esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Digital Twin Technology for Electric and Autonomous Vehicles
## 2 Digital Twin in Transportation Systems
### Digital Twin Networks in Smart Transportation  
A digital twin network (DTN) is the natural evolution of the development of DT technology in the modern era. The DT of any physical object is the first cell of the DTN; thus, we can define the DTN as a set of virtual digital representations of different groups of physical objects connected by a high-speed communication medium to configure an integrated virtual system. The data exchange between the virtual model and physical object in the DT is done in a one-to-one unidirectional way. The operational changes in the physical object will directly affect the virtual model but not the opposite.  
On the other hand, the DTN allows for the comprehensive data exchange between DTs and physical assets in a multidirectional manner [23]. Recently, transportation has encountered issues that have increased with the development of urban cities, such as traffic congestion and accidents. In this context, the DTN can provide a better solution for such a complex environment and help to optimize the entire transportation system. The DTN also offers innovative transportation services, such as traffic information reporting, vehicle security, and data sharing. To keep pace with the rapid progress in the electric mobility sector, we need to use and integrate DTN technology with EV networks in smart cities, whether involving autonomous or non-autonomous vehicles, which will provide high possibilities for managing and improving transportation network systems at the city level as well as at a broader level.  
We consider the DTN architecture shown in Figure 4, composed of three layers: physical, network, and virtual. The physical layer consists of EVs, charging stations, roads,and facilities. These entities are connected to the network layer through sensors that transmit data concerning the vehicle positions and velocities, the road traffic, and the charging station status. The network layer receives information from the physical layer by communication services provided through 5G or WIFI technologies. Moreover, this layer sends information and data to the virtual layer composed of a network of DTs and servers. At the virtual level, the DTs are connected to collaborate in executing the simulation and computation tasks based on new enabling technologies devoted to decision-making, analysis, and maintenance issues (such as AI, AR, and ML). In this context, [PERSON] et al. [24] proposed a new DTN model to build the network topology and integrate it with the IoT network. The adopted system has significantly solved many problems, such as computation offloading as well as resource allocation problems.
# Imaging Diagnostics of Inside of a Building Wall Using Millimeter-Wave Reflectometer
## 4 Experimental Results and Discussion  
Various types of simulation sample subjects (blocks with tiles attached to the surface) were fabricated for the practical test measurement by using the above prototype system. Specifically, the tiles were attached to the concrete surface using an organic adhesive which is difficult to detect by a hammering inspection. A standard sample is shown in Figure 6a. The tiles were glued on top of the block with adhesive and sandwiched between a 19 mm-wide cushioning material (polyethylene sheet). The thickness of the polyethylene sheet  
Figure 4: The trace of the two-dimensional scanning sequence.  
was approximately 1 mm and played the role of an air layer. Another good example is shown in Figure 5(b). It has a hollow space inside the block. As described, it is quite difficult to distinguish the air layer behind the tile from the hollow space of the concrete in these types of blocks using hammering methods, including a hammering robot.  
Figure 5: The overview flowchart of the signal processing algorithm.  
Figure 6: Test examples of the block: (**a**) normal type; (**b**) hollow type.  
Figure 7a,b show the measurement results for the two samples (Figure 6a,b, respectively). For clarity, the area of a single tile is surrounded by a black frame. A threshold for the evaluation of the non-adhesive state was set from the difference between the maximum and minimum of the reflected wave signal. The yellow color area in the figure was determined to be the \"non-adhesive state\" by the microwave measurement.  
In order to verify the results of the microwave measurement, the conventional method was utilized; that is, the tiles were peeled off, and the adhesive surfaces were visually checked and classified into the following three categories for each 5 mm square.  
* Non-adhesive state: red dot.
* Half-adhesive state: yellow dot.
* Adhesive state: green dot.  
The photos of the peeled surface are shown in Figure 8a,b. The results of the visual inspection are shown in Figure 8c,d. A cushioning material was inserted as a temporary air layer, but there were other areas where the air layer remained without being sufficiently bonded.  
Figure 8: Visual results of bonding surface: (**a**) normal base; (**b**) hollow-type base; (**c**) normal base (visual evaluation overlaid); (**d**) hollow-type base (visual evaluation overlaid).  
Figure 7: Microwave measurement: (**a**) normal base; (**b**) hollow-type base.  
The adhesion area ratio, determined by the microwave measurement, was compared with the visual result, as shown in Table 1. The differences in the adhesion area ratio were 4.1 percent points (pp) for the normal sample and 2.0 pp for the hollow-type sample. These results are satisfactory. The present system can contribute to practical applications.
## 2 Model of Electromechanical Servo System  
### Mathematical Model of PMSM  
For PMSM vector control system, it is very convenient to analyze the dynamic and steady-state performance in d-q coordinate system. The following is the mathematical model of surface mounted permanent magnet synchronous motor in synchronous rotating coordinate system:  
\[\left\{\begin{array}{l}L\frac{d\dot{q}_{d}}{dt}=-R_{s}\dot{i}_{d}+n_{p}\omega Li _{q}+u_{d}\\ L\frac{d\dot{q}_{d}}{dt}=-R_{s}\dot{i}_{q}-n_{p}\omega Li_{d}-n_{p}\phi\omega+u_ {q}\\ l\frac{d\dot{\omega}}{dt}=\tau-\tau_{L}=n_{p}\phi\dot{i}_{q}-\tau_{L}\\ \frac{d\theta}{dt}=\omega\end{array}\right. \tag{1}\]  
\[\tau=n_{p}\phi\dot{i}_{q} \tag{2}\]  
where \(i_{d}\) and \(i_{q}\) are the stator current of d-axis and q-axis, respectively; \(u_{d}\) and \(u_{q}\) are the stator voltage of d-axis and q-axis, respectively; \(\theta\) is the rotor angle; \(\omega\) is the mechanical angular velocity of the rotor; \(\tau\) and \(\tau_{L}\) is electromagnetic torque and load torque, respectively; \(J\) is the moment of inertia; \(L\) is the stator inductance; \(R_{s}\) is the stator phase resistance; \(n_{p}\) is the polar logarithm; \(\phi\) is the permanent magnet flux linkage of motor.
# Multi-Information Fusion Indoor Localization Using Smartphones
## 2 Related Work  
Indoor localization technology has been widely researched for decades. Relevant research at home and abroad can be divided into the following two categories. First, indoor positioning technology is based on wireless networks, such as ultrasonic [18; 19], ultrawideband [4; 37], Bluetooth [13; 38; 39], and Wi-Fi [6; 40]. The second category includes indoor positioning technology based on inertial devices, pedestrian inertial navigation systems (PINS), and PDR.  
The localization approach ultrasonic-base promotes the development of positioning technology due to its compatibility with smartphones. [PERSON] [41] first proposed the GuoGuo positioning system with a smartphone, in which an acoustic signal between 15 and 20 kHz is used and achieves 6-25 cm positioning performance. [PERSON], [PERSON] et al. [42] proposed a new ultrasonic positioning method based on the receiver array optimization scheme, which can effectively improve the accuracy of indoor positioning. The literature [43] analyzed the localization effect of the CHAN and Taylor algorithms and demonstrated that the CHAN algorithm achieves better localization than the Taylor algorithm. The acoustic signal is susceptible to environmental interference, and accidental errors inevitably affect the localization performance.  
Pedestrian location in PDR estimation can be determined via step accumulation during walking. The literature [14] proposed the BtPDR localization method, which integrated the PDR method with Bluetooth. The localization accuracy is improved by 42.6% compared with the traditional PDR method. [PERSON], [PERSON] et al. [44] proposed an indoor localization method that combined the UWB method with the PDR method. The proposed fusion algorithm improved the localization accuracy and solved the errors for non-line-of-sight environments in the UWB estimation. The literature [45; 46] proposed the fuse localization method, which combines ultrasonic signals with PDR estimation and achieved high-precision performance in the indoor environment. The literature [47; 48] proposed an indoor positioning method based on the Wi-Fi, Bluetooth, and PDR methods. The fusion method solved the Wi-Fi signal instability and cumulative error in PDR localization.  
In research, many achievements in indoor localization have been made. However, low cost, high accuracy, and compatibility with different smartphones cannot be uniformly satisfied in different scenarios with the existing methods. Thus, inspired by existing localization technologies, we propose a fusion localization method that combines the CHAN algorithm and the improved PDR algorithm. The experiments demonstrate that the proposed method can solve abnormal points in acoustic signal localization, and effectively alleviate cumulative errors over time caused by the PDR algorithm. Also, the proposed method can use different scenes and different devices.
## 3 The Proposed OAM&WDM-PON Architecture Design with the P2 MP Topology
### Development of Algorithms for Allocation Methods  
The aim of this practical part is the creation of algorithms that can demonstrate functionalities for the utilization of the OAM technique in the proposed WDM-PON architecture. Presented algorithms for allocation methods are realized for three possible operating technologies--WDM, OAM, and their OAM/WDM combination. Each considered technology can provide three different allocation methods--static, dynamic, and dynamic customized. Each allocation method can utilize specific functions for setting the simulation program and appropriate input parameters of algorithms.
## 3 Two-Loop Closed-Loop Control Design
### Current Control Loop Design  
The schematic diagram of the inner current control loop is shown in Figure 7. The input inductor current is fed back to the error amplifier with the gain of \(H_{1}\)(s). The error is processed by a PI controller, and the output of the controller is compared with the modulator to generate the gating signals of the devices. The inductors' currents \(i_{L1}\) and \(i_{L2}\) are then regulated by adjusting the duty ratio of the switches.  
Duty ratio to the inductor current transfer function is derived from Equation (27) and given by:  
\[\frac{i_{L1}(s)+i_{L2}(s)}{\hat{d}_{s1}(s)+\hat{d}_{s2}(s)}=\frac{\big{(}\frac {C_{o}V_{o}}{n}\big{)}s+\frac{V_{o}}{nR_{L}}+\frac{2(1-D)}{n}\frac{I_{L}}{n}}{ (LC_{o})s^{2}+\frac{I_{L}}{R_{L}}s+\frac{2(1-D)^{2}}{n^{2}}} \tag{31}\]  
For the given specifications as shown in Table 1, the duty ratio to inductor current transfer function is given by:  
\[T_{p1}(s)=\frac{i_{L1}(s)+i_{L2}(s)}{\hat{d}_{s1}(s)+\hat{d}_{s2}(s)} =\frac{0.004542s+0.331514}{4.26\times 10^{-8}s^{2}+\ 5.847\times 10^{-7}\ s+3.472\times 10^{- 3}} \tag{32}\]  
The gain margin and PM of the current control loop without controller is plotted in Figure 8, which shows PM = 90\({}^{\circ}\) at 177 krad/s. The PI controller is designed at PM of 60\({}^{\circ}\) in Figure 9 to increase the low-frequency gain and to reduce the steady-state error [21].  
Figure 6: Pole-zero map of the control-to-output voltage transfer function of the system without controller.  
Figure 7: Inner current control loop schematic diagram.  
The transfer function of a PI controller is given by:  
\[T_{C1}(s)=K_{p}+\frac{K_{i}}{s}=\frac{K_{p}(s+K_{i}/K_{p})}{s} \tag{33}\]  
The open-loop transfer function of the current loop is given by:  
\[T_{OL1}(s)=T_{C1}(s)\times T_{m}(s)\times T_{p1}(s)\times H_{1}(s) \tag{34}\]  
LEM sensor LA25-NP is used to sense the inductor current and to provide the isolation between power circuit and controller. Here, \(H_{1}\)(s) is the current feedback gain and \(T_{m}\)(s) is the overall gain of the modulator.  
Here, the current feedback gain is selected as \(H_{1}\)(s) = 1.  
Overall gain of the modulator is chosen to be:  
\[T_{m}(s)=\frac{1}{10} \tag{35}\]  
To realize PM = 60\({}^{\circ}\), the following angle condition for the open-loop transfer function is applied:  
\[T_{OL1}(jw_{c})=PM-180^{\circ} \tag{36}\]  
\[T_{OL1}(jw_{c})=\frac{106572K_{p}\Big{(}jw_{c}+\frac{K_{i}}{K_{p}}\Big{)}(jw_{ c}+73.02\ )}{jw_{c}(jw_{c}+\frac{1}{13.72}jw_{c}+81502.34 jw_{c})} \tag{37}\]  
To realize the desired crossover frequency, the following gain condition of unity is applied for the open-loop transfer function, i.e., \(\Big{|}T_{OL1}(jw_{c})\Big{|}=1\):  
\[k_{p}^{2}\bigg{(}1.5625\ \times 10^{10}+\ \frac{k_{i}}{k_{p}}^{2}\bigg{)}=4.875 \ \times 10^{11} \tag{38}\]  
PI controller parameters are designed to obtain PM of 60\({}^{\circ}\)[21; 22] at the gain crossover frequency of 31.5k rad/s (5000 Hz) (Figure 9). Low-frequency gain is improved. It results in the gain \(K_{p}\) and \(K_{i}\) as 0.16 and 7269.58, respectively.
## 5 Performance Evaluations  
In this study, five static path planning models are used to compare the proposed model's performance in the obstacle-presence scenario. These models are HILBERT, LMAT, Z-curve, H-curve, and M-curves. APT and WCL localization algorithms are used to evaluate the various performances of mobility models. Figure 10 shows the obstacle-handling trajectory of the proposed model and the estimated positions of the UNNs in the obstacle-presence scenario using both localization methods, respectively.  
Figure 10: The proposed mobile path’s obstacle-handling trajectory and estimated locations of the nodes distribution in (**a**) WCL, and (**b**) APT.
## 3 Methodology
### Study Selection Process  
The study selection process followed a rigorous approach to ensure both the relevance and the high-quality standard of the chosen studies. The procedure can be visualized in the study selection flowchart shown in Figure 1.  
1. Identification: a total of 152 documents were identified through searches in the Scopus database.
2. Selection: of these, 79 documents were selected for further analysis according to title and abstract, and 73 documents were discarded at the initial stage.
3. Eligibility: The remaining 79 papers were reviewed in full text to determine eligibility. At this stage, 55 papers that did not meet the specific inclusion criteria were excluded.
4. Inclusion: 24 papers were included in the final analysis and were finally considered eligible.
## 3 Materials and Methods
### Type of Tests and Standards on the Hardened Material
#### 3.3.2 Flexural Tests  
Flexural tests were carried out following the standard \"UNI EN 1015-11:2019 [14]. Methods of test for mortar for masonry. Determination of flexural and compressive strength of hardened mortar\". They were constituted by three-point bending tests on prismatic beams (40 \(\times\) 40 \(\times\) 160 mm\({}^{3}\)) cured for 28 days. The testing machine is a frame model Advantest by Controls S.p.A. with a maximum load of 15 kN (Figure 4), constructed with a standard device for flexural tests, consisting of two steel supporting rollers of length 50 mm and 10 mm diameter, spaced 100 mm apart, and a third steel roller of the same length and diameter located centrally between the support rollers (Figure 5a,b). The load was applied at a uniform rate of 50 N/s until failure. Figure 5a,b represent the initial and the final steps of the flexural test, respectively. Flexural strength, \(f\), in N/mm\({}^{2}\) was obtained using the following formula:  
\[f=\frac{1.5\cdot F\cdot l}{b\cdot d^{2}}\]  
where  
\(F\) is the maximum load applied to the specimen, in Newtons,  
\(l\) is the distance between the support rollers, in millimeters (in this case 100 mm)  
\(b\) is the width of specimen, in millimeters (40 mm)  
\(d\) is the depth of the specimen, in millimeters (40 mm)  
Figure 3: Samples and setup of water absorption test: (**a**) setup of a set of specimens, (**b**) contact surface after the water absorption tests.
# A Novel Edge Platform Streamlining Connectivity between Modern Edge Devices and the Cloud
## 5 The \"PAIR Mechanism\"--Ensuring Data Integrity in Edge-Cloud Synchronization
As shown in Figure 10, the core of the PAIR Mechanism's workflow is a scheduled task that initiates at predetermined intervals, say every few minutes. This scheduler triggers the cloud starter, which sends a message to the edge device containing the name of the dataset (or table), the number of rows it should have, and an MD5 hash of the dataset. The MD5 hash serves as a fingerprint of the data, providing a compact representation that can be used to verify integrity without transmitting the entire dataset. Upon receiving this message, the edge agent compares the provided MD5 hash against the hash of the local dataset in its database. If the hashes match, it indicates that the data on the edge device is in sync with the cloud, and no action is needed. This check acts as a lightweight verification step to avoid unnecessary data transmission when both ends are already synchronized. However, if the hashes do not match, the edge agent interprets this as a discrepancy in the datasets. It then enters a reconciliation phase where it sends a message back to the cloud, including the same metadata: the table name, its MD5 hash, and the number of rows from its perspective. The cloud then evaluates this information to determine the next course of action. Should the edge report more rows than the cloud, it suggests that data has been generated at the edge which has not yet been synchronized. Figure 10: The PAIR Mechanism workflow. The cloud will then request all rows to be sent back for inclusion in the cloud dataset, thus achieving parity. In cases where the cloud has more rows, it suggests missing data to the edge, and the cloud will send the necessary rows to the edge to fill in the gaps. This back-and-forth communication ensures that both datasets reach a point of equivalence, even if multiple iterations are required. By transferring the entire dataset when discrepancies are detected, the PAIR Mechanism simplifies the process of error checking and data recovery. It is a robust approach, particularly suited for environments where the integrity of the data is crucial, and where the additional bandwidth consumed by transmitting the full dataset is a worthwhile trade-off for the assurance of consistency. The PAIR Mechanism's approach is markedly different from traditional synchronization methods that may employ more granular data checks and incremental updates. By operating under the assumption that synchronization will fail at some point, it ensures a robust and resilient system that maintains data integrity across distributed nodes. This mechanism is especially relevant in industrial applications where the cost of data inconsistency could be significantly higher than the cost of data transmission, making the PAIR Mechanism a strategic choice for systems where failure is not an option.
## 5 Market Survey on Small Satellites Monopropellant Propulsion
### State-of-the-Art CubeSat Monopropellant Propulsion  
State-of-the-art small satellites monopropellant propulsion were found to utilize thrusters with a range of thrust from 0.1 to 1 N, refer to Table 12. Some of the distinguished systems in the market are namely, EPSS C1 by NanoAvionics [53] in Figure 8a, BGT-X5 by Busek Company [12; 43] in Figure 8b, and CubeSat Modular Propulsion System MPS-130 by Aerojet Rockketdyne [12; 54] Figure 8c. The former system uses an ADN-based green propellant, while the latter two systems use the HAN-based ASCENT (formerly AF-M315E) [56]. [PERSON] et al. [56] discussed the development of the MPS propulsion system with both hydrazine and the green monoppellant AF-M315E; the development and fabrication process used state-of-the-art additive manufacturing techniques using advanced space materials such as Inconel-625\({}^{\text{\textregistered}}\) and Ti-6 Al-4V (Ti64) alloys which helped significantly in the development of these modular systems suitable for CubeSats envelope and mass constraints. All the above-mentioned systems utilize a pressurant gas either a conventional barrier separated pressure-fed system as in the case of EPSS C1 and MPS-130 or utilizing a post-launch gas generation mechanism such as the novel CO\({}_{2}\) gas generator in the case of BGT-X5 [45]. Nevertheless, new systems that are using pump feed with a propellant management device PMD are proposed by Aerojet Rockketdyne under the MPS propulsion system family, such as the MPS-135 4U and 6U [54]. Another recently market-released propulsion module series for small satellites is the Pinot-G developed by IHI Aerospace Japanese company [57]. Pinot-G burns HNP225 green monopropellant and incorporates four thrusters 0.5 N each. The wet mass of Pinot-G is 10 kg and occupies dimensions of \(\diameter 350\) mm diameter and 135 mm height. The pressurization technique relies on post-launch tank pressurization designed to be pressurized in orbit. The figures available on the company's website, refer to Figure 8d, show that three gas generators are connected to the propellant tanks, from both ends, which may be used in tank pressurization. A very interesting recent green monopropellant propulsion system for CubeSat is the one being designed and developed for the Lunar Flashlight Mission by the Georgia Institute of Technology and NASA [58]; this custom propulsion system is fueled by AF-M315E and fits in 2.5U standard size and maintains a total wet mass of less than 6 kg. This system relies on propellant pump feed and delivers over 2500 N s of total impulse.