esa-sceva/satcom-chunk-collection huggingface dataset:
# Denoising Diffusion Models on Model-Based Latent Space
## 3 Proposed Method
### Latent-Space Encoding
#### 3.1.3 Residual Quantization  
A different strategy to improve VQ is represented by additive quantization [22]. In this case, the quantizer \(q\) can be seen as the sum of a set of quantizers \(\{q_{j}\}_{j=1}^{m}\). Each of these quantizers maps the vector \(x\) into its own codebook \(\mathcal{C}_{j}\):  
\[q\colon X \longrightarrow\mathcal{C} \tag{15}\] \[x \longmapsto c=\sum_{j=1}^{m}c_{j}=\sum_{j=1}^{m}q_{j}(x) \tag{16}\]  
Similarly to PQ, the code assigned to \(x\) is a vector in \(\mathbb{R}^{m}\), and the bitrate of the encoding corresponds to the sum of the bitrates of the subcodes. Unlike in PQ, here, the centroids are vectors with the same length as \(x\) and not just a fraction of it.  
Residual Quantization (RQ) [23] is a specific example of additive quantization. The idea is to give a hierarchy to the quantizers \(q_{j}\). The first one, \(q_{1}\), is learned normally without any additional information. The second one, instead, wants to be the optimal quantizer for the vector \(x-q_{1}(x)\), i.e., the residual between \(x\) and its quantization. In general, for \(j\geq 2\), \(q_{j}\) is  
Figure 2: Product quantization. (*) When \(R\
eq 1d\) this corresponds to OPQ.  
meant to quantize the vector \(x-\sum_{s=1}^{j-1}q_{s}(x)\), which effectively places this strategy under the additive quantization umbrella. While RQ demonstrated superior performance in minimizing reconstruction errors, as shown in Section 4.1, it exhibits a fundamental limitation. Specifically, it allows for the possibility of multiple valid encodings for a single data point, which undermines the core principles of the generative model training procedure.
## 3 Composite Metric Routing Approach  
It is worth noting that the distance metric of (2) can be used by a shortest path algorithm, e.g., Dijkstra or Bellman-Ford, to find the path from source to destination with minimum physical distance/length. On the other hand, the energy-cost metric of (8) can be also used by a shortest path algorithm to find the path from source to destination with minimum required energy consumption, with no explicit consideration to the distance of the path. To void the NP-hardness of finding the path with shortest distance subject to constraints on the energy consumption, we propose the following composite routing metric that combines the path distance and energy consumption in a multiplicative fashion.
# A Learned Reduced-Rank Sharpening Method for Multiresolution Satellite Imagery
###### Abstract
The remainder of this paper is organized as follows: Section 2 presents our methodology, including the mathematical model, network architecture, and training procedure. Section 3 describes the experiments used to demonstrate our method's effectiveness through both quantitative comparisons using synthetic data and qualitative assessments using real Sentinel-2 imagery, an ablation study of key components, and its extension for fusion with Landsat 8 data. Finally, Section 5 summarizes our findings and discusses potential future directions. Throughout, vectors are denoted in bold lower case and matrices in bold upper case, \(\mathbf{x}_{l}\) denotes the vectorized S2 image at band \(b\), \(\mathbf{X}=[\mathbf{x}_{b}]_{b=1}^{L}\) is an \(n\times L\) matrix with \(L\) bands and \(n\) pixels. A matrix \(\mathbf{F}\) constructed from \(L\) row vectors \(\mathbf{f}_{(i)}\) is denoted by \(\mathbf{F}=[\mathbf{f}_{(i)}^{T}]_{i=1}^{L}\). Code that implements the method in Python can be downloaded from GitHub, [[https://github.com/[PERSON]/LRRS2](https://github.com/SveinnEirikur/LRRS2) SM]([https://github.com/[PERSON]/LRRS2](https://github.com/SveinnEirikur/LRRS2) SM), accessed on 23 January 2025 [48].
## 4 Data Analyses, Discussion and Findings
### Integrated Project Delivery Method  
As shown in Figure 5, most contractors suggested that the integrated project delivery (IPD) method is more appropriate for piping prefabrication. As the majority of projects use design-bid-build (DBB) method, several MEP contractors in the same project only sign contracts with the general contractor or even with the owner. Beyond that, no contractual relationship exists between any two MEP contractors. Conflicts of interest exist during the installation stage. The installation schedules of different MEP systems are not well communicated before construction. Therefore, the piping prefabrication is risky when the installation space of pipes is occupied by other MEP contractors. The IPD method, which seeks to improve project outcomes through a collaborative approach by aligning goals of the project team through shared risk and reward, early involvement of all parties, and a multiparty agreement [33]. This application of IPD method in piping prefabrication will decrease the risk of rework. The interests gained through the successful implementation of piping prefabrication can be shared by the manufacturer, distributor and the MEP contractor.
# Influence of the Quality of Consumer Headphones in the Perception of Spatial Audio
## 2 Headphones Measurements and Virtual Headphone Simulation
### Non-Linear Distortion Simulations  
As commented on before, the swept-sine method employed to measure the frequency response of the headphones provides, apart from the frequency response, distortion harmonics simultaneously. Figure 3 shows the frequency response and the second and third distortion harmonic of the reference ((a)-REF) and the airline ((b)-AirL) headphones. Both of these headphones are a good example of low (a) and high distortion (b).  
To simulate the non-linear distortion of each headphone, the method described in [13], which uses Volterra kernels and a series of linear convolutions, was chosen. With this method, the transfer function of a system is described by means of a Volterra series expansion. The output signal can be represented as the sum of the linear convolution of the measured impulse responses with the input signal and the corresponding frequency-shifted version. Applying Fourier transforms to these series results in a linear equation system. The solution of this system allows the computation of the diagonal Volterra  
Figure 3: Frequency response with distortion of two headphones (left channel). Solid curve, magnitude; dashed curve, second order distortion harmonic; dotted curve, third order distortion harmonic. (**a**) REF, Reference headphone; (**b**) AirL, Airline headphone.  
kernels obtaining the impulse response terms for the main response and the first two distortion orders; Equation (3).  
\[\left\{\begin{array}{l}H_{1}=H_{1}^{{}^{\prime}}+H_{3}^{{}^{\prime}}\\ H_{2}=-2H_{2}^{{}^{\prime}}\\ H_{3}=-4H_{3}^{{}^{\prime}}\end{array}\right. \tag{3}\]  
where \(H_{1}^{{}^{\prime}}\), \(H_{2}^{{}^{\prime}}\), \(H_{3}^{{}^{\prime}}\) are the measured frequency responses and \(H_{1}\), \(H_{2}\), \(H_{3}\) are the Volterra kernels (\(\hat{\ }\) represents the Hilbert transform).  
Using these equations, the second and third distortion orders were simulated by convolution, applying them to Equation (4), where \(x(n)\) is the input signal and \(M\) is the number of samples of the kernel:  
\[y(n)=\sum_{i=0}^{M-1}h_{1}(i)\cdot x(n-i)+\sum_{i=0}^{M-1}h_{2}(i)\cdot x^{2}( n-i)+\sum_{i=0}^{M-1}h_{3}(i)\cdot x^{3}(n-i) \tag{4}\]  
More details of this technique can be found in [13]. This procedure was followed for Test 3 (Section 5).
# Slip Estimation Model for Planetary Rover Using Gaussian Process Regression
###### Abstract
In Section 5, we showed the training results, obtained by ground tests, and the estimation results, compared to real data on Mars conditions. In Section 6, we provided our conclusions. The main contributions of this paper are as follows:  
1. Obtained a GPR slip estimation model, which can not only have high estimation accuracy but also provides uncertainty of estimated values. The confidence interval obtained by models can be used in subsequent robust path planning research. 2. Compared models based on two kinds of simulated soil data, the results demonstrate that the soil type and dataset source had a direct impact on the applicability of the slip model on Mars conditions. In contrast, the JLU Mars-2 simulated soil are more similar to the soil near the Zhurong landing site.
# Adaptive Pitch Controller of a Large-Scale Wind Turbine Using Multi-Objective Optimization
## 3 Control Methodology  
The control strategy proposed in this work incorporates two control loops: one loop to maintain the generator speed at its rated value and another loop to reduce the tower f-a displacements. The control scheme is depicted in Figure 3. There is a gain scheduling PI controller regulating the generator speed by actuating on the pitch angle (\(\beta_{\mathrm{PI}}\)). This loop also contains an adaptive feedforward action (\(\beta_{\mathrm{FF}}\)) to compensate for the wind speed, considered a measured disturbance. In addition, there is an adaptive gain to mitigate the tower f-a oscillation that generates an extra pitch control component (\(\beta_{\mathrm{fa}}\)) proportional to the tower f-a velocity \(\dot{x_{\mathrm{t}}}\). This is also added to the pitch control signal.  
It is important to highlight that the control system is underactuated. It has only a single actuator, the pitch (\(\beta\)), and two controlled variables: the generator speed controlled by the PI feedforward controller and the tower f-a displacement compensated by the adaptive gain \(\mathrm{K_{f-a}}\).  
In the next subsections, the procedure of the proposed methodology is described. First, linear models are identified at different operating points within the nominal region. Then, the proposed control scheme of Figure 3 is implemented. The parameters of the blocks of this control scheme are tuned by means of multi-objective algorithms looking  
Figure 3: Proposed control system scheme for the nominal region.  
for a compromise between two conflicting objectives (tower f-a oscillations and generator speed) at each operation point and obtaining multiple possible solutions. Each solution in the Pareto frontier is analyzed through the aforementioned MCDM methods (SAW, TOSIS, VIKOR) and a ranking is generated with the results. Furthermore, the different MCDM ranking methods are averaged to determine the best solution (satisfactory solution) at each operation point. Finally, three solutions (satisfactory, optimal objective 1, and 2 solutions) with their corresponding control parameters are selected to be implemented and compared in the simulation with different wind patterns.
## 3 Methods
### Baseline Model: HiSup
#### 3.2.1 Channel Attention Module  
HiSup utilizes the channel attention mechanism [54] to enhance the feature representation of vertex maps \(F_{ver}\) and semantic features \(F_{seg}\), thereby improving the accuracy and consistency of building shape prediction. The computation method is as follows:  
\[F_{seg}^{\epsilon}=\delta\big{(}C1D\big{(}GAP\big{(}F_{line}+F_{seg}\big{)} \big{)}\big{)}\times F_{seg}+F_{seg} \tag{1}\]  
\[F_{ver}^{\epsilon}=\delta(C1D(GAP(F_{line}+F_{ver})))\times F_{ver}+F_{ver} \tag{2}\]  
where \(C1D(\cdot)\) represents one-dimensional convolution, \(GAP(\cdot)\) represents global average pooling, and \(\delta(\cdot)\) represents the sigmoid activation function.
## 4 Conclusions
Data quality enhancement: The quality and quantity of traffic flow data are crucial for the performance of any predictive model. Future work could focus on increasing the data collection frequency and improving the accuracy of traffic monitoring devices. For example, deploying more advanced sensors and ensuring their proper maintenance can lead to higher data quality. Furthermore, integrating data from multiple sources, such as GPS data from vehicles and mobile applications, could provide a more comprehensive view of traffic conditions. In summary, although this paper still needs to be improved in terms of the indicator system, its prediction effect can be enhanced through optimization and contributes to the study of intelligent transportation. The authors confirm contribution to the paper as follows: study conception and design: G.D.; data collection: R.Z. ; analysis and interpretation of results: Y.D. and H.X. ; draft manuscript preparation: Y.D. and Y.S. All authors have read and agreed to the published version of the manuscript. This research was funded by Shaanxi Provincial Philosophy and Social Science Research Special Project \"Shaanxi MBA Industry-University-Research Collaborative Educational Ecosystem Construction Research\" (2024 HZ0590); Xi'an Polytechnic University, Faculty Doctoral Research Initiative, \"Study on the Path of Data Empowerment Driving Intelligent Transformation of Industry\" (105-256082433); Shaanxi Provincial Innovation Capability Support Program Project \"Research on Mechanisms of Forming Cloud Manufacturing Ecosystems in Shaanxi Province Driven by Digital Economy\" (2023-CX-RKX-142). Not applicable. Data  
Supporting the results of this study are available from the Traffic Police Detachment of the Xi'an Municipal Public Security Bureau, but the availability of these data is limited; these data were used under license for this study and are therefore not available to the public. However, with permission from the Traffic Police Detachment of the Xi'an Municipal Public Security Bureau, the authors can make these data available upon reasonable request. If you need these data, you can contact [EMAIL_ADDRESS]. The authors declare no conflicts of interest.
## 1 Introduction
This system is applicable in scenarios such as inter-campus academic conferences, multi-location remote education, smart city emergency command, and cross-border enterprise live events. In such applications, direct IPv6-based node interconnection eliminates NAT traversal, while SRT ensures low-latency and reliable data delivery, making the system highly suitable for deployment in bandwidth-constrained or dynamically changing environments. The remainder of this paper is organized as follows: Section 2 provides a comprehensive review of related work in P2P video streaming, including P2P-assisted HTTP streaming, decentralized crowdsourced systems, and high-quality P2P streaming architectures. Section 3 presents the detailed system design, covering the IPv6-based P2P architecture, SRT protocol integration, chain topology construction, intelligent self-healing mechanisms, and deployment implementation details. Section 4 describes the experimental methodology and presents extensive performance evaluations, including benchmarks, comparative analysis, and fault tolerance assessments across diverse network environments and hardware configurations. Section 5 presents the experimental results and performance analysis. Section 6 discusses the implications of our findings, limitations of the current approach, and potential future research directions. Finally, Section 7 concludes the paper and summarizes the key contributions.
## 4 Results and Discussion
### Compressor Outlet Pressure
The results show that the charging pressure of 12 MPa can be an optimal condition with ideal technical and economic performance. When the charging pressure is set to 12 MPa, the RTE, ERTE, LCOE, and net output power of the system reach their maximum values (47.72%, 69.74%, 0.0965 USD-kWh\({}^{-1}\), and 27,369 kW, respectively), and the SPC is 0.1855 kWh\({}_{\text{e}}\cdot\)kg\({}_{\text{LA}}\)\({}^{-1}\). Although the investment costs for the components (USD 28.44 million) and materials (USD 2.55 million) are high, it takes only 5.73 years to recup the invested capital, with an expected net return of USD 55.81 million over 20 years. The detailed stream parameters (i.e., \(T\), \(p\), \(m\)) are included in Appendix A, Table 14.
## 5 Experiments and Results
### Results
However, as the communication rounds increase, the performance for all 'C' values tends to converge. The threshold is crossed when the communication round is approximately 3. Figure 11: Execution of P2P FedAVG and FedSGD with f1-score threshold set at 0.9 and Heuristic 2. Figure 12: _Cont._  
Table 2 provides a summary of the communication efficiency of various federated learning algorithms, comparing how many communication rounds are needed to cross a predefined f1-score threshold, which is set at 0.9. Figure 13 illustrates the f1-score trajectories of an SGD-based learning algorithm under IID and non-IID conditions, comparing the scenarios with and without the application of self-training. \begin{table}
\begin{tabular}{l c} \hline \hline
**Algorithms** & \multicolumn{2}{c}{**Communication Rounds to Cross**} \\  & **f1-Score Threshold** \\ \hline Centralized FedAVG IID & 3 \\ \hline Centralized FedAVG non-IID & 4 \\ \hline Centralized FedSGD IID & 4 \\ \hline Centralized FedSGD non-IID & 4 \\ \hline FedAVGP2P Heuristic 0, IID & approx 4 \\ \hline FedAVGP2P Heuristic 0, non-IID & approx 4 \\ \hline FedSGDP2P Heuristic 0, IID & 4 \\ \hline FedSGDP2P Heuristic 0 non-IID & 3 \\ \hline FedAVGP2P Heuristic H1, IID & 4 \\ \hline FedAVGP2P Heuristic H1, non-IID & 4 \\ \hline FedSGDP2P Heuristic H1, IID & 4 \\ \hline FedSGDP2P Heuristic H1, IID & 4 \\ \hline FedSGDP2P Heuristic H1, non-IID & approx 3 \\ \hline FedAVGP2P Heuristic H2, non-IID & 4 \\ \hline FedSGDP2P Heuristic H2, IID & approx 3 \\ \hline FedSGDP2P Heuristic H2, non-IID & 4 \\ \hline FedAVGP2P Heuristic H2, non-IID & 4 \\ \hline FedAVGP2P Heuristic H3, IID & 4 \\ \hline FedAVGP2P Heuristic H3, non-IID & approx 4 \\ \hline FedSGDP2P Heuristic H3, IID & 5 \\ \hline FedSGDP2P Heuristic H3, non-IID & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of communication rounds needed to cross f1-score threshold. Figure 12: Execution of P2P FedAVG and FedSGD with f1-score threshold set at 0.9 and Heuristic 3.
## 1 Introduction  
A major goal of periodontal therapy continues to be regeneration of attachment structures of teeth, including new bone, periodontal ligament (PDL), and cement, which have been destroyed by periodontal diseases or trauma [1]. Periodontal regeneration in intrabony defects, has various approaches including open flap debridement (OFD), bone grafts, guided tissue regeneration (GTR), biologic mediators such as enamel matrix derivatives (EMD) and combination of barrier membranes with grafts [2].  
The periosteum (rich in osteopropgenitor cells) has acquired popularity in the recent times as an alternative to other barrier membranes [2, 3]. The advantages of using an autogenous periosteal membrane are that it requires only one surgical procedure, minimizes any untoward tissue responses during healing and has the potential for stimulating new bone formation [4].  
Several alloplastic graft materials have been used in the past. With the emerging technological advances, even an eggshell can be used as a graft by processing it into nano hydroxyapatite particles [5]. The Eggshell-derived nano-hydroxyapatite (EnHA) meets the required criteria and properties of an ideal graft substitute with its inherent porosity and osteotransconductivity [6; 7]. The human eggshell is a ubiquitous source of hydroxyapatite, and nanotechnology has made it possible to elicit a veritable biomaterial in the form of Eggshell-derived nano Hydroxyapatite (EnHA).  
Microsurgery enhances our surgical skills and improves the overall results. The main advantages are: a small surgical site, minimal tissue trauma and a better surgical outcome [8]. Major principles of microsurgery include improvement of motor skills, an emphasis on passive wound closure with exact primary apposition of the wound edge and the application of microsurgical instrumentation and suturing to reduce tissue trauma.  
Hydroxyapatite is used for sinus grafting, socket preservation, grafting of bony defects, ridge augmentations, as a bone graft expander when combined with autogenous bone, and so on. Eggshell-derived hydroxyapatite seems to be promising graft material with excellent properties for grafting with viable various production techniques. Lack of disease transfer risks, biocompatibility and ease of use makes EHA a viable choice as regenerative material. Eggshell-derived hydroxyapatite is hydrophilic in nature, absorbed by body fluids and blood, so that handling becomes easy for placing it in the surgical site [7].  
The present study is a pioneer in all its aspects. To our knowledge, to date none of the studies have used Eggshell-derived nano Hydroxyapatite (EnHA) for regeneration of intrabony defects and have quantified it with CBCT. Using a periosteal pedicle with EnHA in an intrabony defect and comparing it to EnHA, OFD using a precise technology such as CBCT for bone density is a path finder for future studies.  
Thus, the aim of the following study was to evaluate the regeneration of intrabony defects by the confluence of periosteal membrane and eggshell-derived nano hydroxyapatite graft (EnHA) with a more precise technique microsurgery.
# Progressive Multi-Scale Fusion Network for Light Field Super-Resolution
## 4 Experiments
### Datasets  
In this work, we conducted experiments on both synthetic and real-world datasets. It is more meaningful for LF algorithms to adapt to different datasets with different scenes. As listed in Table 1, HCInew [26], HCIold [27], EPFL [28], INRIA [29], and STFgantry [30] are the six public LF datasets we use, each having different characteristics. STFgantry has the largest LF disparity, including the most distinctive information between two adjacent views compared with other datasets. EPFL, INRIA, and STFgantry are captured by the Lytro Illum cameras, consisting of real-world scenes. Each dataset is divided into the training and testing parts. Specifically, 144 LF images were used for training and 23 LF images were used for testing our method. The original SAIs of LF for these datasets have an angular resolution of \(9\times 9\).
## 3 Software Architecture and Measurement Algorithm
### Coefficient of Variation  
The coefficient of variation (CV) is a standardised measure of dispersion defined by the ratio of the standard deviation and the mean. The CV for the envelope for frequency bin \(k\) is  
\[c_{k}=\frac{\sqrt{m_{2k}-\left(m_{1k}\right)^{2}}}{m_{1k}}. \tag{7}\]  
To estimate the window of first-order stationarity, the CV is calculated to analyse the variability of the LME. This standard measure of dispersion is defined as the ratio between the standard deviation of the LME and its mean. Using the output parameters of the algorithm presented, the CV for frequency bin \(k\) and time window \(T_{i}\) can be calculated as  
\[c_{k}^{T_{i}}=\frac{\sqrt{m_{2k}^{T_{i}}-\left(m_{1k}\right)^{2}}}{m_{1k}}. \tag{8}\]  
For sufficiently long windows (large \(T\)), the coefficient of variation \(c_{k}^{T}\) converges to zero as \(m_{2k}^{T}\rightarrow\ (m_{1k})^{2}\). The CV is here used to find the required window length to obtain a local mean sufficiently close to the true mean of the envelope. This window length \(T_{s}\) is considered the window of stationarity in this paper. The limit of convergence is set at \(-10\) dB; thus, if \((c^{T_{s}})^{2}<0.1\), then each \(T_{s}\) long local average is considered long enough to include a sufficiently varied selection of samples to form a consistent mean.  
For a constant envelope signal, \(c=0\). Then, each observed sample is a good representation of the mean. For independent samples (white noise), the coefficient of variation for a window of length \(T\) is obtained in Equation (9) by inserting the second-order moment \(m_{2k}^{T}\) from Equation (A10) into Equation (8).  
\[c_{k}^{T}=\frac{\sqrt{\left((m_{1k})^{2}+\frac{m_{2k}-(m_{1k})^{2}}{T}\right)- (m_{1k})^{2}}}{m_{1k}}=\frac{1}{\sqrt{T}}\cdot\frac{\sqrt{m_{2k}-(m_{1k})^{2}} }{m_{1k}}=\frac{c_{k}}{\sqrt{T}}. \tag{9}\]  
For a complex AWGN, the envelope is Rayleigh distributed and the relationship between the moments, \(m_{1}=\sqrt{\frac{\pi}{4}m_{2}}\), renders a coefficient of variation of  
\[c_{k}=\frac{\sqrt{m_{2k}\cdot\left(1-\frac{\pi}{4}\right)}}{\sqrt{m_{2k}\cdot \frac{\pi}{4}}}=\sqrt{\frac{4}{\pi}-1}\approx 0.52. \tag{10}\]  
Hence, for AWGN, an average window of just three samples is sufficient to obtain a CV below \(-10\) dB, and only \(27\) samples to obtain it below \(-20\) dB.