esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Preparation of PPyNP-Based Sensor Electrode  
PPyNPs were immobilized on the interdigitated array electrode to determine the sensing performance of the particles as a function of pH. The conduction pathway and effective surface area were utilized to compare the performance of the particles with the variation in their chemical state. Figure 6 shows the data for the source-drain current as a function of the source-drain voltage (I\({}_{\text{SD}}\)-V\({}_{\text{SD}}\)) for the nanoparticles; the results indicate that the nanoparticles are in good electrical contact with the sensor substrate. However, the uniform decoration of the particles improves the conduction pathway, i.e., increases the sensitivity to the analyte. The randomly stacked arrangement of the PPyNPs that were prepared by drop-casting has a higher contact resistance than the uniform array that was obtained by spin-coating due to the interruption of the conduction pathway through the accumulated particle-particle assembly. Therefore, PPyNPs were deposited on the electrode via spin-coating to determine the sensing performance in real time with minimum contact resistance.  
Figure 5: Electrical conductivities of PPyNPs at various pH values.  
Figure 6: (**a,b**) Field emission scanning electron microscopy (FE-SEM) images and (**c,d**) source-drain voltage-current curves of the PPyNP-decorated sensor electrodes prepared by (**a,c**) drop-casting or (**b,d**) spin-coating.
## 3 System Model of FGSM and Traditional EDAS  
### FGSM  
An advanced variant of SM called FGSM enables the simultaneous transmission of the same symbol by one, many, or all antennas. Figure 1 depicts a simplified block diagram of the FGSM system. The index for the active antenna subset and the associated symbol to be transmitted are chosen in response to the incoming group of bits. Later, the maximum likelihood detector is applied to the received signal vector. The transmitted group of bits are reconstructed by demapping the detected transmit antenna indices and their associated transmitted symbol.  
In comparison to the SM, the SE is linearly proportional to the number of transmitting antennas, which enhances the SE. The SE of the FGSM is estimated as follows [12]:  
\[\xi_{FGSM}=(N_{t}-1)+\log_{2}(M) \tag{2}\]  
A sample FGSM mapping for \(N_{t}\) = 4 and \(M\) = 2 is displayed in Table 1. Let the grouped bits be [0 1 0 1], where the first (\(N_{t}-1\)) bit, i.e., [0 1 0] refers to the antenna combination (1, 4) and the last \(\log_{2}(M)\) bit, i.e., [1], corresponds to the transmitted symbol +1. The accompanying transmit vector is given by \(\mathbf{\kappa}\) = [1, 0, 0, 1].  
Additive white Gaussian noise (AWGN) \(\mathbf{n}\) and multipath channel \(\mathbf{H}\) have an effect on the transmit vector \(\mathbf{\kappa}\). The FGSM receiver employing maximum likelihood detection is elaborated in [12].  
Figure 1: Block diagram of FGSM system.
## 4 Discussion
According to published data, the population doubling time of the UC-MSCs obtained by the explant method was found to vary among different groups, ranging from 26 h to 60 h in early passage cells [36; 55; 56]. To better characterize WJ-MSCs and SA-MSCs, we paid attention to the expression of pluripotency-associated genes, oct4, nanog, and sox2. These gene code transcription factors are generally considered important for the efficient maintenance of the fine balance between self-renewal and differentiation in human embryonic stem cells [57]. OCT4, SOX2, and NANOG are typically part of core reprogramming cocktails that can induce pluripotent properties in somatic cells [58; 59]. The transcription factor OCT4 was shown to maintain the pluripotency of mouse and human embryonic stem cells [60]. In stem cell research, it is common for authors to use the OCT4 gene as a reference to OCT4A. The gene coding for OCT4A also generates OCT4B and OCT4B1 [61]. Although there has been extensive research on the function of OCT4A in maintaining pluripotency in stem cells, the function of OCT4B protein isoforms remains unclear. In the literature, data for the expression OCT4, SOX2, NANOG, and SSEA-4 in MSCs derived from the UC are controversial. At the protein level, we detected OCT4, SOX2, and NANOG in WJ-MSCs and SA-MSCs, similarly to a previous report by [PERSON] et al. [48]. [PERSON] et al. indicated a slight expression of OCT4 (11.31 \(\pm\) 1.77) in WJ-MSCs isolated by the explant method [33]. We also confirmed the SSEA-4-positive cell population in SA-MSCs [25] and WJ-MSCs [62]. At the mRNA level, several studies reported the expression of OCT4, SOX2, and NANOG in MSCs isolated from the entire UC or WJ by the explant method [48; 51]. Also, the expression for OCT4 and NANOG in WJ-MSCs obtained by enzyme digestion has been reported [63; 64]. [PERSON] et al. displayed the presence of OCT-3/4 and NANOG and a lack of SOX2 in MSCs derived from subamniotic human umbilical cord lining membrane by explant cultures [25]. On the other hand, [PERSON] et al. reported that umbilical cord lining MSCs are negative for OCT4 and SOX2 [65]. The comparison of MSCs isolated from various compartments of the human umbilical cord displayed a significantly lower expression of OCT4A, OCT4B, NANOG, and SOX2 genes in cells from the SA and amnion compared to cells from WJ [26]. Apart from that, OCT4A and OCT4B genes were significantly less expressed in cells from the UC compared to WJ [26]. By contrast, we identified similar levels of mRNA for OCT4A, OCT4B, NANOG, and SOX2 in both SA-MSCs and WJ-MSCs. The applied explant method appears to predominantly enrich WJ-MSCs and SA-MSCs, which possess no difference in growth kinetics, MSC characteristics, and the expression of pluripotency markers (OCT4, NANOG, SOX2, and SSEA-4).
## 3 Finite Element (FE) Investigation
### Fracture Simulations  
The ductile fracture model by [PERSON] [11] in conjunction with the true stress-strain response identified in Section 3.2 was applied to simulate fracture in three types of specimens, in which tension and in-plane shear specimens were used for calibration of the fracture locus shown in Figure 8. Figure 10 compares the FE load-displacement curves of tension and in-plane shear specimens against those obtained from experimental tests. It shows that the FE models were able to replicate the experimental responses of the tension coupons with a reasonable level of accuracy, while it overestimates the load capacity of in-plane shear specimens.  
One reason for this inaccuracy is the use of the von Mises yield criterion, which was derived from uniaxial tension tests. As the Lode angle effect is not considered in the plasticity model of SS275 structural steel, the inaccurate yielding function was adopted for the pure shear specimens. The similar finding can be found in the results of the high strength (Q690) pure shear specimens tested by [PERSON] et al. [28], in which the von Mises yield criterion overestimates the load capacity by up to 25%. Another possible reason is the characteristic of the uncoupled damage model, which neglects the material softening by accumulated damage.  
Figure 9: Results of mesh sensitivity analysis: (**a**) Specimen Tension-3; (**b**) Specimen Shear-2.  
The results of fracture simulation are consistent with the experimental results in Figure 3a,b for the tension and in-plane shear specimens, respectively. It was found that the fracture started at the center of the tension specimen, while it initiated at the edge of shear specimen. The fracture model was also validated against the test results of the tension and shear specimens that have not been used for defining fracture criterion.  
It can be seen from Figure 11 that the FE model underestimates the fracture displacement of the tension and shear specimens. This is mainly due to the off-axis loading induced by misalignment. Although pin-loading is generally recommended for ensuring axis loading, the fixed-end boundary condition was applied to the testing with the limitation of experimental design. This resulted in the fracture that started at an earlier stage and developed at the center of specimen, in contrast to the experimental response shown in Figure 3c.  
Figure 10: Results of fracture simulation: (**a**) Uniaxial tension specimen; (**b**) In-plane shear specimen.  
The conservative prediction could be also explained by the use of a simplified fracture model based on only two model parameters. The inaccuracy of the BW model in predicting ductility of steel material subjected to combined tension and shear loading has been pointed out by [PERSON] [23]. Better accuracy could be achieved when introducing more mode parameters to account for Lode angle dependence. Nevertheless, this simplified model may be still favorable to engineers, due to its ease of implementing fracture simulation. In fact, the model has provided sufficient accuracy for fatigue life prediction [29,30].
# A Rotating Object Detector with Convolutional Dynamic Adaptive Matching
## 1 Introduction
In samples with high aspect ratios, the majority of preset boxes that meet the IoU threshold fail to fully encompass the target, and the angle between these preset boxes and the ground truth boxes is significant. The angle difference between low-quality samples and truth labels is significant, resulting in significant gradients during the training process. However, high-quality samples with high cover and ground truth boxes have smaller gradients, which contribute less to training losses. In general, low-quality samples with a higher proportion contribute more to the loss, while high-quality samples with a lower proportion contribute less to the loss, resulting in unstable training. We design an adaptive rotating target detector with sample dynamic adjustment, which achieves plug-and-play and high-precision detection without increasing computational complexity. In the backbone network, an angle estimator that matches the target orientation is constructed. The spatial sampling position of the convolution kernel is rotated, and a plug-and-play dynamic convolution layer is designed to ensure that the convolution operator for feature extraction aligns with the target in the spatial direction. The loss function we designed amplifies the gradient of samples with high IoU values and low regression gradients, improving the contribution of high-quality samples to regression loss. By dynamically adjusting the gradients generated by the samples during the training, we ensure efficient and stable training of the model. Our contributions are as follows:  
1. We systematically analyze the problem of feature misalignment and imbalanced gradients caused by different quality samples during the training process of the current RS detector. 2. In the feature extraction stage, we design a plug-and-play rotating dynamic convolution that can adaptively align the convolution with the target direction based on the spatial distribution of the target. 3. In training, we design a gradient adaptive equalization loss function to optimize the contribution of gradients from different samples to regression loss and improve the training stability. The organizational structure is as follows: In Section 2, rotating object detection and dynamic networks are introduced. Section 3 proposes two innovative designs, namely dynamic convolution aligned with the target direction and gradient adaptive equalization loss function for sample balancing. Section 4 conducted a series of comparison and ablation experiments on three public datasets to demonstrate the effectiveness of our method. Section 5 summarizes and analyzes the proposed methods.
## 3 System Model and Architecture Design
### Communication Phase of Proposed SBD2D System Model  
In the architecture shown in Figure 2, there are three communication possibilities for UEs, (i) direct communication without BS via D2D communication, (ii) communication through eNB, i.e., the cellular communication mode, and (iii) communication via the SCeNB, i.e., the small cell communication mode [29]. The D2D communication phase starts  
Figure 3: Relay node transmission.  
once the connection between the two devices of the smart beehive is established through the licensed/unlicensed spectrum. With this setup, the QoS is easy because the cellular spectrum can be fully managed by the BS and a good network throughput is achieved. Building on Equation (2), we considering a Rayleigh fading environment and assume that \(k\_nR\) and \(k\_D2D\) are exponentially distributed. In such a D2D communication case, cellular is the main source of interference signal. Hence, the data rate of link \(r\) of SBD2D is given by a version of the Shannon capacity formula below [29; 30].  
\[\varphi_{g}(r)=\sum_{i=1}^{U}B\text{log}_{2}\Big{(}1+\beta_{D_{g}}(r)\Big{)} \tag{3}\]  
where \(B\) denotes the bandwidth of the D2D channel \(g\) (it is expressed in Hz), \(r\) is the D2D link that uses the channel \(g\), and \(\varphi_{g}\) is the data rate of link \(r\)[30; 31].
# The Integration of AI and Metaverse in Education: A Systematic Literature Review
## 4 Results and Discussion
### Rq4: What Are the Challenges of Utilizing AI in the Metaverse for Education?
#### 4.4.4 Integration and Interoperability Challenges  
Integrating AI with the existing metaverse can be complex, requiring careful consideration of interoperability. Indeed, the metaverse relies on a wide range of technologies, which dictates the need for a unified and integrated system that combines virtual learning environments with metaverse technologies [6; 108]. However, achieving this integration is complex due to the diverse range of technologies involved, including AI, blockchain, AR, VR, and MR. Each of these technologies has its own set of standards and protocols, which can complicate their integration into a cohesive educational platform. Ensuring that these technologies work together harmoniously is a significant challenge. Technical implementation challenges also contribute to interoperability issues. As an example, the AI-based generation process of a class notes by [PERSON] et al. [61] involves various technologies, including Optical Character Recognition (OCR), Automatic Speech Recognition (ASR), and text-to-speech. Ensuring seamless integration and functionality among these technologies can be challenging and may require extensive testing and refinement. Concerning AI-based chatbots and virtual assistants, integrating their functionalities with existing metaverse infrastructure demands interoperability with various systems, requiring robust APIs and frameworks [56]. Further concern regarding interoperability is the lack of unified standards for integrating AI within the metaverse [56].
# Use of Phase Change Materials for Food Applications--State of the Art in 2022
## 2 Food Applications of PCM
### Food Processing
#### 2.1.6 Distileries for Whiskey  
In a feasibility study, performed as part of Phase 1 of the BEIS Green Distileries competition, the company Sunamp and Heriot-Watt University investigated if a high temperature, large-scale PCM TES could be used by distilleries to enable fuel switching to low carbon or even zero emission technology. The results are published in a report [19].  
According to the report, the Scotch whiskey industry is one of the most energy intensive subsectors of the national \"food and drink\" industry, and is working towards net zero emissions by 2040, including all operations from maltings production to distillation, maturation, blending, bottling and warehousing. Generation of heat for malting and distillation is the main source of emissions in the distilling industry. Heat demand is met by steam, in most cases today produced by the combustion of fossil fuels or biomass.  
The feasibility study investigated if large scale PCM TES could allow to size distillery heat generating technology to an average load instead peak demand, and if then a smaller generating unit could encourage the use of zero or low carbon technology and/or enable the use of more renewable options. Using energy and process modelling from Heriot-Watt University, with data from their on-campus distillery as well as industry input (e.g., distilleries peak demands, duration, temperature,... ) the size of storage was determined, required to peak shave and enable renewable energy derived thermal energy. The result is that a large-scale (MWh) PCM TES can be used, which could be available e.g., in 10, 20 and 40 ft shipping container modules. Currently Sunamp manufactures PCM TES with storage capacities ranging from 3 kWh to 12 kWh for the domestic market and 80 kWh for industrial applications. Throughout, the same basic technologies and construction, a container which contains PCM and heat exchanger, are used, so can be scaled to a required size. For the case studied, requiring steam production, the existing PCM TES technology for 118 \({}^{\circ}\)C can be used. The report shows how scaling can be done. According to the report, in a system with 5 kW PV coupled and a 20 kWh storage, the storage will have a rapid payback time from savings on electricity, which could pave the way to net zero carbon distillery. They plan, in Phase 2, to proof the finding by real scale testing at several demonstration sites.
# Conservative Interference Injection to Minimize Wi-Fi Sensing Privacy Risks and Bandwidth Loss
## 5 Proposed Contention Model for Defence Against Wi-Fi Sensing
### CSMA/CA in 802.11 Networks  
802.11 networks are designed with Medium Access Control (MAC) protocols such as Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) to handle a large amount of devices attempting to access the wireless channel resources simultaneously [23]. CSMA/CA requires stations to sense the channel to ensure that it is idle prior to attempting any transmission [24]. When a station is ready to send a packet, it listens to the channel for a period known as the Distributed Inter-Frame Space (DIFS) interval [24]. If the channel remains idle throughout the entire DIFS interval, the station backs off for a randomly chosen number of discrete time slots. This random back-off duration is uniformly distributed between \((0,W-1]\), where \(W\) is a variable known as the contention window [23; 24; 25]. In each slot where the station senses an empty channel, it decreases the back-off counter. Once the back-off counter reaches zero, the station can attempt to transmit again using the wireless medium. If a collision is detected, the station again randomly chooses a back-off interval, this time choosing an integer uniformly distributed between \((0,2W-1]\), and the process repeats. The upper limit iteratively increases as a function of the number of collisions \((m)\)[25] by \(2^{m}W-1\). This is limited to a maximum ceiling of \(2^{m_{\max}}W-1\), where \(m_{\max}\) is the maximum number of collisions allowed, which varies between Wi-Fi hardware.
# Beamforming Based on a SSS Angle Estimation Algorithm for 5G NR Networks
## 4 Numerical Results
### FR1 Operation Mode  
Figures 4-6 illustrate the comparison of the different decoding percentages of the DL PHY channels of the three algorithms referred to above for the FR1 operation mode. Despite the lower effective PBCH decoding throughput of the designed BSAE algorithm, we can verify an enhancement in the DL-SCH decoding percentages. This exists because the algorithm presented in [29] only uses a few possible beams, since it is limited to the tested beams, whereas in the proposed BSAE algorithm, we do not have this limitation because we use the estimated spatial correlation matrix, which allows us to use any angle that maximizes the RSRP.  
Figure 4: Decoding percentage of PBCH for FR1.  
Figure 5: Decoding percentage of PDCCH for FR1.  
The worst performance of this mentioned beam pair selection algorithm based on RSRP maximization is noticeable. Almost all TBs were only successfully decoded above \(E_{b}/N_{0}=0\) dB. Moreover, the BSAE algorithm achieved 100% throughput for values higher than \(E_{b}/N_{0}=-12\) dB, which corresponds to a 12 dB performance gain. By comparing the DL-SCH decoding throughput between the proposed and an optimal beam pair determination algorithm, it is still possible to observe similar performances.  
The respective performance results in terms of BER for the three algorithms mentioned above are presented in Figure 7, where the proposed BSAE algorithm presents a clear improvement over the beam pair selection based on the RSRP maximization algorithm. The performance gain was around 17.5 dB at the BER target of \(10^{-3}\).  
Figure 6: Decoding percentage of DL-SCH for FR1.  
Figure 7: Performance BER comparison of the 3 different algorithms for FR1.
## 1 Introduction
Numerous researchers have found that time domain analysis is able to indicate the presence of bearing faults, but it cannot identify the location of these faults. Frequency domain analysis can identify the location of bearing faults. In the measured response spectrum, response peaks occur at certain bearing fault frequencies, from which the location of defects in the bearing mechanism can be identified. In addition, envelope analysis can also be a very useful method to detect incipient defects of the rolling bearing. Combined time-frequency domain analysis could detect bearing faults effectively due to its very fine response data resolution [14]. Wavelet analysis is also a good method for fault diagnostics. A significant amount of research has been undertaken on artificial neural networks (ANNs) and fuzzy logic for bearing fault diagnostics [15]. These techniques can detect bearing faults very quickly, and with high accuracy. A static and modal analysis of the engine mounting bracket was performed to support the engine, which sustains the vibration caused by the engine and tires due to bumping on the road surface. The natural frequency was identified from finite element analysis (ANSYS) for the gray cast iron bracket, with which its low natural frequency and mode shapes by modal analysis proved to be restrictive in the vibration characteristics of the bracket [16]. A static and modal analysis of the supporting L-shaped bracket for air the compressor system was performed to find its natural frequency and mode shapes. The modal analysis avoids the resonance phenomenon by proper selection of materials and design to avoid the common natural frequencies in the system, in order to eradicate the problems of inefficiency of the system and financial loss [17]. In this paper, modal analysis and harmonic response analysis were also performed on the bracket in ANSYS software, to identify and eradicate the common natural frequencies which could result in resonance. Known bearing vibration testing methods are more often adopted and developed for industrial machines than for agricultural ones. Therefore, we needed to consider how to use the existing bearing testing methodologies for mobile agricultural machines. There are cases where it is not possible to directly measure bearing vibrations due to a lack of access to a required measurement location. This is the case for the harvester threshing drum bearing. The objective of the current research was to develop a vibration measurement procedure that could provide the required information to assess rolling bearing defects. The main novelty of this study was the use of a specialized bracket and its implementation for a bearing test in a difficult-to-access component of mobile agricultural machines. By implementing the bracket, the testing equipment was able to be used to measure the bearing vibrations of agriculture machines, without dismounting the bearing, as after dismounting the threshing drum bearings, reinstallation of the old bearings is not feasible.