esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Verification of Improved BTS  
Due to space limitations, only wind speed, turbulence kinetic energy, and wind attack angle of case 1 as well as 2, without and with improved BTS are compared to verify its effect.  
It can be seen from Figure 10 that when there is improved BTS, wind speed distribution is more uniform and closer to that of the inflow wind speed profile. Besides, there is no flow separation near the boundary of the terrain model.  
Turbulence kinetic energy (TKE) is a measure of the intensity of turbulence and determines the ability of the flow to maintain turbulence or become turbulence, thus indicating flow stability. From Figure 11, it can be concluded that at the inlet of the terrain model without improved BTS, the TKE with BTS is smaller than that of the terrain model without BTS, which indicates that the improved BTS is able to reduce turbulence intensity induced by artificial cliffs and uplift the inflow gradually.  
Wind attack angles induced by artificial cliffs will have a significant influence on the wind attack angle at the bridge site, thus reducing wind attack angles at the inlet boundary hold a decisive place in wind characteristic prediction over complex terrain. As shown in Figure 12, the wind attack angle at the inlet boundary with the improved BTS is much smaller than that without BTS and is closer to  
Figure 11: Turbulence kinetic energy distribution along the inflow direction near the inlet of the terrain model without or with improved BTS from CFD simulation (unit: m\({}^{2}\)s\({}^{-2}\)). (**a**) Case 1 without BTS; (**b**) Case 2 without BTS; (**c**) Case 1 with BTS; (**d**) Case 2 with BTS.  
Figure 10: Wind speed distribution along the inflow direction near the inlet of the terrain model without or with improved BTS from CFD simulation (unit: m/s). (**a**) Case 1 without BTS; (**b**) Case 2 without BTS; (**c**) Case 1 with BTS; (**d**) Case 2 with BTS.  
zero, indicating that the improved BTS can reduce the wind attack angle caused by artificial cliffs to a great extent.  
In conclusion, by comparing wind speed, turbulence kinetic energy, and wind attack angle near the inlet of the terrain model without and with improved BTS, the BTS can effectively reduce the impact of artificial cliffs on wind characteristics after the inlet boundary.
## 2 Materials and Methods
### Equilibrium Studies  
A fixed amount of adsorbent (0.1 g) was added to 250 mL Erlenmeyer flasks containing 100 mL of different initial concentrations (30, 60, and 90 mg/L) of nitroimidazole solutions, without adjusting the pH. Experiments were carried out at initial pH values that ranged from 2 to 10. The initial pH of the solution was adjusted to the desired value by adding either HCl or NaOH solution. The effect of the presence of electrolytes on the adsorption was analyzed by increasing the concentrations of KCl (0.005-0.1 M). At equilibrium, the concentration of nitroimidazole was measured at 308 nm (RNZ), 317 nm (TNZ), and 320 nm (MNZ and DMZ), by a double beam UV-vis spectrophotometer (Model UV 1601; Shimadzu, Kyoto, Japan). Each experiment was conducted in duplicate, under identical conditions. For computing the amount of antibiotics adsorbed at equilibrium, \(q_{e}\) (mg/g), the following material balance was used:  
\[q_{e}=(C_{0}-C_{e})\left(\frac{V}{W}\right), \tag{1}\]  
where \(C_{0}\) and \(C_{e}\) are the initial and final equilibrium concentrations of nitroimidazole in mg/L, respectively. Also, W is the adsorbent mass (g), while \(V\) is the volume of the aqueous solution (L).
## 1 Introduction
In case a void-hole is unavoidable, the information is dropped after waiting for a predefined interval of time. Thirdly, the uniqueness of the time for which a node holds a packet ensures that nodes with low battery power hold packets for short time. In addition, nodes with no or a low count of neighbors hold the packets for a long time so that enough neighbors are available to them; due to the movement of nodes with sea tides and currents, to further forward packets. These strategies reduce the packet loss and, in effect, enhance reliability in packets' advancement to the sea surface. The Co-ESRVR algorithm adds cooperative routing to ESRVR so that the destination receives a number of copies of the packets. These packets are further processed by the destination. In cooperative routing, the source-relay-destination triplet participates more in data routing than the ordinary source-destination pair. This feature reduces the harsh effect of the channel on the packets due to space diversity, which is not addressed in the ordinary source-destination pair. Consequently, Co-ESRVR further enhances the reliability of ESRVR by transferring packets to the sea surface over multiple links rather than the single link of source-destination in ESRVR. Moreover, the proposed algorithms do not make use of the geographical information of sensor nodes, which reduces complexity in nodes' deployment and, therefore, the network scalability is improved.
## 2 Materials and Methods
### Geospatial Data Enhancement  
To produce more accurate predictions, several geospatial features have been added to the dataset. As outlined by [PERSON] et al. [46], it is possible to enhance our understanding of buildings using technology that enriches simpler geospatial data.  
This geospatial information is obtained from the 2D GIS and alphanumerical data of the cadastre through programming, combining the available information to obtain the following characteristics of the buildings:  
* Facades and Party Walls  
An important feature is to differentiate between the building surfaces in contact with the external air (facades) and the surfaces in contact with adiabatic elements (party walls). The surface area in contact between buildings is calculated in GIS, knowing the building's floor plan geometry and its number of floors, both data obtained from the Spanish cadastre. A standard story height of 3 m is assigned for the calculation, following the methodology developed in [47]. For better machine learning training, these data are expressed as a percentage of facade surface area relative to the total envelope area.  
* Envelope and Compactness  
Knowing the building's floor plan geometry and its number of stories, the square meters of the envelope and the cubic meters of volume are calculated. Compactness is calculated according to Spanish legislation [48], considering the entire envelope in contact with the external air and the ground and excluding the party wall surface area. The formula used is Equation (1).  
\[Compactness=\frac{V}{\sum A_{i}}^{\text{m3}/\text{m2}} \tag{1}\]  
where:  
\(V=Volume\) _enclosed by the energy envelope_  
\(A_{i}=Area\) _of the envelope in contact with external air or the ground_
# Displacement-Dependent Damping Inerter System for Seismic Response Control
## 2 Theoretical Analysis of Displacement-Dependent Damping Inerter System (DDIS)  
### Mechanical Model  
The inerter (in Figure 1) is a mechanical element that ideally provides a force proportional to its inertance \(m_{in}\) (having dimensions of mass) and relative acceleration between its two terminals. Experimental research works show that the inertance can be amplified thousands of times that of the physical mass of the inerter [48], proving that the inerter is a very effective vibration mitigation device owing to this mass enhancement effect. A series of physical realizations have been proposed for the construction of the inerter, such as the rack-pinion [21], the ball screw [1], the fluid [49], and the electromagnetic mechanisms [50]. In the ideal model of the inerter, the output force of the inerter \(F_{in}\) is given by [51; 52; 53; 54; 55]:  
\[F_{in}=m_{in}\Big{(}\ddot{u}_{1}-\ddot{u}_{2}\Big{)} \tag{1}\]  
where \(\ddot{u}_{1}\) and \(\ddot{u}_{2}\) are the respective accelerations of the two terminals.
# A Review on Head-Related Transfer Function Generation for Spatial Audio
## 8 Conclusions and Future Directions
Moreover, exploring hybrid approaches that combine perceptual metrics with statistical optimisation could yield more robust and user-friendly solutions for personalised spatial audio experiences. Conceptualisation, S.C. and T.L. ; investigation, V.B., A.V., A.Q. and L.G. ; writing--original draft preparation, V.B. and S.C.; writing--review and editing, V.B., L.G., N.A.D., A.Q., A.V., T.L. and S.C.; supervision, S.C. and T.L. All authors have read and agreed to the published version of the manuscript. This work was partially funded by the European Union - Next Generation EU. Project Code: ECS00000041; Project CUP: C43C22000380007; Project Title: Innovation, digitalisation and sustainability for the diffused economy in Central Italy-VITALITY. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript:  
AR Augmented Reality  
ARD Adaptive Rectangular Decomposition  
ASM Active Shape Model  
BEM Boundary Element Method  
BRIR Binaural Room Impulse Response  
CNN Convolutional Neural Network  
CVDD Continuous Variable Digital Delay  
DNN Deep Neural Network  
DTW Dynamic Time Warping  
EF Eigentransfer Function  
FDTD Finite-Difference Time-Domain  
FEM Finite Element Method  
FIR Finite Impulse Response  
FLiP Functional Link Polynomial  
FMBEM Fast Multipole Boundary Method  
FMM Fast Multipole Method  
FoV Field of View  
GAN Generative Adversarial Network  
GPU Graphic Processing Unit  
GRNN Generalised Regression Neural Network  
HAT Head and Torso  
HOSVD High-Order Singular Value DecompositionHRIR & Head-Related Impulse Response & HRTF & Head-related Transfer Function & IDW & Inverse-Distance Weighting & IIR & Infinite Impulse Response & ILD & Interaural Level Difference & IMU & Inertial Measurement Unit & IPTF & Inter-Positional Transfer Function & IRS & Inverse Repeated Sequence & ITD & Interaural Time Difference & JND & Just Noticeable Difference & LN & Legendre Non-linear & LS & Least Square & LSD & Log-Spectral Distortion & LTI & Linear Time-Invariant & MLP & Multilayer Perceptron & MLS & Maximum Length Sequence & MP & Minimum Phase & MSE & Mean Square Error & NLMS & Normalised Least Mean Square & OPS & Orthogonal Periodic Sequence & PCA & Principal Component Analysis & PML & Perfectly Matched Layer & PPS & Perfect Periodic Sequence & PRTF & Pinna-Related Transfer Function & RBF & Radial Basis Function & RMSE & Root Mean Square Error & SARITA & Spherical Array Interpolation by Time Alignment & SCH & Spherical Cap Harmonic & SConvCNP & Spherical Convolutional Conditional Neural Process & SD & Spectral Distortion & SDE & Spectral Difference Error & SH & Spherical Harmonic & SHD & Spherical Harmonic Decomposition & SLAM & Simultaneous Localisation and Mapping & SPCA & Spatial Principal Component Analysis & SVD & Singular Value Decomposition & TCN & Temporal Convolutional Network & UWVF & Ultra-Weak Variational Formulation & VBAP & Vector base Amplitude Panning & VQ-VAE & Vector-Quantised Variational Autoencoder & VR & Virtual Reality & WN & Wiener Non-linear & XR & Extended Reality &
## 3 DA-Based Turbo Equalization
### Soft Information-Based ADFE  
As shown in Figure 4, the process of soft information-based ADFE consists of signal filtering represented by solid lines and adaptive coefficient adjustment indicated by dashed lines.
# Solar Radiation Parameters for Assessing Temperature Distributions on Bridge Cross-Sections
## 4 Estimation Model for Solar Radiation in Fujian  
### Estimation Model for Daily Global Solar Radiation  
As discussed earlier, the Angstrom-Page equation [10; 11] was chosen in this study to predict the daily global solar radiation for different cities in Fujian. The daily global solar radiation (averaged over one month) for the entire Fujian cannot be determined because only two meteorological stations in Fuzhou and Jian'ou provide such data. Instead, the daily global solar radiation (averaged over  
Figure 3: Hourly solar radiation: (**a**) measured global solar radiation; (**b**) measured diffuse solar radiation; and (**c**) calculated beam solar radiation.  
one month) from 1983 to 2005 provided in a U.S. NASA database was used in this analysis, since the NASA database is used by many researchers when meteorological data are lacking [54; 55; 56]. The NASA database divides the map of China into grids based on the longitude and latitude and provides data for each grid point. The validity of the data in NASA database has been verified by comparing with solar radiation data from 88 meteorological stations in China measured before 2010 [57].  
The daily astronomical solar radiation can be calculated using earth-sun distance, solar elevation, duration of sunlight and so on [11]. The historical average sunshine duration (averaged over one month) since 1951 for four cities in Fujian (Nanping, Fuzhou, Yong'an and Xiamen) can be obtained from the National Meteorological Information Center, China Meteorological Administration [58]. The average maximum possible sunshine duration (averaged over one month) can be calculated using the following equation:  
\[S_{0}=(2\omega_{\rm s}/15)\times(180/\pi) \tag{7}\]  
where \(\omega_{\rm s}\) is the sunset hour angle. The empirical coefficients \(a^{\prime}\) and \(b^{\prime}\) for different cities in Fujian can be calculated by using a linear regression analysis between the clearness index \(H/H_{\rm C}\) and the sunshine duration fraction \(S/S_{0}\).
## 4 Branch-and-Price-and-Cut Algorithm
### Master Problem
In this case, we can construct a completely different solution by exchanging Truck1's Route1 for Truck2's Route2, as shown in Figure 5. To be specific, in the newly constructed solution, Truck2 with an empty container can directly travel to serve A without taking a detour to the depot. Following this, it seamlessly continues its journey along Route1, which should have belonged to Truck1. Likewise, Truck1 can directly proceed to serve B and continue on the route Route2. It is straightforward to know that the total cost of this new solution is either equal to or potentially even better than that of the supposed optimal solution. **Case 2:** The truck waits at the depot for other trucks to retrieve an empty container and transport it away from the depot. This occurs when the depot is full of empty containers. Similar to Case 1, a new solution can be formed by exchanging partial routes between trucks, which is not inferior to the supposed optimal solution. **Case 3:** A truck is stationed at the depot and waits for the next customer's time window to open. In this situation, the truck can immediately leave the depot and wait patiently at the customer's site until its scheduled time window becomes available. To summarize, routes contained in the supposed optimal solution can be adjusted to create an alternative routing plan with no trucks having waiting time at the depot. The newly derived routing plan performs at least comparably to, if not better than, the supposed one. This contradicts our previous assumption. Therefore, the proposition holds. This proposition also implies that critical times are the earliest times when trucks arrive at the depot from somewhere else. Therefore, it guides the determination of critical times and the generation of feasible truck routes. Figure 5: Case 1: Two truck routes are exchanged to generate new routes without waiting at the depot. There are only \(|N_{IF}\cup N_{OF}\cup N_{OE}\cup N_{IF}^{P}\cup N_{IF}^{D}\cup N_{E}^{P}|+2|T_{ critical}|+1\) constraints in the master problem. Nevertheless, the number of feasible truck route is extremely large. Consequently, attempting to enumerate all of the truck routes might be impracticable. Furthermore, it is not required to list all the truck routes since the optimal solution only contains at most \(K_{truck}\) truck routes. Therefore, we introduce a branch-and-price-and-cut algorithm to solve the master problem. In this paper, the algorithm initializes the master problem by considering only a small subset of columns and relaxing the integrality of the master variables along with constraints (24) and (26), namely the RLMP. To progressively improve the RLMP, we iteratively incorporate advantageous columns with negative reduced costs into the RLMP by solving the following pricing subproblem.
## 3 Precise Positioning Using Uncombined PPP (UPPP) Algorithm
### Analysis of GNSS Observations from Different Logging Apps
#### 4.1.2 Carrier-Phase Observations with No Change over Time  
Another problem was also observed in the RINEX files saved by the GnssLogger App; which is the existence of some carrier-phase observations with no changes over time. Such a phenomenon was observed for the Xiaomi Mi8 and Samsung S20, while this was not the case for the Google Pixel 5 dataset. Figure 10 provides the C/N0 records for the mentioned GPS PRNs along with the epochs in which the carrier-phase observations have no changes over time for the Xiaomi Mi8 and Samsung S20, depicted in the left and right panels, respectively. They are shown with the blue dots. The number of such satellites was more for the Xiaomi Mi8 compared to the Samsung S20. Those PRNs mostly belonged to the lower C/N0 values. A C/N0 mask is usually set to 15-25 dB-Hz, however, there are still some of those epochs with the C/N0 larger than the threshold. It therefore needs further attention than just masking the lower C/N0. As given in Equation (3), the carrier-phase observation can be obtained from \(AccumulatedDeltaRangeMeters\) variable (abbreviated as ADR) from the GNSSMeasurement class. As mentioned before, checking the validity of the  
Figure 9: CMP and carrier-phase predicted error for GPS PRNs 01 (Google Pixel 5).  
carrier-phase measurements by using the AccumulatedDeltaRangeState variable is crucial. By looking at the ADR logs from the Xiaomi Mi8 and Samsung S20, it is revealed that these questionable epochs are mainly related to the invalid ADR. However, it is not observed in the Geo++ RINEX file, meaning that it properly handled the invalid ADR by excluding them. In the converted RINEX, such an issue cannot be seen.  
Concisely, the results confirmed the importance of evaluating the logging Apps before employing them since these logging Apps are the basics of any smartphone positioning algorithm development. In the next subsection, the UPPP positioning accuracy obtained from the three RINEX files are assessed in the post-processed mode within a kinematic experiment.
## 4 Improved ACO for Global Path Planning
### Improved ACO Algorithm Flow  
An improvement to the ACO is proposed. The computational complexity of the improved algorithm is \(O(n^{2})\). The flow chart of the improved ACO is shown in Figure 7. The improved algorithm steps are as follows:  
1. Establishing a grid map based on the surrounding environment and initializing parameters of the algorithm. Set the number of ants \(m\), number of iterations \(N\), current iteration \(n\), information heuristic factor \(\alpha\), expectation heuristic factor \(\beta\), initial pheromone, pheromone evaporation rate \(\rho\), pheromone intensity coefficient \(Q\), starting point, and ending point.
2. Place ants at the starting point and add the starting point to the Tabu list. According to (1), calculate the transition probability between nodes \(ij\), select the next node \(j\) by the roulette method, and add the current node to the Tabu list.
3. Determine whether the ant reaches the ending point. If it is, calculate the length by path nodes recorded in the Tabu list. If it is not, continue to search for the next node until it reaches the ending point. Loop all the ants in this generation until travel fin-ished and go to step 4.
4. Calculate the _Iter_ and Value of the ant colony according to the Equations (6) and (7). They are used to adjust \(\alpha\) and \(\beta\) in the fuzzy algorithm. Determine whether the algorithm falls into the local optimum. If it does, change \(\rho\) by using Equation (12). If it does not, do not change \(\rho\). Update the pheromone in stages according to the strategy of Equation (11).
5. Determine whether the iteration number of the algorithm satisfies \(n\geq N\). If it does, go to Step 6; otherwise, go to step 2. Let the ants start the cycle again from the starting point.
6. The outcome is the optimal path, the iterative convergence curve, and the length of the shortest path. End the algorithm. Figure 6 shows a flow chart of the improved ACO algorithm.
# The Challenges of Advanced CMOS Process from 2D to 3D
## 3 Process Integration of New Transistor Architecture
### High-\(k\) Dielectric and Metal Gate (HKMG)
#### 3.6.2 Atomic Layer Deposition of Metal Gate
developed TiAlX films as metal gate by thermal ALD technique using titanium tetrachloride (TiCl\({}_{4}\)), trimethylaluminum (TMA) and NH\({}_{3}\). It was demonstrated that NH\({}_{3}\) presence in the TiCl\({}_{4}\) and TMA reaction resulted in the film more like TiAlN(C) while its absence made the film turn to TiAlC. The TiAlC films have smaller effective work function than the TiAlN(C) films [82]. The effective work function can be tunable from 4.49 eV to 4.79 eV by tuning growth temperature, TMA dose and film thickness [83]. Further studies showed that the effective work function of TiAlC films can be decreased by increasing Al concentration. [PERSON] et al. introduced a new precursor triethylaluminum (TEA) to react with TiCl\({}_{4}\). TEA is a metal organic precursor with special \(\beta\)-hydrogen, which can lead to H\({}_{2}\) elimination. The H\({}_{2}\) elimination will generate Al intermediate, which can decompose more easily at high temperatures and further enhance Al doping into the final product [84]. The effective work function of the TiAlC with TEA as precursor can be tunable from 4.46 eV to 4.24 eV by adjusting the growth temperature and the film thickness. TaAlC based on TMA and TEA has almost the same effective work function as TiAlC based on TMA and TEA separately [85; 86]. However, for filling metal in the transistor dummy gate trench, W metal using ALD has a good capability of step coverage to instead traditional physical vapor deposition (PVD) Al materials applied in the small pitch device. There are two types of ALD W films depending on SiH\({}_{4}\) and B\({}_{2}\)H\({}_{6}\) precursors. The related research showed that the properties of the ALD W were precursor-dependent due to crystalline quality and induced strain of the deposited films [87; 88; 89].
# Underground Mine Road Detection Using Deep Learning Technique
## Materials and Methods
By changing the backbone network, the real-time performance of the network model is improved, and can also achieve better real-time performance within the complex and changeable environment of the underground mine road. The improved BiSeNet mainly consists of a spatial path and a contextual path. First of all, the lightweight network short-term dense concatenate is used as the backbone network of the encoder in the space path, and the original three sets of convolutional layers are replaced. The replaced backbone is composed of STDC modules. The number of deep feature channels is small, and the number of shallow feature channels is large. The STDC module uses skip connections to enrich features, so that the lower layers have enough channels to encode more detailed information with smaller receptive fields, and the higher layers with larger receptive fields strengthen the expression of high-level information. The backbone network composed of STDC modules can reduce the calculation amount of the model network and improve the calculation speed. A unified attention fusion module (UAFM) is added to the context path, which enriches the feature representation. The UAFM improves the detection level of underground mine slopes through channel and spatial attention. The semantic segmentation network model of the improved BiSeNet is shown in Figure 3; the red box of the space path is the lightweight backbone network STDC, and the red box of the context path is the UAFM. Figure 2: BiSeNet network.