esa-sceva/satcom-chunk-collection huggingface dataset:
# Information-Centric Network-Based Vehicular Communications: Overview and Research Opportunities
## 4 An Overview of Research Challenges
### Cloud-Based Vehicular ICN  
Unlike data processing/execution in a local area network (LAN), Cloud computing is a concept where computation depends on difference resources that are shared in a wide area network (WAN). Usually, the architecture of Cloud computing is based on across-the-board data centers connected together wherein users access their desired resources through the Internet [24]. At present, various companies such as Google, Microsoft, and Amazon exploit Cloud data centers [25] for caching a huge  
Figure 3: A taxonomy of vehicular network with other emerging technologies based on literature survey.  
amount of records and hold prevalent service applications [26]. Because of the hardware and/or software constraints or reformation of security issues, it is indispensable to use data centers for storing records [25; 26]. However, service provision in this situation becomes a crucial problem.  
Due to exponential growth in Internet traffic, use of vehicles as cloud nodes is an accepting idea. This is possible because several vehicles are equipped with caching and sensing sensors for providing safety in the driving as well as infotainment for passengers. Using the cloud as an _infotainment-only_ feature is easy in the current IP-based Internet paradigm. However, linking this feature with the ICN model is a challenging task due to vehicles' mobility. Using the concept of naming in ICN, seamless mobility can be supported without executing difficult network administrations needed in the IP-based networks, when topological or physical locations of mobile nodes change [27]. In the last few years, various strategies have been proposed for addressing this challenge from the viewpoints of publisher and subscriber mobility. The proposals for enabling subscriber's mobility generally incorporate proactive caching [28] and prompt recovery of request/reply [29].  
Basically, ICN is a publish-subscribe networking model, wherein subscribers are mainly interested in actual contents rather than their locations [30], its primary focus is on content retrieval to allow subscribers to attain the requested information. Thus, it is a prospective model to resolve several ubiquitous challenges face in the IP-based infrastructure, for example, mobility and security among others [27].  
Mobile cloud services can be integrated with the ICN-based VANET to provide access to the stored information. However, _due to the nature of ICN accessing method which is based on names_, a feasible connectivity approach is required for the smooth transition. Nevertheless, this facility raises several new challenges in the network model, which are discussed in Section 5.
# Continuously Beam-Steered Phased Array Antenna Using GaN Varactors for Millimeter-Wave Applications
## 1 Introduction
The superior characteristics of GaN technology, such as high-temperature operation due to large energy band gaps, high breakdown field, and high saturation velocities of electrons, make this semiconductor technology a promising candidate for achieving good linearity and realizing high-power and high-frequency varactor diodes; consequently, the designs of AIAs with high power handling capability and high-frequency operation are now easily achieved [15]. As a result, GaN-based HEMT semiconductor devices are no longer limited to high-power amplifier circuits [15], having now been extended to AIA systems [16; 17]. Reconfigurable RF circuits, with high power/frequency and excellent linearity, are currently in great demand. GaN varactor diodes are among the key components in the design of these reconfigurable RF circuits to achieve the required performances for millimeter-wave applications. Furthermore, phased array antennas with beam steering capability at high power/frequency and with excellent efficiency have attracted a lot of research interest over the last few years. Many research works using varactor diodes to obtain the beam steering functionality have already been reported in the literature. Most of them use GaAs varactors and thus result in a limitation in terms of frequency and RF power. This paper addresses the subject of a beam-steering phased array antenna based on a classical structure that is very known in the literature and which plays a vital part in wireless millimeter-wave applications. The array antenna is designed to continuously steer the main beam in continuous resolution without increasing the antenna elements or switching elements. The design of this antenna array has focused on developing the controllable continuous reflection type of phase shifter by using for the first time GaN HEMT varactor diodes as loads. Thus, this results in a novel beam-steering two-element patch antenna array using GaN HEMT components. The proposed overall objective of this research work is to demonstrate the realization of a large-signal continuous beam-scanning antenna array for millimeter-wave applications based on GaN technology which will be used as an active varactor co-integrated in low-cost microstrip technology to achieve the tunability of beams. The proposed phased antenna array design is based on existing designs of beam steering; here, GaN HEMT varactors are used for the first time for beam steering applications around 40 GHz. This work can be divided into three major parts: the modeling of the GaN-based varactors, the phase shifter where these developed GaN devices are integrated, and the two-element patch array antenna. The completed study, simulation, and measurement of the GaN HEMTs as varactor devices are presented as well as the design of the used analog phase shifter and the two-element phased antenna array. To the best of the authors' knowledge, this is the first time that GaN HEMTs have been used for an array antenna to achieve continuous beam steering.
## 3 Results
### Myogenic Gene Expression of Directly Reprogrammed Myogenic cells by RV/bMyoD  
After 7 days of direct conversion of BDFs into myogenic cells using RV/_bMyoD_, morphological and genetical changes were analyzed (Figure 4). On day 3, the formation of myotubes in BDFs treated with RV/_bMyoD_ was observed, showing elongated morphology, and more matured byotubes were generated over time (Figure 4A). Cells with parallel arrangements were also observed on days 3 and 5 after transduction with RV/_bMyoD_. Some of the cells showed fused and connected formation on day 5. On day 7, BDFs were analyzed for the expression of representative myogenic marker genes by RT-PCR (Figure 4B). Specific primers were used to detect the endogenous myogenin (_MyoG_), myosin heavy chain 3 (_MYH3_), and myomarker (_MYMK_). According to the results, BDFs with RV/_bMyoD_ showed enhanced expression of those myogenic genes, whereas the control (untreated BDFs) did not show clear expression of the genes. Then, the intensity of each band was quantified using Image J, as shown in Figure 4C. As a reference gene, GAPDH  
Figure 3: Viral-based direct reprogramming using bMyoD retrovirus. (**A**) Plasmid constructs of pMX/_bMyoD_. (**B**) PCR gel of double-digested pMX/_bMyoD_ retroviral vector. Red arrows indicate pMX and bMyoD, respectively. (**C**) Histogram of FACS for infected HT1080 with concentrated retrovirus. (**D**) Titer of concentrated retrovirus. \(n=3\) per group.  
was used, by which each mRNA expression value was normalized. RV/_bMyoD_-treated BDFs showed a statistically significant increase in mRNA expression, compared with the value of control. Expression of _MyoG_, an early myoblast marker, was 4-fold to control. The expression of both _MYH3_ and _MYMK_, the late myotube markers, was 7-fold to the control, respectively. The results demonstrated that RV/_bMyoD_ treatment induces myogenesis of the BDFs, resulting in morphological changes and up-regulated expression of both an early myoblast gene and the late myotube genes.  
Figure 4: Observation of myotube formation and myogenic gene expression of induced myogenic cells. (**A**) Morphological analysis of BDFs. Induced myogenic cells using RV/_bMyoD_ showed formation of microtubules. Scale bars, 100 μm in white and 50 μm in yellow. (**B**) PCR gel images of representative myogenic genes: _MyoG_, _MYH3_, _MYMK_. (**C**) Quantification of gene expression using Image J. \(n\) = 3 per group. Statistical significance was determined using one-way ANOVA in comparison with the non-treated control BDFs. **_p_ < 0.01, ***_p_ < 0.001.
# GNSS-Assisted Low-Cost Vision-Based Observation System for Deformation Monitoring
###### Abstract
A phase correlation algorithm is suggested as a primary processing strategy. 4. Preliminary analysis of the VOS accuracy for the test structure. The investigation is carried out using statistical simulation and results from stage 3. 5. Determination of the monitoring parameters for the actual structure. Relative displacements of the VOS elements are used to model the structure frame model and compare with the design model of the structure. 6. Prediction model. Based on the structure frame model (values of monitoring parameters), a prediction model is built for a given point in time. In this article, the features of the first four implementation stages will be described and studied in detail, and the simulation and experimental measurement results will be presented. The paper is structured as follows. Section 2 outlines the general concept of the VOS, its design, and its displacement determination approach. Section 3 describes the results of experimental studies and simulation of the VOS for a high-rise building. A comprehensive analysis of the results after the simulation and discussion are presented in Section 4. Section 5 presents the conclusions.
###### Abstract  
Deep network fault diagnosis requires a lot of labeled data and assumes identical data distributions for training and testing. In industry, varying equipment conditions lead to different data distributions, making it challenging to maintain consistent fault diagnosis performance across conditions. To this end, this paper designs a transfer learning model named the multi-adversarial joint distribution adaptation network (MAJDAN) to achieve effective fault diagnosis across operating conditions. MAJDAN uses a one-dimensional lightweight convolutional neural network (IDLCNN) to directly extract features from the original bearing vibration signal. Combining the distance-based domain-adaptive method, maximum mean difference (MMD), with the multi-adversarial network will simultaneously reduce the conditional and marginal distribution differences between the domains. As a result, MAJDAN can efficiently acquire domain-invariant feature information, addressing the challenge of cross-domain bearing fault diagnosis. The effectiveness of the model was verified based on two sets of different bearing vibration signals, and one-to-one and one-to-many working condition migration task experiments were carried out. Simultaneously, various levels of noise were introduced to the signal to enable analysis and comparison. The findings demonstrate that the suggested approach achieves exceptional diagnostic accuracy and exhibits robustness.  
intelligent diagnosis; multi-adversarial domain adaptation; deep transfer learning; rolling bearing 2023
## 3 Theoretical Development
### Streaming Potential Coupling Coefficient  
#### 3.4.1 Streaming Current in the REV  
Adapting the strategy given by [90], the streaming current in a fracture due to transport of charge by the fluid under a fluid pressure difference is given by  
\[i_{\rm s}(a)=\int_{-a}^{+a}v(y)\bar{Q}_{v}(y)(w.dy), \tag{26}\]  
where \(a\) is the half aperture and \(y\) is the coordinate along the aperture as shown in Figure 2, \(v(y)\) (m/s) is the velocity profile given by Equation (9) and \(\bar{Q}_{v}(y)\) (C/m\({}^{3}\)) is the charge distribution in a fracture. Under Debye-H\(\ddot{u}\)ckel approximation and the fluid of a binary symmetric 1:1 electrolyte, e.g., [51; 91], \(\bar{Q}_{v}(y)\) is given by, e.g., [57; 92]  
\[\bar{Q}_{v}(y)=-\frac{\epsilon_{r}e_{\phi}\zeta}{\lambda^{2}}\frac{\cosh(\frac {y}{\lambda})}{\cosh(\frac{a}{\lambda})}, \tag{27}\]  
where \(\lambda\) is the Debye length depending on properties of the fluid and not on properties of the solid surfaces, e.g., [36; 93].  
Combining Equations (9), (26) and (27), one obtains  
\[i_{\rm s}(a)=-\frac{\epsilon_{r}e_{\phi}\zeta}{\eta}\frac{2 wa}{\tau}\bigg{[}1 -\frac{\lambda}{a}\tanh(\frac{a}{\lambda})\bigg{]}\frac{\Delta P}{L_{0}} \tag{28}\]  
Under most environmental conditions, ionic strengths (i.e., a proxy for ionic concentration) in potable water are normally between \(10^{-3}\) and \(10^{-2}\) mol/L [94]. Reservoirs can be saturated with brine with much higher ionic concentrations. Therefore, the Debye length is typically less than 10 nm at 25 \({}^{\circ}\)C [93]. In addition, typical characteristic size of pore and fracture aperture in geological media is tens of micrometer, e.g., [95]. Therefore, the Debye length is normally much smaller than the pore sizes (thin EDL assumption). As seen in Figure 3, when \(a\) is 20 times larger than \(\lambda\), \(1-\frac{\lambda}{a}\tanh(\frac{a}{\lambda})\) can be approximated as 1 with around 5% difference (see the red dot in Figure 3). Under that condition, Equation (28) is simplified to  
\[i_{\rm s}(w)=-\frac{\epsilon_{r}e_{\phi}\zeta}{\eta}\frac{2 wa}{\tau}\frac{ \Delta P}{L_{0}}=-\frac{\epsilon_{r}e_{\phi}\zeta}{\eta}\frac{2\beta w^{2}}{ \tau}\frac{\Delta P}{L_{0}}. \tag{29}\]  
Therefore, the total streaming current through the REV is given by, e.g., [53; 56]  
\[I_{\rm s}=\int_{w_{min}}^{w_{max}}i_{\rm s}(w)(-dN) \tag{30}\]  
Combining Equations (5), (29) and (30) yields  
\[I_{\rm s}=-\frac{2\epsilon_{r}e_{\phi}\zeta}{\eta\tau}\frac{\Delta P}{L_{0}}( \beta D_{f}w_{max}^{D_{f}})\int_{w_{min}}^{w_{max}}w^{1-D_{f}}dw=-\frac{2 \epsilon_{r}e_{\phi}\zeta\beta D_{f}}{\eta\tau}\frac{w_{max}^{2}}{(2-D_{f})} (1-\alpha^{2-D_{f}})\frac{\Delta P}{L_{0}}, \tag{31}\]  
recall that \(\alpha=w_{min}/w_{max}\).
## 4 Results and Discussion
### Evaluation of Performance Gain of the Proposed Method
The reason behind this was in those two arrangements, the ability of the MIS was not required and the robot was navigated without switching the perception from the default perception. In all other layout arrangements, the intention of the robot was changed only once in each case which leads to a reduction of required total number of steps. Therefore, the number of user instructions or steps required to navigate the robot to a desired location in this kind of situations can be reduced by deploying the MIS. Even though the step number reduction in this kind of task is small (about 1-3 steps), a robot that is used as supportive aid in a caring facility such as a nursing home would be required to perform this sort of navigation task a large number of times per a day and hence there would be a noticeable reduction of the work load in real-world applications. Moreover, this validates the potential of the MIS in enhancing the human-friendliness of the robot and interpretation of ambiguous voice instructions. Furthermore, a user study has been carried out similarly to the performance analysis carried out in [2; 27]. In here the participants were asked to rate the ability of robot based on the effectiveness of interpreting uncertain information in user instructions with MIS (a system similar to [20]) and without MIS (the system proposed in this paper) situations on a scale of 0 to 10 as similar to the evaluation approach of the work presented in [2]. The mean values obtained from this user rating the two systems are given in the graph shown in Figure 11 with the standard error bars. The ratings for the system with MIS and without the MIS are 8.0 and 6.4, respectively. According to the two sample \(t\) test, the system with the MIS has a statistically significant (\(p<0.05\)) higher rating than the system without the MIS. Therefore, this validates the enhancement in uncertain information interpretation ability of the proposed concept. Moreover, these results validate the potential of the MIS in enhancing the interpretation of voice instructions with uncertain information and subsequently the improvement of the human-friendliness of the robot. Figure 10: This graph shows the average number of steps/instructions taken in order to navigate the robot to the goal positions in different experimental layout arrangements during the experiment for evaluating the performance gain of the proposed MIS. The error bars represent the standard error.
# Treatment of Aqueous Bromate by Superparamagnetic BiOCl-Mediated Advanced Reduction Process
## 2 Results and Discussion  
### Characterization of the Photocatalyst  
Peaks appearing in diffraction patterns for BiOCl-HAP\({}_{\mathrm{SM}}\) (Figure S1) can be indexed to BiOCl (JCPDS No. 73-2060), HAP (JCPDS No. 09-0432), and Fe\({}_{3}\)O\({}_{4}\) (JCPDS No. 85-1436). SEM and TEM images show that a 3D-microflower morphology is formed for BiOCl-HAP\({}_{\mathrm{SM}}\) (Figure 1a) and the HAP\({}_{\mathrm{SM}}\) particles are uniformly embedded into the BiOCl structure (Figure 1b). Experiments were also conducted to directly test the magnetization of the photocatalyst. BiOCl-HAP\({}_{\mathrm{SM}}\) powder (50 mg) was ultrasonically dispersed in 4 mL ultrapure water filled with a glass vial for 5 min before separation (Figure 2a). Once a magnet approached the glass vial, a clear and transparent solution was obtained within 30 s (Figure 2b). This occurrence indicates that the BiOCl-HAP\({}_{\mathrm{SM}}\) powder can be separated and recovered easily from the solution. Figure 3 presents the magnetization (M-H) curves recorded with magnetic fields from \(-\)20 kOe to 20 kOe at room temperature. A saturation magnetization (Ms) of 16.8 emu\(\cdot\)g\({}^{-1}\) was measured for BiOCl-HAP\({}_{\mathrm{SM}}\). BiOCl-HAP\({}_{\mathrm{SM}}\) attained an extremely narrow magnetic hysteresis loop corresponding to low values in remanence and coercivity. This result suggests the mixture's superparamagnetic properties, which is provided by the Fe\({}_{3}\)O\({}_{4}\) constituent (Figure S1). Figure 4 demonstrates the UV-visible (UV-Vis) diffuse reflectance (DR) absorption spectra of BiOCl-HAP\({}_{\mathrm{SM}}\). BiOCl-HAP\({}_{\mathrm{SM}}\) exhibited a strong UV absorption. These results clearly show that micron-sized superparamagnetic BiOCl-HAP\({}_{\mathrm{SM}}\) with outstanding UV absorptive ability was successfully prepared through the two-step method.  
Figure 1: (a) SEM and (b) TEM images of BiOCl-HAP\({}_{\mathrm{SM}}\).
## 2 Data and Methods
### SSD Network Framework
#### 2.2.4 Detection Flow  
While the usual sliding window technique slides the whole image at a fixed sliding step, it cannot ensure that the windows cover the objects exactly. Moreover, small sliding steps result in huge computation costs and larger window sizes, thus decreasing the accuracy. As shown in Figure 6, a detection flow was created with the sliding window approach and an optimized sliding step to achieve higher accuracy and faster detection [43]. As an example schema, when the sliding was performed with a 300-pixel size over a \(500\times 500\) pixel image patch, the objects at the edges of the window could not be detected or the bounding box offsets of them would be incorrect. To tackle this problem, an overlapping area between two windows was determined as 100 pixels, which covers the object size for this research. In the sliding process for an image with a certain overlap, \(\text{k}\times\text{l}\) windows were obtained to detect by the object detector for the horizontal and vertical directions, respectively:  
\[k=\left[\frac{height-overlap}{ssd\ height-overlap}\right], \tag{5}\]  
\[l=\left[\frac{width-overlap}{ssd\ width-overlap}\right]. \tag{6}\]  
After the sliding and detection step, the non-maximum suppression (NMS) algorithm [44] (Appendix A) was used to eliminate multiple detection occurrences over an object in the overlapping regions and a score threshold was also applied to decrease the number of false detections (Figure 7).  
Figure 6: Process of the proposed detection flow of a \(500\times 500\) image with 100 pixels overlapping; the colored parts in the middle represents overlapping areas.
## 4 Numerical Simulation Results
\[Improvement(\%)=\left|\frac{\textit{passive}-\textit{active}}{\textit{passive}} \right|\times 100 \tag{15}\]  
Figure 15: The percentage performance comparisons of (**a**) the X body position and X body acceleration and (**b**) suspension and tire deflection. According to the graphs in Figure 15, the body performance increased by 84.2%, car acceleration increased by 90%, suspension deflection increased by 84.5%, and tire deflection increased by 86.7% compared to the passive system by using the PSO-based fuzzy-LQR control method. As shown in Figure 15, maximum performance improvements were obtained for all parameters using the PSO-based fuzzy-LQR method, one of the three control methods we used. Additionally, and perhaps most significantly, Table 7 contrasts the performance standards of the fuzzy-LQR control method, one of our suggested approaches, with those of the research in the literature. This table demonstrates how effective our method is in comparison to other methods. The best values for error performance are highlighted in bold in Table 7. According to the results of car body motion obtained utilizing the ITAE performance criteria in Table 7, the PSO-based fuzzy-LQR control method showed the lowest error performance, and its value was 0.00302 m. According to the performance criteria in the studies reported in the literature, the car body motion performance is determined by the harmonic, MPC, and H-infinity control methods, respectively, and their values are 0.0039, 0.0041, and 0.0071 m, respectively. According to the results of car acceleration in the table, the minimum error value is determined by the fuzzy-LQR control method, MPC control method, H-infinity control method, and harmonic control method, respectively, and their values are 0.07545, 0.1252, 0.1588, and 0.1683 m/s\({}^{2}\), respectively. According to the suspension deflection results of another parameter given in the table, the minimum error value is determined by the PSO-based fuzzy-LQR control method, the harmonic control method, the MPC control method, and the H-infinity control method, and their values are 0.00280, 0.0044, 0.0072, and 0.0046 m, respectively. Finally, according to the actuator force results shown in the table, the minimum error values were determined by the PSO-based fuzzy-LQR control method, the harmonic control method, the MPC control method, and the H-infinity control method, and their values are 59.29, 80.7, and 95.5 N, respectively. Considering the above results, the proposed PSO-based fuzzy-LQR control method showed superior performance in terms of all parameters compared with the studies in the literature when compared with the ITAE criteria.