esa-sceva/satcom-chunk-collection huggingface dataset:
# An Integrated Access and Backhaul Approach to Sustainable Dense Small Cell Network Planning
## 3 Problem Formulation
### One-Hop Problem Formulation
#### 3.1.2 Access and Backhaul Constraints  
To determine the location of \(\alpha_{j}\), we consider a set of constraints grounded in both access service provision across the entire spatial region and one-hop wireless backhaul's data rate limitations. The following constraints elucidate the service provision mechanism:  
* **Access Service Provision**: As defined by expression (9), there is a coverage constraint, meaning that the nodes should be deployed in a way to cover all the considered area. Expressions (10) and (11) further delineate the conditions under which a donor \(i\) and node \(j\) can collectively ensure coverage service to \(k\), contingent on the \(SNR\). \[\sum_{i=1}^{I}a_{i}C_{ik}+\sum_{j=1}^{J}a_{j}C_{jk}\geq 1,\quad\forall k\in K\] (9) \[C_{ik}=\begin{cases}1&\text{if }SNR(d_{i,k})>SNR_{0}\\ 0&\text{otherwise}\end{cases}\] (10) \[C_{jk}=\begin{cases}1&\text{if }SNR(d_{j,k})>SNR_{0}\\ 0&\text{otherwise}\end{cases}\] (11)
* **Backhaul Service Provision**: Expression (12) defines that each deployed node \(\alpha_{j}\) must be supported by a donor \(i\). Meanwhile, expression (13) ascertains that the donor's available data rate remains sufficient for service provision, even when catering for multiple nodes. This is premised on the assumption that the donor's capacity \(F_{i}\) is invariant, \(A_{i}\) solely depends on the donor's access data rate, and \(R_{o}\) is a fixed overhead. Hence, \(\sum_{j\in I}Y_{ij}R_{ij}\) represents the data rate required for all nodes \(j\) connected to donor \(i\): \[\alpha_{j}-Y_{ij}\leq 0,\quad\forall i\in I,\quad\forall j\in J\] (12) \[F_{i}-R_{o}(A_{i}+(\sum_{j\in I}Y_{ij}R_{j}))\geq 0,\quad\forall i\in I\] (13)
* **Data Rate Interpretation**: The data rate \(R_{ij}\) between a donor at position \(i\) and a node at position \(j\) is formulated considering both the access data rate and the associated overhead: \[R_{ij}\geq f(A_{j})\] (14) where \(f(x)\) is the linear function capturing the overhead and \(A_{j}\) is determined by the access data rate. From the aforementioned analysis, it is evident that both the coverage and backhaul constraints must be satisfied while minimizing the number of nodes during the network planning process. Consequently, we present the optimization problem as follows: \[\min\sum\alpha_{j}\] subject to: \[(9)(10)(11)(12)(13)(14)\] \[C_{7}:\alpha_{j}\in 1,0,\forall j\in[J]\] All parameters are listed in Table 1. The formulated problem is a mixed-integer (binary) linear programming one which can be solved by solvers like CPLEX [19].
# An Efficient RRT Algorithm for Motion Planning of Live-Line Maintenance Robots
## 4 Online Motion Planning  
In this section, the method of using offline trees to plan paths for live-line maintenance is introduced, including dynamic obstacles and path smoothness. During the operation, the robot takes the tool from the platform and moves to the target position, as shown in Figure 15. The movement process will be affected by obstacles such as platforms and electrical facilities (cables, cross arms, etc.). The target position is indeterminate and is provided by the depth vision sensor. The steps of path planning are shown in Algorithm 3.  
First, in the offline state, the random tree \(T_{offline}\) is expanded with the root node of \(p_{start}\) in the joint space \(\Phi_{f}\) of the robot. Unknown obstacles, such as cables, are not considered during the expansion process, and only fixed obstacle constraints are considered \(P_{StaticObs}\). The method of expanding random trees offline is introduced in Algorithm 2.  
During the working process, a tree \(T_{goal}\) is built with the cable position \(p_{goal}\) obtained by the sensor as the root node. \(T_{goal}\) is expanded using RRT* until the new node \(p_{new}\) of \(T_{goal}\) can be connected to the node of \(T_{offline}\). From this node, two paths can be planned to reach \(p_{start}\) and \(p_{goal}\). Through \(p_{new}\), a path from \(p_{start}\) to \(p_{goal}\) can be planned. Dynamic obstacles, such as cables, are imported into the model, and whether the planned path collides with these obstacles is checked. If a collision occurs, Algorithm 4 is used to replan the path.  
Figure 14: Trunks of the tree and the simulation environment. (**(left)** The trunk of a rapidly exploring random tree and the red branches will be removed, **(right)** simulation environment for robot motion).  
Figure 13: The effect of \(len\) on tree expansion (sample times = 4000, (**a**) len = 2, nodes num = 1086; (**b**) len = 4, nodes num = 498; (**c**) len = 6, nodes num = 301).
## 3 Performed Full-Scale/Model Test Comparisons
### Low-Rise Buildings
#### 3.1.3 Silsoe Full-Scale Measurement Program 1: The Steel-Framed Building  
The Silsoe Structures Building was a steel-framed building with a 10\({}^{\circ}\) duo-pitch roof, constructed from 1986 to 1987 for taking full-scale wind pressure measurements. It was 24 m long, by 12.9 m in span, and by 5.3 m ridge height, and was located on an open-country site at the Silsoe Research Institute. The building had changeable eave geometry, offering either a traditional sharp eave or a curved eave of 635 mm in radius.  
A lot of studies have been undertaken by researchers based on the resulting full-scale measurements and the subsequent model tests. [PERSON] and [PERSON] [11] compared the model-scale wind pressures for the Silsoe Structures Building to the full-scale data and found that the two sets of data compared well for transverse winds; however, model tests were likely to underestimate the mean wind pressure coefficients for cornered winds. [PERSON] (1977) also conducted full-scale/wind tunnel surface pressure comparisons on the Silsoe Structures Building, which suggested that differences between the model and full-scale surface pressure spectra at two taps were notable. [PERSON] et al. (2018) conducted detailed wind pressure comparisons and found differences at the separated flow region of the windward roof slope. [PERSON] et al. (2018) compared the wind pressures obtained on the full-scale Silsoe Structures Building with those from two 1:100 scaled models. The results suggested that when certain wind tunnel procedures were implemented, good predictions of the full-scale mean wind pressure coefficients could be obtained. However, high negative pressures still tended to be underestimated in the wind tunnel. Comparisons of the surface pressures measured on the Silsoe Structures Building at both full-scale and model scale were undertaken by [PERSON] et al. (2018), who observed differences in the separated flow at the windward eaves of the building, and the smoke technique for full-scale flow visualization was then utilized to reveal the related flow mechanics.
# A Survey of the Real-Time Metaverse: Challenges and Opportunities +
## 6 Metaverse Engine
### Immersive Technologies
#### 6.1.2 Augmented Reality (AR)  
Augmented reality (AR) is a technology that overlays digital information and virtual objects onto the real world, enhancing the user's perception and interaction with their environment [162]. Unlike virtual reality, which creates an entirely simulated experience, AR blends virtual elements with the physical world, often through smartphones, tablets, or AR glasses [163]. AR technology has gained widespread popularity in applications such as mobile gaming, with games like Pokemon GO allowing users to interact with virtual characters in real-world locations [164]. AR also enhances navigation and location-based services by providing real-time information and directions overlaid on the physical environment, improving user convenience and engagement [165].  
Beyond entertainment and navigation, AR is making significant strides in many fields, such as retail, education [166] (distance online learning), and healthcare [167]. In retail, AR applications allow customers to visualize products in their own space before purchasing [168], such as seeing how furniture would look in their home or trying on virtual clothing [169]. AR not only enhances the shopping experience but also reduces return rates and increases customer satisfaction. In education, AR brings learning materials to life by enabling interactive and immersive experiences, such as 3D visualizations of historical events or scientific phenomena, which can deepen student understanding and engagement [54]. In healthcare, AR assists surgeons by providing real-time overlays of critical information during procedures, improving precision and outcomes [170]. As AR technology continues to evolve, its integration into everyday life and various professional fields is expected to grow, offering increasingly innovative and practical applications.
# High Repetition Rate, TEM\({}_{00}\) Mode, Compact Sub-Nanosecond 532 nm Laser
## 3 Experimental Results  
### Seed Source Characteristics  
In order to achieve high repetition, LD1 was run on the CW model. Due to the compact structure, a GRIN lens was selected for coupling the pump light. Both the repetition rate and output power increased when the pump power was increased, as Figure 2 shows. The temperature of LD1 during operation was controlled at 25 \({}^{\circ}\)C by the TEC for the ideal absorption efficiency of Nd: YAG. The seed's output average power increased from 27.8 mW at 2.6 kHz to 477.2 mW at 21 kHz by adjusting the pump power from 1.4 W to 4.5 W. The average power and repetition rates were 317.8 mW and 16 kHz when the pump power was at 3.9 W. The single-pulse profile recorded by a Tektronix DPO70804C 8 GHz Digital Phosphor Oscilloscope and a DET025 A/M Photodetector of 2 GHz from THORLABS is shown in Figure 3. The full width at half-maximum (FWHM) of the laser pulse is 857 ps.  
Figure 2: The average power and repetition rate of the seed versus pump power.
# Metal-Coordinated Polymer-Inorganic Hybrids: Synthesis, Properties, and Application
## 5 Conclusions  
In summary, this review highlights the synthesis, classification, and applications of metal-coordinated polymer-inorganic hybrids, focusing on materials that incorporate polymers through coordination with metal ions. We first examine key synthetic methods, including homogeneous and heterogeneous approaches. Homogeneous synthesis involves directly combining polymers and inorganic components in solution, making it particularly suitable for water-soluble polymers. In contrast, heterogeneous synthesis uses liquid and vapor phase infiltration as post-processing techniques, allowing for the selective incorporation of metal precursors into specific polymer domains. Furthermore, we classify these hybrids based on the functional properties they gain after complexation, focusing on enhanced electrical conductivity, thermal stability, catalytic activity, and tunable photophysical characteristics. An essential advantage of these hybrids is their templating capabilities: they can either create hybrid-induced self-assembled structures or act as frameworks where one component can be removed, leaving a stable scaffold of the remaining material.  
Additionally, these hybrids serve as linkers, connecting polymer chains through metal coordination to form metallosupramolecular polymers. This group of hybrids includes three main categories: metal-organic frameworks (MOFs), coordination polymers (CPs), and supramolecular coordination complexes (SCCs), such as 2D metallacycles and 3D metallacages. These metallosupramolecular structures offer unique structural and functional properties that are highly advantageous for applications in sensing, catalysis, and drug delivery.  
Looking ahead, future research in polymer-inorganic hybrids will likely focus on expanding the variety of polymer and metal pairings to create hybrids with increasingly tailored properties for specialized applications. An exciting direction involves exploring the coordination of Group IV semiconductors, such as silicon and germanium, with polymers and block copolymers. Given their tunable band gaps, these semiconductors could impart unique electronic and optical properties to hybrid complexes, making them highly valuable for optoelectronic applications and next-generation materials with programmable properties.  
The manuscript was written through contributions of all authors. All authors have read and agreed to the published version of the manuscript.  
S.A., N.H., S.T.M., G.M., C.L.I and B.C. are supported by Iowa State University startup funds.  
S.A., N.H., S.M., G.M., C.I. and B.C. are supported by Iowa State University startup funds. C.I. would like to acknowledge support from the ISU College of Engineering Dean's Scholar Award.  
The authors declare no competing financial interests.
### Audio Classification and Machine Learning  
A classification method for sound, with the use of convolutional neural networks (CNNs) and Mel-frequency cepstral coefficients (MFCCs) for sound conversion to spectrograms suitable to be used as inputs for CNNs, has been proposed. This method improved the classification accuracy of the dataset from 63% to 97% and offered various benefits, such as faster training [19]. The diversity of acoustic quantification in vocal audio hasbeen used in a generalizable method with an unsupervised random forest framework. The authors demonstrated that their method could classify acoustic structures with a high degree of accuracy, offering a standard tool for music variation comparison [20]. Moreover, the use of Mel spectrograms has been evaluated using the architectures of convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. The findings showcase that a hybrid model, using compression techniques, Taylor pruning, and 8-bit quantization, achieved a high accuracy rate compared to those of traditional methods while using minimal computational resources [21].  
A systematic review analyzed machine-learning techniques with small-data augmentation for audio-based sound classification, including voice and speech. The analysis presented common challenges, such as insufficient or highly noised data, as well as their impacts on the classification performance. That review sheds light on sound analysis for feature extraction [22]. A later survey examined multiple audio classification deep-learning models with the main focus on five architectures: convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders, transformers, and hybrid models. Standard audio transformation techniques, such as spectrograms and Mel-frequency cepstral coefficients (MFCCs) were included [23]. A systematic literature review of small data and augmentation techniques examined the potential for improving classification via the deep learning of sound features, while identifying challenges, such as noise or data insufficiency, which make feature extraction difficult and unreliable. Recommendations were made in this context for the enhancement of the performance of sound classification [22]. A recent study explores the repeated training of an already trained convolutional neural network to investigate the impacts on the accuracy and processing time that the retraining parameters will have across several sound datasets [24].
## 5 Experimental Evaluations
### Discussion  
The COME-WP reduces latency by only predicting the user's location. COME-UP anticipates user direction and selects the fastest server. The COME-UP fitness function prioritiises server latency over other performance metrics such as energy and server utilization (see Figure 5a). With 100 users, TAOM automatically assigns tasks to the fastest server. When used with a large number of users, it does not scale well. DMPO has a lower task offloading latency than TAOM due to its simple server searching mechanism. Figure 5 shows how we measure latency by giving equal priority to the performance parameters. All of the techniques' latency increased compared to the previous case. Since TOAM and DMPO lack the fitness function we proposed, their latency increases with user count. The energy consumption of COME-UP is lower than TAOM and DMPO but higher than its COME-WP variant in some cases. The server information contained by COME-UP is limited as it focuses on a particular area predicted by its LDPM model. As a result, there is a possibility that makes it hard to find an energy-efficient server there, especially when the numbers of users and servers are low. The energy consumption of the proposed techniques is lesser when the weight of energy is given higher priority during the server selection stage. However, no effect is observed in the case of TOAM and DMPO since they lack the proposed fitness function, as shown in Figure 6. The proposed framework outperforms existing methodologies and makes efficient use of available computational resources, as shown by the server usage results. TAOM has lower CPU utilization than DMPO, COME-UP, and COME-WP. Inefficient server selection causes high energy consumption and low CPU utilisation in the TAOM. Since the system's workload grows with the number of users, the system's total energy consumption grows as well. As a result, idle CPU energy is consumed positively, resulting in efficient resource usage (Figure 7). Also, when the server load was given more weight during optimal server selection, the resource utilisation of servers increased. The loss of the model is used to measure the performance of the model. The RMSE of the LSTM model-based proposed framework is calculated as 0.5321 that lies in an acceptable range for the training process, as shown in Figure A3.
# A Multi-Physics Modeling-Based Vibration Prediction Method for Switched Reluctance Motors
## 4 Experimental Results and Verification
### Simulation and Experimental Results
#### 4.2.2 Comparisons of the Obtained System Modal Results  
The system modal of the SRM is obtained by using the simulation method presented in Section 3.3. In addition, the sinusoidal excitation method is adopted to acquire the actual system modal. Specifically, by applying different frequencies of sinusoidal signals to the SRM through exciter, the frequency response characteristics of the motor are obtained, and the vibration characteristics of the motor are determined. In the test, the SRM is fixed on the desktop by elastic cotton, and the sinusoidal excitation force is generated by the exciter (BK 4809, 10N). In addition, the vibration of the shell surface is recorded and analyzed by the laser Doppler Vibrometer (Polytech PSV-500-3D). Then, the exciter generates sinusoidal excitation with varying frequencies and scans the vibration response of the motor. The frequency response of the test points is shown in Figure 10. It is seen that the frequency of the first four orders are 618 Hz, 1215 Hz, 3022 Hz, 3856 Hz, respectively. The experimental characteristic frequencies are compared with those obtained by the proposed finite element method, as shown in Table 3. According to the simulation and experimental results, it is shown that the proposed modal simulation method based on static pre-calculation achieves high accuracy, and the errors between predicted and experimental results are within 3.7%. Compared with the results predicted by only considering the stator, the prediction error of the proposed method is reduced by at least 2.5%.  
Figure 10: Frequency domain responses at the test point.  
Figure 9: Flux linkage curve.
## 3 Experimental Results and Discussions
### Load-Deflection Curves  
Figure 13 shows the relationship between the load and the mid-span deflection. When compared with S-0 that was not strengthened, the other three columns (S-1 to S-3) that were strengthened while using SMRC had higher load capacities and peak deflections. In each curve, the peak load represents the load capacity, and the mid-span deflection of the load capacity represents the peak deflection. As the number of steel mesh increases from one to three, the load capacity is increased from 492 kN to 539 kN, while the peak deflection is reduced from 11.8 mm to 10.1 mm. The main reason why the peak deflection decreases with the increasing of layer number of steel mesh is that the whole stiffness of the specimen increases with more steel meshes.  
There are three phases for the load-deflection curve [37]. The first phase is the linear elastic phase. In this phase, the load that is applied on the column is small and there are no cracks on the SMRC or RC column. The second phase is the crack propagation phase. With the increase of the load, the resin concrete begins to crack. The cracks occur in different position of resin concrete, and the spacing between these cracks is large. Besides, some cracks on the RC column can be observed. The third phase is steel bar yielding phase. When the load applied on the column approaches the load capacity, the steel bars yield. Many cracks occur on the resin concrete in a short period with small spacing. Obvious flexural deformation for column can be observed. Also, the stress of steel mesh increases rapidly because of the yielding of steel bar.  
Table 2 summarizes the compressive test results of the four columns. When compared with S-0, the use of one steel mesh in the SMRC increases the cracking load by 96%, the load capacity by 23%,  
Figure 12: Strain at different depths of the mid-span cross section: (a) S-0, (b) S-1, (c) S-2, (d) S-3. Strain gauge No. 6 failed at the highest loading levels in each figure.  
Figure 13: Test result of load-deflection curves.  
and the peak deflection by 22%. The result indicates that the use of SMRC can delay the presence of crack, enhance the load capacity, and improve the deformability. Increasing the steel mesh layer number from one to three increases the cracking load by 10% and the load capacity by 10%, and it decreases the peak deflection by 14%, and the maximum crack width by 75%. The result indicates that the increase of steel mesh layer can delay the presence of crack, enhance the load capacity and reduce the maximum crack width, but it may compromise the deformability. The main reason why the reinforcement material can improve the deformability of the RC column is the high-performance of resin concrete. As can be seen in Figure 3b, the ultimate tensile strain of resin concrete is much larger than the normal concrete.