{tabular}{l l} CBF & These methods use the data and preprocessing procedure described in Section 2.4. \\  & TFIDF is implemented using Equations (3) and (4), and LSI is implemented by using transcripts: \\ \end{tabular}   
Figure 3: Star Trek television series episode IMDb user ratings distribution as of December 2018.  
performing SVD, as described in Section 2.2.1, on the document-term matrix obtained from the data. The number of latent factors was varied from 10 to 100 in increments of 10. Both approaches are implemented using the Gensim (Gensim: Topic modelling for humans (2019). URL: [[https://radimrehurek.com/gensim/](https://radimrehurek.com/gensim/)]([https://radimrehurek.com/gensim/](https://radimrehurek.com/gensim/)). (Online; accessed 30 June 2019)) text processing library [71].
# Smart Contract-Based Right-of-Way Trade with Game Theory in Blockchain-Enabled Internet of Vehicles
## 3 A Crossroad Passing Model
### Green Light Duration  
In this part, we discuss the green light duration for a certain direction, which represents the time for vehicles on that road to pass the crossroad continuously. To think about fairness, the maximum and minimum green light durations of a single direction are strictly limited. Suppose that the green light time of the \(x^{\text{th}}\) passing order direction road is \(t_{phase,(x)}\), whose upper and lower bounds are \(g_{(x),min}\) and \(g_{(x),max}\), respectively. Thus,  
\[g_{(x),min}\leq t_{phase,\ (x)}\leq g_{(x),max}. \tag{6}\]  
1. Minimum Green Light Duration The minimum green light time is limited to the single passing of the convoy that originally received this passing order, which does not allow any other convoy to follow. In other words, its minimum is only the time for convoy \(F_{(x)i}\) to pass.
2. Maximum Green Light Duration Assume that the start time for the \(x^{\text{th}}\) passing order direction road to pass is \(T_{(x)s}\), the actual end time is \(T_{(x)e}\), and the longest end time for allowance is \(T_{(x)e,max}\). So, \[T_{(x)e,max}=T_{(x)s}+g_{(x),max}.\] (7) Suppose that the time when the last vehicle of the next convoy \(F_{i+1}\) on the same road leaves the crossroad stop line is \(T_{i+1}\). If \(T_{i+1}>T_{(x)e,max}\), then \[t_{phase,(x)}=T_{(x)e}-T_{(x)s}.\] (8) It is guaranteed that all the convoy \(F_{(x)i}\) will pass the crossroad at this time, and that convoy \(F_{i+1}\) cannot follow it and will get the latest passing order \((y+1)\). If \(T_{i+1}<T_{(x)e,max}\), then \[t_{phase,(x)}=T_{i+1}-T_{(x)s}.\] (9) The convoy \(F_{i+1}\) receives the passing order \((x)\) and passes the crossroad together with the convoy \(F_{(x)i}\). This section mathematically models the convoy passing crossroad problem. A convoy follows the arrival order at the crossroad and passes sequentially. In terms of overall passing efficiency, a convoy following criterion is designed. A maximum green light duration is designed for fairness. Several methods for determining a convoy's passing order are based on the principle of minimizing the total passing time at the crossroad. However, the value of the same elapsed time may be different for different vehicles. Some vehicles consume more fuel per minute, and some are more sensitive to time which is presented as the time cost factor \(\alpha\), etc. For example, a fully loaded bus is more time-sensitive than a private car with only one passenger. This is a common situation in road transportation. So, if you are time-sensitive and need to get to your destination as soon as possible by navigating a crossroad surrounded by convoys, how to arrive speedily is a problem.
# An Accelerated Method for Protecting Data Privacy in Financial Scenarios Based on Linear Operation
## 3 Proposed Methods
### Level of Protection and Attacker Assumptions
#### 3.2.1 User Identity Protection  
As seen in Figure 2, this method uses a hash algorithm to process user ids and user device IDs. Since hash computation is irreversible, it can effectively protect the privacy of the user's identity.
# Extremely Low-Profile Monopolar Microstrip Antenna with Wide Bandwidth
## 4 Conclusions  
An extremely low-profile monopolar microstrip antenna with broad operating bandwidth was discussed and demonstrated based on a new design approach, that is, controlling the unique degenerated TM\({}_{02}\) modes from a simple substrate, a single-layered substrate and only a single resonator structure. The degenerated TM\({}_{02}\) modes of the proposed antenna can be easily optimized by the periodically arrayed patch resonator with a meshed ground structure and the cavity resonance from the thickness of substrate between the top patches and bottom meshed ground structure. Due to the broad bandwidth of the proposed antenna, the numbers of swarm senor network performances can be expanded. In addition, the extremely thin substrate thickness of the proposed antenna that can even be bent flexibly may reduce the surface drag dramatically for the high-speed moving sensor nodes, such as ground vehicles, UAV swarm and LEO satellite constellations. Finally, the proposed antenna may be further studied in multiple polarization operations for the military and civil wireless communication system as an RF sensor application.  
Conceptualization, Y.H. and S.L.; methodology, Y.H.; validation, S.L., J.-i.J. and S.P.; formal analysis, S.P.; investigation, S.L.; resources, Y.H.; data curation, Y.H.; writing--original draft preparation, Y.H.; writing--review and editing, S.P.; visualization, S.L.; supervision, J.-i.J.; project administration, S.P.; funding acquisition, S.P. and S.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) (2021R1A2C2011560) and in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2016R1D1A1B04932071).  
Not applicable.  
The authors declare no conflict of interest.
## 4 Calibration Methodology
### Building Calibration
#### 4.1.1 Method  
In the first step, data are collected from the test sites at a 15 min sampling rate. Collected features include weather parameters (outside temperature, ground temperature, relative humidity, irradiance, atmospheric pressure, wind speed), indoor climate (room temperatures and relative humidity), as well as technical equipment parameters (water temperature and flow, on/off status) and electric consumption disaggregated by sources. Standard data pre-processing techniques are applied to the collected data to improve their usability, namely: gap reconstruction (via interpolation), removal of sensor malfunction periods, and on/off status reconstruction for technical systems for which this signal was not made directly available.  
In a second step, the buildings are modelled and their envelope calibrated using the collected data. It uses free oscillation data, i.e., periods where the HVAC equipment is off to eliminate HVAC interference. As described in depth in [24], envelope calibration is realized through a parametric analysis, a sensitivity analysis and a genetic algorithm simulation (using the NSGA-II genetic algorithm; see [25]) guided by an appropriate objective function (based on normalized root mean square error (NRMSE), determination coefficient, and normalized mean bias error (NMBE)).  
In a third step, HVAC and technical equipment are introduced and configured to supply the building demand. A detailed HVAC model is added to the previously calibrated envelope model and simulated in EnergyPlus. Known HVAC equipment parameters are set to technical specification values, different performance curves are determined for each of the components, while unknown parameters are set either based on technical information of similar equipment, or calculated based on test site data. This detailed model undergoes a new calibration process similar to the one used for the envelope, i.e., a parametric/sensitivity analysis followed by a genetic algorithm simulation guided by a new objective function. This calibration is performed until the simulation model uncertainty indices are acceptable within the expected KPIs; see Section 4.1.2.
# Improving Streaming Video with Deep Learning-Based Network Throughput Prediction
## 3 Theoretical Background
### Quality Measures  
DASH systems strive to achieve the best possible QoE by selecting optimal video bit rates in given network conditions. This selection involves a few conflicting issues. Firstly, a user expects that the streaming video will have the highest possible bit rate available  
Figure 2: An example operation of a simplified version of the MSS algorithm. At times \(t_{1}\) and \(t_{2}\), network throughput is high enough to increase the video bit rate (2e). At \(t_{3}\), the buffer level, which is above the threshold, allows increasing the video bit rate again (2a). At times \(t_{4}\) and \(t_{5}\), network throughput drops, which causes the video bit rate to decrease (2c). At time \(t_{6}\), the buffer level drops below the threshold, and the video bit rate is reduced again (2b). After \(t_{6}\), network throughput is not high enough to increase the video-bit rate (2d).  
for the given network throughput. Secondly, the playback should be smooth, which a player achieves by avoiding frequent bit rate changes. Thirdly, a player should minimize the times when the video freezes on a user's screen due to the player's buffer depletion. Having identified the three main components of QoE, namely bit rate, bit rated variation and rebuffering time, we combine them into a single measure, where the QoE is calculated as a weighted sum of the parameters of each video segment in the range from 1 to \(N\):  
\[\text{QoE}=\sum_{i=1}^{N}q_{i}-\lambda\sum_{i=1}^{N-1}|q_{i+1}-q_{i}|-\mu\sum_ {i=1}^{N}(K(b_{i})),\]  
where \(\lambda\) and \(\mu\) are positive weights corresponding to video quality variations and rebuffering time. To count the buffer's starvation periods, we introduce the penalty function \(K(b_{i})=1,b_{i}=0\) and \(K(b_{i})=0,b_{i}>0\).  
Video stalls disturb users more than bit rate switches do; thus, the value of \(\mu\) is usually much higher than \(\lambda\). A small \(\lambda\) indicates that bit rate switches are less nuisance for a user, while a large \(\mu\) implies that the user is more concerned about rebuffering.  
To obtain time- and video-independent measures with possibilities of comparison with different algorithms, we adapt from [26] a normalized QoE model  
\[\text{nQoE}=\frac{\text{QoE}}{\text{QoE\_ref}}, \tag{4}\]  
where QoE_ref is the QoE of another benchmark algorithm.
## 4 Results and Discussion
### Nanofluid Characterization
#### 4.2.1 The Thermal Conductivity of the Nanofluids  
Figure 6a clearly shows that the \(k_{NF}\) increases as the TiO\({}_{2}\) NP volume concentration of the NFs increases, as well as temperature. This trend confirms that the addition of TiO\({}_{2}\) NPs to the base fluid (i.e., deionized water) increases the thermal conductivity of the resulting TiO\({}_{2}\)-W NFs. Over the test temperature range, the NFs containing 1.5 and 0.2 \(v\)/\(v\)% TiO\({}_{2}\) NPs exhibited an enhancement in thermal conductivity of 11-12% and 4-7%, respectively, compared to the base fluid. Figure 6a also shows the nonlinear dependence of the \(k_{NF}\) on the NP volume concentration. This behavior was observed in the experimental data obtained at 25, 35, and 45 \({}^{\circ}\)C. In addition to this, Figure 6b compares the experimental data on the effective thermal conductivity obtained at 35 \({}^{\circ}\)C with the predictions obtained by two of the classical models. These models are defined as follows:  
The [PERSON] [38] model,  
\[k_{NF}=k_{BF}\Bigg{[}\frac{k_{NP}+2k_{BF}+2\phi(k_{NP}-k_{BF})}{k_{NP}+2k_{BF}- \phi(k_{NP}-k_{BF})}\Bigg{]} \tag{6}\]  
The Hamilton and Crosser (H-C) [39] model,  
\[k_{NF}=k_{BF}\Bigg{[}\frac{k_{NP}+(n-1)k_{BF}-(n-1)\phi(k_{BF}-k_{NP})}{k_{NP} +(n-1)k_{BF}+\phi(k_{BF}-k_{NP})}\Bigg{]} \tag{7}\]  
where \(n\) is the empirical shape factor, which is defined as \(n=3/\psi\), with \(\psi=1\) for spherical NPs, \(k_{BF}\) and \(k_{NP}\) are the thermal conductivities of the base fluid and the NPs, respectively, and \(\phi\) is the NP volume fraction in the NFs.  
Classical models of the effective thermal conductivity of two-component materials, such as those expressed in Equations (6) and (7), typically predict a linear dependence on the NP volume concentration. However, the TiO\({}_{2}\)-W NFs used in this study proved not to follow this trend after experimental measurements, as evidenced in Figure 6b. It can be seen that the \(k_{NF}\) measurements are significantly greater than those predicted by the Maxwell and H-C models (both model predictions overlap). Also, the measured nonlinear dependence differs from that of the Wasp [40] model, the Bruggeman [41] model, and the effective medium theory [42]. Nevertheless, the nonlinear behavior obtained in the \(k_{NF}\) measurements is similar to that reported by [PERSON] et al. [43].
## 2 Materials and Methods
### Flow Cytometric Analysis of Cell Phenotype  
CRC cell lines and dendritic cells were stained with the following cocktail of monoclonal antibodies purchased from BD Biosciences, USA: anti-CD29-APC (clone MAR4, IgG1x), anti-CD44-FITC (clone C26, IgG2 bx), anti-CD95-PE (clone DX2, C3H/Bi IgG1x), anti-FasL Biotin (clone NOK-1, IgG1) coupled with Streptavidin-APC, anti-CD11c-APC (clone S-HCL-3, IgG2b), anti-CD80-PE (clone L307, IgG1x), anti-CD83-APC (clone HB15e, IgG1x), anti-HLA-DR-PerCP (clone L243, IgG2a). Anti-CD133/2-PE (clone 293C3, IgG2 bx) monoclonal antibodies were purchased from Miltenyi Biotec. After 30 min of incubation in the dark, samples were fixed with PBS containing 1 mM EDTA and prepared for further analyses. Flow cytometric analyses were performed using FACS Calibur flow cytometer (BD Biosciences, Franklin Lakes, NJ, USA) with BD CellQuest Pro software. During the analysis the dead cells and debris were excluded on SSC/FSC dot plot. Next, populations expressing particular specific surface markers were distinguished and measured. Unstained cells were used to set a threshold of positive signal. Data are presented as mean fluorescent intensity (MFI) related to unstained control MFI value.
# A Method to Improve the Distribution of Observations in GNSS Water Vapor Tomography
###### Abstract  
Water vapor is an important driving factor in the related weather processes in the troposphere, and its temporal-spatial distribution and change are crucial to the formation of cloud and rainfall. Global Navigation Satellite System (GNSS) water vapor tomography, which can reconstruct the water vapor distribution using GNSS observation data, plays an increasingly important role in GNSS meteorology. In this paper, a method to improve the distribution of observations in GNSS water vapor tomography is proposed to overcome the problem of the relatively concentrated distribution of observations, enable satellite signal rays to penetrate more tomographic voxels, and improve the issue of overabundance of zero elements in a tomographic matrix. Numerical results indicate that the accuracy of the water vapor tomography is improved by the proposed method when the slant water vapor calculated by GAMIT is used as a reference. Comparative results of precipitable water vapor (PWV) and water vapor density (WVD) profiles from radiosonde station data indicate that the proposed method is superior to the conventional method in terms of the mean absolute error (MAE), standard deviations (STD), and root-mean-square error (RMS). Further discussion shows that the ill-condition of tomographic equation and the richness of data in the tomographic model need to be discussed separately.  
GNSS remote sensing; atmospheric sounding; water vapor tomography; meteorology +
Footnote †: journal: Sensors