esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulations
### The 2 Nth-Order Broadside Differential Beamformer Synthesized by the Proposed Method  
Different orders of the target broadside differential beampattern with maximized directivity factor are synthesized by the solution (50) using a line array of 31 loudspeakers with a spacing of 0.038 m. We first set the regularization parameter \(\mu\) to a small non-negative number, \(10^{-12}\), and then study the effect of this parameter on the beamformer.  
The synthesized beampatterns are shown in Figure 3. Different orders of broadside radiation patterns can be synthesized by the modal matching method with Tikhonov regularization. Except for the slight deviation in the null positions at some frequency points, the beamformer gives the frequency-invariant beampattern almost across the evaluated frequency range. The reason for the nulls' deviation is that, unlike the null-constrained method which adds constraints at the null positions, the proposed method approximates the desired beam in the modal domain. The finite truncation in (39) results in the spatial aliasing with higher-order circular harmonics. To solve this problem, a higher truncation order of the Jacobi-Anger expansion should be considered [32].  
The WNG and DI are two important metrics in beamforming algorithms. The WNG is a good indicator of the robustness to element imperfections. The DI quantifies the directional property. Figure 4 shows the performance of different-order beampatterns synthesized by the proposed method in terms of WNG and DI. As can be seen in Figure 4a, the WNGs of the different-order beampforners tend to be consistent above 1 kHz, all exhibiting relatively high WNG values, which indicate that the proposed method is robust enough at high frequencies. At low frequencies, the higher the order of the target beampattern synthesized, the lower the WNG obtained. It indicates that the eighth-order broadside differential beamformer has the worst anti-perturbation ability below 1 kHz. Figure 4b shows the DIs of different order beamformers designed by the proposed method. As seen, the DI increases with the order of the synthesized beampattern. Except for some fluctuations at certain frequencies, which coincide with the conclusion of Figure 3, the DIs of all four kinds of beamformers can approximately maintain a constant at all evaluated frequencies.  
Figure 4: The performance of the different-order synthesized beamformers: (**a**) WNG and (**b**) DI.  
Figure 3: Synthesized broadside beampatterns for a line array of 31 loudspeakers. (**a**) Second-order, (**b**) fourth-order, (**c**) sixth-order, (**d**) eighth-order.
# On Deploying the Internet of Energy with 5G Open RAN Technology including Beamforming Mechanism
## 1 Introduction
The most important criteria are the needs of wide coverage, cost-saving (in both, money expenses and energy usage) and security assessment (including confidentiality, integrity, and availability) such as the implementation of Security Incident and Event Management (SIEM) and Security Orchestration, Automation and Response (SOAR) systems, as well as the implementation of continuous integration/continuous deployment (CI/CD) of the network elements (generally, the Network Functions) [6]. In this paper, we focus on the requirements and potential solutions (implementations) of the underlying communication network that provides connectivity to IoE systems. This communication network will be deployed in all the physical infrastructures of the energy company. For example, with the radio resources (frequency) reserved for the energy companies, this will interconnect all sensors in a power plant but also will connect management hubs through the whole energy distribution infrastructure. All the hubs and points of presence may be connected by one common radio technology that uses a separate frequency. We analyse the advancements of the 5G radio technology and show that 5G is the most suitable communication technology for IoE, since it fulfils the requirements related to critical applications and massive data treatment. This is discussed in Section 2. In Section 3, we present an efficient and cost-saving implementation of the 5G based on Open RAN (Open Radio Access Network) concept. The reasoning for using Open RAN is that it will be possibly cheaper (thanks to increased concurrency) and more flexible for specific requirements that the IoE may pose. The biggest issue for deploying Open RAN in the IoE is the limitations of the Open RAN technology, especially in the use of beamforming technique since Open RAN has not yet implemented an effective coding scheme through the different Open RAN modules. Therefore, the research effort of this paper goes in the direction of solving the lack of beamforming deployment in Open RAN by introducing an adapted equalization algorithm. The model of the equalization and the results are presented in Section 4. The results show the gaining of the proposed equalization algorithm in comparison to other algorithms. At last, Section 5 concludes that by solving beamforming, Open RAN may be the solution for a cost-saving and efficient 5G technology for the IoE.
## Appendix A Equations
### k-Means  
The k-means algorithm seeks to partition n observations into K (K < n) set of G groups G = {G_1,G_2,G_3...G_K} minimizing the within-cluster sum of squares.  
\[arg\ min\ G\sum_{i=1}^{K}\sum_{x\in G_{i}}\left(x-\mu_{i}\right)^{2} \tag{10}\]  
where:  
\(arg\ min\ G\): is the minimum argument in the set of clusters G.  
\(\mu_{i}\): is the mean of the values in the cluster \(G_{i}\).
# A Modular Web-Based Software Solution for Mobile Networks Planning, Operation and Optimization
## 3 Radio Access Network Algorithms
### Network Planning  
When real network data is unavailable or nonexistent (e.g., performance or fault management data), the common approach is to reach for link-level or system-level network simulations. This is the case in initial network planning, where coverage simulations in green fields are required to plan the future deployment of the BSs. In this work, a network planning support algorithm is presented: the RF coverage estimation. A second algorithm, the energy consumption extrapolation algorithm, provides energy consumption estimates, which can also be used for planning purposes.  
The RF coverage estimation algorithm was developed to simulate cell coverage, considering customized propagation models, by using DT measurements to calibrate existent stochastic propagation models, and antenna models to consider the link-level antenna gain. The calibration process of the propagation models improves the large-scale path loss estimation error, and allows developing more realistic planning and coverage studies. This algorithm has been developed to meet the 5G requirements, including a new beamforming Massive Multiple-Input Multiple-Output (mMIMO) antenna model, and also the use of high-resolution 3-Dimensional (3D) environment databases, the last, to cope with Millimeter Wave (mmWave) propagation modelling [25].  
The topic of RAN power consumption and energy efficiency received great interest by MNOs on the past years, as a mean to reduce Operating Expense (OpEx) but also to develop more environment friendly (green) networks. In this scope, the energy consumption algorithm was developed, allowing extrapolating the power consumption of a BS Remote Radio Unit (RRU) and Baseband Unit (BBU) under different radio conditions. The algorithm is based on a model obtained using records of energy consumption of BS RRU and BBU equipment and considers the effects of voice and data traffic variations on the BS power consumption. Other variables, such as transmitted power, cell availability, or resources allocated to users, are also considered. These energy consumption models were obtained using supervised regression techniques and mixed effect models, and validated with real energy consumption data from several BSs [26].  
These algorithms are being applied to 2G/3 rd Generation (3G)/4G legacy networks and 5G.
# Digital Photoelastic Analysis of TAD-Supported Maximly Arch Distalization
## 2 Materials and Methods  
### Photoelastic Model  
A photoelastic model of the left maxillary dentition was used for this study. The model was created as follows. Epoxy anatomical teeth models (Anatomical Tooth Model, B2-306, Nissin, Kyoto, Japan) were used as artificial teeth. To reproduce normal occlusion during treatment, pre-adjusted edgewise brackets with 0.018-inch slots were bonded to the facial axis point, which is considered to be the appropriate bracket position [19; 20] of each artificial tooth, and a 0.017 \(\times\) 0.025-inch stainless steel archwire was inserted and fixed to the brackets with elastic chains. This was the model representing normal occlusion after orthodontic treatment. An alveolar wax form with an actual width of 10 mm of the left maxillary dentition was fabricated to match the model. An impression was made using polyvinyl siloxane. The impression material was removed from the alveolar wax form area, and epoxy resin (Devcon ET-300, ITW performance polymer, Osaka, Japan) was poured into the area to create an elastic model. As a result, a photoelastic model consisting of a bracket, wire, and alveolar bone made of epoxy resin and teeth was fabricated (Figure 1).  
A retraction hook was soldered to the wire between the amines and first premolars. A 0.045-inch stainless steel wire with a posterior anchor was attached to the model 10 mm distally from the maxillary second molar. By changing the posterior anchor position, we simulated various retraction positions. For example, retraction may vary when using a plate-type anchor [6; 21], since it is possible to alternate the vertical position with the three hooks or use a miniscrew implant anchor, since its placement is limited by inter root space [22; 23].  
An elastic chain was attached from the retraction hook to the posterior anchor, and stress of 200 g was applied (Figure 1). There were six combinations with three retraction hook lengths (2 mm, 6 mm, and 10 mm) and two posterior anchor positions (2 mm and 8 mm from the maxillary first molar cervical region) (Figure 2).  
Figure 1: A photoelastic model consisting of a bracket, archwire, alveolar bone made of epoxy resin, retraction hook, posterior anchor, and elastic for applying retraction force.
# Overlap Avoidance of Mobility Models for Multi-UAVs Reconnaissance
## 4 Performance Evaluation
### Inter-Arrival Time  
We also measure the average inter-arrival time of two consecutive visitings to a unit region in order to analyze whether UAVs reconnoiter the area uniformly or not. Table 6 shows the average inter-arrival times and scanned coverage rates of the simulated models. As shown in Table 6, the mobility models with overlap avoidance provide shorter inter-arrival time than those without overlap avoidance. Since the total scan coverage rate is more than the previous models, the overlap avoidance schemes seem to visit each unit region more frequently with covering more area.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mobility Model** & **Average Inter-Arrival Time (s)** & **Scan Coverage Rate (\%)** \\ \hline RW & 2227 & 92.4 \\ RWOA & 1981 & 93.6 \\ \hline RMP & 1980 & 94.9 \\ RMPOA & 1908 & 97.4 \\ \hline EGM & 2007 & 96.3 \\ EGMOA & 1783 & 98.2 \\ \hline DPR & 1826 & 97.7 \\ DPROA & 1757 & 98.6 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The average inter-arrival time and scan coverage rate.  
Figure 10: The result of overlapped area.  
Among simulated mobility models, the DPR model with overlap avoidance shows the best performance. Since UAVs communicate with each other to share pheromone information, they can be guided to unreconnoitered places with consideration of overlap avoidance.  
Figure 11 illustrates the number of unit regions for each inter-arrival time from 1 to 900 s. Red dotted lines are existing models without overlap avoidance and blue solid lines are those with overlap avoidance. As shown in Figure 11, previous models have many regions with shorter inter-arrival time. This implies many regions are scanned almost at the same time which is inefficient for reconnaissance applications. On the contrary, the proposed schemes reduce not only the overlapped reconnaissance, but also the number of regions with shorter inter-arrival time. Let us see the much gap between red dotted lines and blue lines in the beginnings of the graphs.
# A Design of a Lightweight In-Vehicle Edge Gateway for the Self-Diagnosis of an Autonomous Vehicle
## 2 Related Works
### Vehicle Protocol
The introduced layer is particularly advantageous in the automotive domain since it reduces the complexity of scheduling time-triggered communication systems and simplifies incremental changes of existing schedules. [PERSON] et al. [31] proposed a new FlexRay bus data acquisition and calibration system based on a high-speed and flexible USB interface. The system overcomes the shortcomings of traditional FlexRay bus test equipment, such as complex software and hardware. [PERSON] et al. [32] proposed a FlexRay-MOST gateway architecture. The proposed gateway system is designed with Verilog HDL and implemented using SoC kits. [PERSON] et al. [33] explained the difference between the in-vehicle network CAN and MOST. CAN is a widely used in-vehicle network for control data and MOST is a network for multimedia data. However, CAN and MOST do not integrate and operate independently. [PERSON] et al. [34] proposed a universal plug and play (UPnP) controller area network (CAN) gateway system using UPnP middleware for interoperability between external smart devices and an in-vehicle network. The proposed gateway consists of an UPnP communication device, a CAN communication device, and a device translator layer. An automatic CAN gateway test system has been proposed to solve the difficulties and complexity problems of manual vehicle gateway testing. The test system integrates IPC, CAN tools and network control module. It supports 2-4 channel gateways as well as complex gateways with signal routing, network status management and other complicated functions [35]. [PERSON] et al. [36] presented a novel \"sensorless\" longitudinal road-gradient estimation method, which was developed in two steps starting from a benchmark system design. The gradient benchmark system consists of an inclinometer sensor and an acceleration-based error correction algorithm, which can be used to verify the accuracy of the road gradient obtained using a sensorless estimation method. The sensorless road-gradient estimation algorithm uses the vehicle data currently available on the vehicle CAN bus. [PERSON] et al. [37] aimed at a new anti-theft vehicle lock system. Here, with high speed reliable CAN, a sensor-based mechanism is interfaced with Engine Control Module (ECM) using ARM7 TDMI microcontroller. To prevent vehicle from theft fuel flow sensor observes ignition of engine and attached GSM sends an alert message to owner. [PERSON] et al. [38] presented info-security scheme, real-time hardware/software solutions and their application scenarios. The core is the Integrated Info-Security Circuit Board to communicate with ECUs and sensors inside a vehicle through CAN Bus, LIN Bus, FlexRay and MOST Bus with wire interfaces, and to communicate with other vehicles, road-side infrastructure and mobile phones with wireless interfaces. A Design of a Lightweight In-Vehicle Edge Gateway (LI-VEG) for the Self-Diagnosis of an Autonomous Vehicle
# Non-Destructive Study of the Microstructural Effects of Sodium and Magnesium Sulphate Attack on
## 2 Materials and Methods
### Subplate Solutions  
The mortars were immersed in three different aggressive sulphate solutions, denominated according to the percentage by weight of the dissolved magnesium and sodium sulphates. The first solution contained 15% by weight of reagent grade anhydrous magnesium sulphate (MgSO\({}_{4}\)). The second solution contained 15% by weight of reagent grade anhydrous sodium sulphate (Na\({}_{2}\)SO\({}_{4}\)). Finally, a mixed sulphate solution was also studied, containing 7.5% magnesium sulphate and 7.5% sodium sulphate by weight. The content in the aggressive ions of interest is shown in Table 2. The 15% sulphate concentration has been selected in order to accelerate the attack of this aggressive ion and to observe its deleterious effects at relatively early exposure ages. The solutions were changed once during the test period (after 45 days exposure) to maintain a high enough concentration of sulphates in the solutions. The volume of solutions was approximately 4 times the volume of the samples, as recommend by the ASTM C 1012-04 standard [33].  
\begin{table}
\begin{tabular}{c c c} \hline
**Compound** & **CEM I 42.5R (\%)** & **Silica Fume (\%)** \\ \hline CaO & 64.5 & 0.4 \\ SiO\({}_{2}\) & 20.0 & 94.1 \\ Al\({}_{2}\)O\({}_{3}\) & 5.7 & 0.3 \\ Fe\({}_{2}\)O\({}_{3}\) & 2.5 & 0.1 \\ SO\({}_{3}\) & 3.2 & 0.1 \\ MgO & 0.9 & 0.3 \\ Na\({}_{2}\)O & 0.1 & 0.6 \\ K\({}_{2}\)O & 1.0 & 0.6 \\ \hline \end{tabular}
\end{table}
Table 1: Chemical composition of the cement and silica fume.
## 3 Results
Finally, when the patterns of changes are considered across the frequency spectrum, as summarized in Table 2, it can be observed that there is minimal overlap between the results obtained with EPOC and those with Brainvision across tasks. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Task** & **EEG Feature** & **Mental State** & **System** & **Significant Changes from Low to High State** \\ \hline ANEW & Amplitude & Arousal & Epoc & Increase for Gamma \\ \cline{3-5}  & & & BV & Decrease for Delta, Theta, and Alpha \\ \cline{3-5}  & & & & Increase for Beta and Gamma \\ \cline{3-5}  & & & & Increase for Beta \\ \cline{3-5}  & & & & Increase for Delta and Gamma \\ \cline{3-5}  & & & & Increase for Delta and Gamma \\ \cline{3-5}  & & & & Increase for Delta and Gamma \\ \cline{3-5}  & & & & Increase for Delta \\ \hline IAPS & Amp & Arousal & Epoc & Increase for all frequency bands \\ \cline{3-5}  & & & & Decrease for Delta \\ \cline{3-5}  & & & & Increase for Theta, Beta and Gamma \\ \cline{3-5}  & & & & Increase for Delta, Theta, and Gamma \\ \cline{3-5}  & & & & Increase for Alpha \({}^{*}\) \\ \hline n-Back & Amp & 1-3-5 & Epoc & Decrease for Delta \\ \cline{3-5}  & & & BV & Increase for Beta and Gamma \\ \cline{3-5}  & & 2-4-6 & Epoc & Decrease for all frequency bands \\ \cline{3-5}  & & & BV & Increase for Theta, Alpha, and Beta \\ \hline ANEW & Power & Arousal & Epoc & Decrease for Alpha \({}^{*}\) \\ \cline{3-5}  & & & & Decrease for Delta, Theta, and Alpha \\ \cline{3-5}  & & & & Increase for Beta and Gamma \\ \cline{3-5}  & & & & Decrease for Theta \({}^{*}\), Alpha, and Beta \\ \cline{3-5}  & & & & BV & Increase for Delta, Beta, and Gamma \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of patterns of EEG amplitude and power changes measured with EPOC and Brainvision. Figure 2: **(A)** Efficiency ratio of EPOC signal strength compared to Brainvision in ANEW and IAPS tasks. **(B)** Efficiency ratio of EPOC signal strength compared to Brainvision in the n-Back tasks.
# Fabrication and Photo-Detecting Performance of 2D ZnO Inverse Opal Films
###### Abstract  
Two-dimensional (2D) ZnO inverse opal (IO) films were fabricated by co-assembly of sacrificed polystyrene (PS) microspheres and citric acid/zinc acetate (CA/ZA) aqueous solution at an oil-water interface followed by calcination. Their morphologies could be controlled by the surface property of polymer templates and CA/ZA molar ratio. Moreover, photo-detecting devices based on such films were constructed, which showed high photocurrent (up to 4.6 \(\upmu\)A), excellent spectral selectivity, and reversible response to optical switch.  
ZnO; 2D inverse opal; oil-water interfacial co-assembly; photo-detecting device Article
# Designing a Novel High-Performance FBG-OADM Based on Finite Element and Eigenmode Expansion Methods
## 4 Eigenmode Expansion Method  
The primary function of the EEM was to transmit the power of simulated guided modes. The RI of FBG-OADM underwent cyclical changes and each change in RI during a cycle was called a segment. The first segment of the FBG-OADM was first dropped with each division referred to as a \"block\". Each block was assumed to be a uniform waveguide. Next, the FEM was used to solve all guided modes in each block, and the EEM was then employed to transmit the power between the block boundaries. Since changes in the RIs of FBG-OADM were cyclical, the power transmission in the segments during the simulations resembled the power transmission situation of the overall FBG-OADM [7; 13; 14].  
The aforementioned information shows that, in contrast to using traditional numerical simulation techniques, this study performed FBG-OADM-based numerical simulations by using the combined FEM-EEM approach, which can complete numerical simulations within a shorter timeframe. Although the finite-difference time-domain (FDTD) method has been used in previous research [20], it requires considerable computation time and memory capacity during simulations--especially for complex structures, such as the FBG-OADM. In summary, the combined FEM-EEM approach is more efficient than conventional numerical simulation techniques for designing devices with a cyclic structure (e.g., FBG-OADM, LPG-OADM, and resonance sensors with a long-period fiber grating).  
Mathematically, EEM embodies an operation concept similar to that of the Fourier series expansion method; that is, if too few expansion functions are used, discrepancies will occur between the expansion and expanded functions. In numerical operations, because including all modes is impossible, errors are unavoidable. To overcome this problem, similar to the FEM approach adopted in this study, the acceptable maximum error (i.e., power loss) for modal expansions was set to \(-40\) dB [7; 13; 14]. In other words, when the power loss failed to reach the review standard, the number of modes was increased and simulations were repeated until the power loss satisfied the review standard.