esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Overview  
### Problem Definition  
This paper assumes the driving condition in which human drivers and autonomous vehicles coexist. Figure 2 represents the driving scene assumed in this paper. There are human drivers around the autonomous vehicle, which is equipped with embedded measurement devices, such as a GPS tracker and laser scanners used to acquire the data on movements of surrounding vehicles. The sensing range is assumed to be within 120 m. The relative speed and distance of adjacent vehicles can be acquired. The proposed method is implemented in the autonomous vehicle.  
This paper assumes a situation when one of adjacent vehicles cuts in the front space of the autonomous vehicle, therefore reproducing one of the main factors of a crash. The future position of the cut-in vehicle for a time horizon of 2 s is predicted, and the collision risk is derived from the prediction result. The collision risks with respect to not only the cut-in vehicle, but also to the following vehicle, should be considered. If the autonomous vehicle immediately decelerates to maintain a distance from the cut-in vehicle, the rear collision can occur. Therefore, the risk assessment towards the two vehicles is strongly required.  
In this paper, the autonomous vehicle is defined as ego, and eight adjacent vehicles are defined, as shown in Figure 2b. The ego vehicle is indicated using green color, and the eight adjacent vehicles are depicted using blue color. The yellow vehicles represent non-target vehicles out of the coverage of the proposed method. The maximum number of considered adjacent vehicles is eight. In the figure, LF represents the following vehicle on the left lane, LA is the alongside vehicle on the left lane, and LP denotes the preceding vehicle on the left lane. FO is the following vehicle on the same lane of the ego vehicle; and PR represents the preceding vehicle of the ego vehicle. In the same way, RF represents the following vehicle on the right lane; RA is the alongside vehicle on the right lane, and RP denotes the  
Figure 2: Problem definition: the green vehicle represents the autonomous vehicle, and the blue ones are the adjacent vehicles, which are the targets of the proposed method. The yellow vehicles are not considered in the proposed method. (**a**) The autonomous vehicle has measurement devices used to acquire the data on the distance and speed of the adjacent vehicles. (**b**) The autonomous vehicle is defined as ego, and the maximum number of considered adjacent vehicles is eight.  
preceding vehicle on the right lane. The ego vehicle is always monitoring the adjacent vehicles while estimating their driving intentions and predicting their trajectories.
# Interference Challenges and Management in B5G Network Design: A Comprehensive Review
## 4 Interference in B5G Networks
### Device-to-Device (D2D)
#### 4.2.5 Related Work in D2D
The RA issue contains allocating spectrum RBs and selecting an idle user to act as a relay to aid the D2D links communication, whereas the purpose of PA is to minimize inter-user interference and enhance the QoS of communication. The analytical formulas for the aggregate throughput were derived for the suggested scenario. The analytical expression demonstrated that choosing an appropriate IU as a relay for each DU, distributing spectrum RB, and managing power for each IU, DU, and CU are three main factors affecting the aggregate throughput of CDHN under the constraint of the Cus' throughput demands. The simulation results stated that the suggested algorithm outperforms competing algorithms for various system parameters. Nonetheless, when the transmission power of BS increases, the interference to Dus also increases, which led to a reduction in the total throughput of the suggested scenario. The summary of previous studies of D2D is presented in Table 3. \begin{table}
\begin{tabular}{p{71.1 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Issue** & **Methodologies** & **Advantages** & **Limitations/Future Work** & **Ref.** \\ \hline Minimize the main- and side-lobe interference as well as enhance the throughput in the mm-wave D2D network. & Side-lobe Interference Reduced vertex coloring & Improve the throughput per time slot significantly resource allocation. & The impact of the ICI, which affects the system throughput, was not considered. & [98] \\ \hline Minimize transmission power according to device allocation and beamwidth selection. & A model integrating a Gaussian directional antenna with a two-way channel. & -Minimize power transmission and interference. & -The different heights of transmitters that have a large influence on the D2D power optimization were not taken into consideration. & [99] \\ \hline Alleviate the interference and dead-zone problems for D2D-Enabled DL/UL Decoupled Het-Nets. & Decoupling user association (DUA) procedure by using UL fractional frequency reuse (FFR) scheme. & & The impact of intra-cell interference was ignored in CUEs in an outage. & [100] \\ \hline  & & Maximize SINR, ergodic capacity, and probability of success as well as minimize networks devoid of stochastic geometry. & The transmission power factor for the BSs, RNs, Cus, and D2D the outage probability for the D2D-enabled cooperative cellular network. & [2] \\ \hline Minimize the co/cross-tier interference between D2D and cellular communication in HetNet while maximizing the network data rate & One-to-many matching algorithm. & Realize the performance of the network nearby (93.7\%) at optimum performance with less overhead and complexity. & When the density of FBSs increased, co/cross-tier interferences on network communications also increased. This led to a decrease in the number of communications that reuse sub-channels. & [101] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The summary of previous studies of D2D in the literature.
# Multi-Objective BiLevel Optimization by Bayesian Optimization
## 3 Methodology
### Noise-Free Setting
```
Inputs:\(\mathbf{F}_{u}(\mathbf{x}_{u},\mathbf{x}_{i}):\mathbf{x}_{\mathbf{u}}\in\mathbb{X}_{u},\mathbf{x}_{i}\in\mathbb{X}_{l}\), Batch points per epoch \(q\), Number of iteration \(n\), Reference point
1: Initial decision data set \(D=\{\mathbf{x}_{u_{i}},\mathbf{F}_{u}(\mathbf{x}_{u_{i}},\mathbf{x}_{i}^{*}) \}_{i=1}^{n}\) with size of \(n\),
2:\(\mathbf{x}_{i}^{*}\) : Initialize best lower-Level decisions as parameters from NSGA-2 Algorithm,
3: Initialize multi-objective Gaussian model with observations \(\{\mathbf{x}_{u},\mathbf{F}_{u}(\mathbf{x}_{u},\mathbf{x}_{i}^{*})\}\)
4:for\(i=0:N\)do
5: Suggest new \(q\)-batch points by optimizing \(q\)-\(\mathit{EHVI}\) and \(q\mathit{ParEGO}\) acquisition functions
6:for\(j=0:q\)do
7: For each upper-level decision \(\mathbf{x}_{u}\), find optimal \(\mathbf{x}_{i}^{*}\) by applying NSGA-2 algorithm
8: Calculate fitness scores \(\mathbf{F}_{u}^{*}\) and \(\mathbf{t}_{u}^{*}\)
9:endfor
10: Update the data set \(D=\left(\mathbf{x}_{u_{i}},\mathbf{F}_{u}(\mathbf{x}_{u_{i}},\mathbf{x}_{i}^{* })\right)_{i=1}^{n}\)
11:endfor
12:Return Pareto-front \(\mathbf{F}_{u}^{*}\) and corresponding optimum variables \(\mathbf{x}_{u}^{*},\mathbf{x}_{i}^{*}\)
```  
**Algorithm 1** Upper-level Optimization
# An Electronically Adjustable Floating Memcapacitor Emulator Circuit Using CDBA
## 5 Conclusions  
In this paper, an electronically tunable memcapacitor emulator circuit designed using CDBA element is presented both in mathematical analysis, simulation and experimental aspects. The development of technology has led to the increasing use of memristive elements in various scientific and technological fields. Mem elements, which are applied in many fields such as communication, neural networks, chaos, biology etc., are currently not available in the market as a commercial two-terminal element, which emphasizes the importance of designing better emulators to emulate them. The main advantages of the CDBA-based memcapacitor emulator proposed in this study are that it has a floating structure without grounding restriction, does not contain a memristor in its structure, has both incremental and subtractive forms and has an electronically tunable feature. The designed emulator has successfully passed the tests of frequency-dependent stepwise charge-voltage and memcapacitance-voltage characteristics, single-serial-parallel connection characteristics, non-volatility, Fourier analysis and electronic tunability. The performance of the proposed memcapacitor emulator was tested in two different electronic circuit applications and we found that it has the potential to provide significant advantages in various electronic applications. According to the measurement results, the memcapacitor emulator has 25% better filtering ability and 27.93% less power consumption compared to the standard capacitor in the low-pass filter circuit. In addition, the emulator effectively performs the learning and data storage functions in the amoeba learning circuit. It is thought that further studies on the proposed emulator circuit will make significant contributions to the literature by evaluating various electrical properties such as signal speed, power efficiency, chaotic signal generation, signal regulation, and investigating additional benefits.  
Conceptualization, S.G.K. and S.E.H.; methodology, S.G.K.; formal analysis, S.G.K. and Z.G.C.T.; investigation, S.G.K.; writing--original draft preparation, S.G.K.; writing--review and editing, S.G.K., Z.G.C.T. and S.E.H.; visualization, S.G.K. and Z.G.C.T.; supervision, Z.G.C.T. and S.E.H.; project administration, S.G.K., Z.G.C.T. and S.E.H. All authors have read and agreed to the published version of the manuscript. This work is supported by the Inonu University Project of Scientific Research Unit (BAP) under the project number FDK-2022-3107.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Figure 16: (**a**) Amoeba’s learning circuit using memcapacitor emulator (\(R=1\) k\(\Omega\) and \(L=1\) mH). (**b**) Response of the amoeba’s learning application.  
**Conflicts of Interest:** The authors declare no conflicts of interest. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.
## 5 Testing and Experimental Configuration
### Metrics  
During the experiments we used different metrics to characterize the connectivity between the transmitter and receiver (See Table 4):  
* The packet delivery ratio (PDR), provides information about the reliability of the communication; the PDR is calculated as the number of packets received by the gateway with respect to the total number of packets sent, with a value of 100% implying success and a value of 0 implying no success [41].
* The received signal strength (RSSI) and signal-to-noise ratio (SNR) are two physical layer PHY-level indicators of LoRa available on-chip, which we use to characterize the signal quality [46].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{3}{c}{**Value/Configuration**} \\ \cline{2-4}  & **Node 1** & **Node 2** & **Node 3** \\ \hline
**Transmission** & Uplink & Uplink & Downlink \\ \hline
**Frequency** & 915 MHz & 915 MHz & 904 MHz \\ \hline
**Preamble** & 8 Symbol & 8 Symbol & 8 Symbol \\ \hline
**Transmission power** & 14 dBm & 14 dBm & 14 dBm \\ \hline
**Coding rate** & 4/5 & 4/5 & 4/5 \\ \hline
**Propagation factor** & 7, 9, 12 & 7, 9, 12 & 7, 9, 12 \\ \hline
**Bandwidth** & 125 kHz & 125 kHz & 125 kHz \\ \hline
**Package size** & 16 bytes & 8 bytes & 4 byte \\ \hline
**Antenna gain** & 2 dBi & 2 dBi & 2 dBi \\ \hline \hline \end{tabular}
\end{table}
Table 3: LoRa communication parameters.  
* The connectivity range represents the measured distance between the receiver and the transmitter. Our objective was to study the connectivity range in the agricultural fields of the Chirinche Bajo community.
# Improvement of Porous GaN-Based UV Photodetector with Graphene Cladding
## 2 Materials and Methods
### Fabrication of Porous GaN  
In this work, porous GaN was fabricated using a photo-electrochemical etching (PEC) method. Prior to that, a 2-inch GaN/sapphire wafer was diced into pieces of 12.7 \(\times\) 6.35 mm\({}^{2}\). The cleaved wafers were sonicated in isopropyl alcohol (IPA) and acetone for 5 min each to remove surface impurities. Next, they were washed in deionized (DI) water, and finally dried under N\({}_{2}\) stream.  
A two-electrode electrochemical setup was used to fabricate porous GaN. The GaN wafer served as the anode, while that of Pt wire was the cathode. Both electrodes were partially immersed in an electrolyte solution, consisting of a mixture of 0.5 M H\({}_{2}\)SO\({}_{4}\) and H\({}_{3}\)PO\({}_{4}\). A UV light source was illuminated on the GaN wafer prior to applying 20 V across the electrodes. Both processes are necessary to initiate the formation of porous morphology on the GaN wafer. The overall process lasted for 30 min. The anodized GaN wafer was subsequently removed and thoroughly washed with DI water in order to remove acidic residues.
# Real-Time Rain Rate Evaluation via Satellite Downlink Signal Attenuation Measurement
## 4 Direct Method for the Estimation of the Rain Rate
### Implementation of the Direct Method  
The direct method utilizes (5) with coefficients given by Table 2 and is estimated by means of nonlinear regressions applied to a large quantity of experimental data [12]. These coefficients are optimized for the specific climatic area where the experiment takes place (i.e., central Italy) and depend on electromagnetic parameters such as frequency and polarization. The method also relies on the knowledge of rain height, that can be evaluated as in (4), where the \(0\,^{\circ}\mathrm{C}\) isotherm height is obtained, e.g., from WRF short-term forecasts, and is adequately reduced by \(\Delta h\in(400\;\mathrm{m},800\;\mathrm{m})\). More specifically, the method envisages the following steps: (i) the SmartLNB measures the (negative) variation of \(E_{s}/N_{0}\) (see Section 4.6), due to the presence of rain, with respect to clear-sky conditions; (ii) this measurement is then used to calculate the downlink rain attenuation \(\hat{A}\); (iii) the length \(L\) of the slant path affected by rain is calculated by exploiting the knowledge of rain height and link geometry; (iv) the specific attenuation \(k\) is evaluated as \(k=\hat{A}/L\); (v) the rainfall rate is eventually estimated from (5) as (see Section 4.4)  
\[\hat{R}=\alpha\left(\hat{A}/L\right)^{\beta}=\Psi(\hat{A}) \tag{6}\]  
Further details are provided in the next section. A variant of the foregoing procedure consists of replacing the analytical relationship (5) with experimental curves or nomograms available in the literature [53], which provide specific attenuation as a function of frequency, rainfall rate and polarization. An example of such curves is shown in Figure 5. Once the transmission frequency is chosen, the cited curves/nomograms provide a sampled version of the specific attenuation vs. rainfall rate, and only some preliminary interpolation between points is needed to carry out step (v) of the method.
## 8 Conclusions and Perspectives  
### Conclusions  
In this review, we have observed that most of the advantages of using DT and XR technologies to improve industrial performance can also be applied to improve building exploitation, especially when using data extracted from the BIM of the building to create the DT. A BIM-based DT can be likened to a centralised database where the real-time collected data of an equipment and its static data, such as maintenance history and technical documentation, are linked to its 3D representation, which facilitates its retrieval when needed. During the exploitation of the building, these data can then be used to monitor the building and its equipment, improve inspection operations while facilitating access to equipment data, and use the prediction algorithms of the DT to plan the maintenance when needed.  
XR technologies can be used to visualise the equipment data directly in front of the equipment, but also specific information either provided by an expert or automatically added by an algorithm. These devices also allow for better data management using enhanced interaction methods such as gesture and voice recognition. Their built-in camera can also be used for image recognition to identify the inspected equipment. This information can then be used to detect discrepancies between the physical and the digital environments, but also to link inspection feedback to its digital representation [66].  
With the impact of COVID-19, it is now necessary to provide new methods to facilitate collaboration with a remote expert when needed [97]. Either using virtual annotations or a shared video, the XR devices provide remote experts with contextual information and advanced methods to display information to the on-site user. The position and the orientation of the on-site user in relation to the inspected equipment can also be shared with a remote expert via a digital avatar. Another aspect of this collaboration is the use of the centralised BIM-based DT, where various remote experts can provide their own data analysis and add meaningful information for maintenance procedures.  
Furthermore, DT was originally been proposed to show the real-time evolution of an asset. Mainly used in the manufacturing industry, this review shows that it can be expanded to represent the twin of an entire smart building. However, another concept has been emerging in recent last years: the concept of a _Smart City_. Some authors have already investigated how a city-scale DT can be created [30; 31; 52; 62] and used for individualisation [22] to specify the city assets, such as buildings.
# Graphene-Based Grid Patterns Fabricated via Direct Ink Writing for Flexible Transparent Electrodes
## 2 Experimental Details
### Characterizations and Testing Methods  
The surface and cross-sectional morphology of as-printed and hot-pressed graphene film were characterized by field emission scanning electron microscopy (FESEM, Nova NanoSEM 450, FEI, Portland, OR, USA). The light transmittance of grid-patterned graphene films at 400-800 nm was recorded by a UV-Vis NIR spectrophotometer (SolidSpec UV-3700, Tokyo, Japan). The sheet resistance of the single-track graphene film and grid-patterned  
Figure 1: (**a**) Picture of DIW device and (**b**) the design of grid-patterned graphene films.  
graphene film was measured using a Keithley 2450 source meter SMU (Tektronix Technology, Shanghai, China). The thickness of the graphene films was measured by a step meter (KLA-Tencor D120, Bruker DktakXT, Billerica, MA, USA). The average thickness was obtained by averaging the thickness measured at five different locations of each graphene film.
## 3 Simulation Analysis of CTEHS
### Analysis of Bifurcation Characteristics of System Static Solution
When \(M_{\rm e}\) is increased to 15 g, as shown in Figure 4f, the middle stable solution is separated from the fork point P. The fork point P turns into the saddle point S\({}_{2}\) and moves to the left, and the offset phenomenon of the middle stable solution becomes more obvious. 1.2 Influence of End Magnet Mass \(M_{\rm e}\) on the (\(d_{\rm v}\), \(u\)) Bifurcation Diagram  
Next, the influence of \(M_{\rm e}\) on the system's bifurcation behavior is studied in (\(d_{\rm v}\),\(u\)) plane. Taking \(d_{\rm h}\) = 10 mm, \(M_{\rm e}\) = 0,5,10,15 g, the bifurcation diagram of the system in the (\(d_{\rm v}\),\(u\)) plane is shown in Figure 5. When \(M_{\rm e}\) = 0 g, as shown in Figure 5a, two symmetrical saddle points, S, and a fork point, P, appear in the system's bifurcation diagram. At this time, the system has a total of three stable and two unstable solutions. When \(d_{\rm v}<d_{\rm vP}\), the system exhibits bistable characteristics, when \(d_{\rm vP}<d_{\rm v}<d_{\rm vS}\), the system behaves in a monostable state. Increase \(M_{\rm e}\) to 5 g, as shown in Figure 5b, the system's bifurcation diagram is no longer symmetrical up and down, the upper saddle point S shifts to the lower left to point S\({}_{1}\), the lower saddle point S shifts to the lower right to point S\({}_{2}\), the fork point P divides into two fork points P\({}_{1}\) and P\({}_{2}\), and the zero stable solution of the system shifts downward. When \(d_{\rm v}<d_{\rm vP2}\), the system behaves as bistable, when \(d_{\rm vP2}<d_{\rm v}<d_{\rm vS1}\), the system exhibits tristable characteristics, when \(d_{\rm vS1}<d_{\rm v}<d_{\rm vS2}\), the system behaves as bistable again, when \(d_{\rm v}>d_{\rm vS2}\), The system is a monostable system. Figure 4: Bifurcation diagram of (\(d_{\rm h}\), \(u\)) with different end magnet masses \(M_{\rm e}\): (**a**) \(M_{\rm e}=0\) g\({}_{2}\) (**b**) \(M_{\rm e}=3\) g; (**c**) \(M_{\rm e}=6\) g; (**d**) \(M_{\rm e}=9\) g; (**e**) \(M_{\rm e}=12\) g; (**f**) \(M_{\rm e}=15\) g.  
Continue to increase \(M_{\rm e}\) to 10 g and 15 g, as shown in Figure 5c,d, the upper saddle point S\({}_{1}\) keeps moving to the lower left and the lower saddle point S\({}_{2}\) keeps moving to the lower right under the action of the magnet gravity. At the same time, the fork point P\({}_{1}\) gradually moves to the left, P\({}_{2}\) gradually moves to the right, and the offset \(\Delta u\) of the middle stable solution becomes larger. The range where the system exhibits a tristable state gradually decreases, and the range where the system exhibits a bistable state gradually increases.
# Application of Blockchain Technologies in Verification of Software Metrics
## 3 Methodology
### Data Collection for Verification  
In order to accurately verify software metric values, it is essential to establish a precise and automated data collection process. This section outlines the methodological approach adopted in the developed software analysis system, which includes the following components:  
* methodology for source code analysis and software metric extraction;
* process of data verification using smart contracts;
* approach for retrieving data from the blockchain for result validation.  
The developed system utilizes an Abstract Syntax Tree (AST) to analyze Python code, parse program files, and automatically compute software metric values. Each metric has a predefined threshold, as presented in Table 1. If the computed values exceed acceptable limits, the system flags them as potential software design issues.  
The verification of software metrics in a blockchain environment involves a smart contract mechanism that ensures a reliable, immutable, and transparent association between analysis results and the user who initiated the analysis. Instead of relying on traditional centralized authentication systems, the blockchain-based approach uses wallet addresses as unique user identifiers--thus achieving decentralized verification and autonomy while eliminating the need for intermediaries.  
The data retrieval process in the developed system also functions without intermediaries, identifying users through their wallet addresses (Figure 3). This ensures that only the owner of the analysis results can access their metrics:  
* The user logs into the system by connecting their digital wallet;
* The user navigates to the verification section;
* The system sends a query to the smart contract to retrieve the data;
* The smart contract accesses the relevant data structure on the blockchain and associates the stored metrics with the user's wallet address;
* The results are returned to the user in JSON format, allowing for detailed review.