esa-sceva/satcom-chunk-collection huggingface dataset:
# An Effective Wide-Bandwidth Channel Access in Next-Generation WLAN-Based V2X Communications
## 7 Conclusions  
In this paper, a fair and efficient channel access scheme for a 20-MHz frame in next-generation WLAN-based V2X communication was proposed. As applications of V2X communication have proliferated recently, more throughput is required for WLAN V2X communication than the throughput supported by IEEE 802.11p. As a result, IEEE 802.11 bd, the so-called Next-Generation V2X (NGV), was established. Standardization is being developed to enhance throughput and communication range performance while maintaining fairness with existing deployed IEEE 802.11p-compliant vehicular devices. In IEEE 802.11 bd, the 20-MHz frame format was proposed to increase channel utilization and to improve throughput performance.  
The channel access scheme for a 20-MHz frame must ensure fair contention with the channel access procedure of 10-MHz frame transmissions in each 10-MHz channel while providing the acceptable transmission latency of a 20-MHz frame. The proposed channel access scheme was designed to jointly optimize fairness and throughput performance. The proposed scheme performs a back-off procedure for the entire 20-MHz bandwidth with PD and ED sensing of each 10-MHz channel using two receivers, leading to fair contention with 10-MHz frame transmission and preventing overproduction.  
Simulation results showed that the proposed scheme met fairness requirements and provided the best 20-MHz transmission performance by avoiding overprotection in a secondary channel. In this paper, alternative 20-MHz channel access schemes were also proposed in order to overcome hardware limitations. However, if only one receiver can be implemented in the station, since ED sensing can only be applied to the secondary channel, then a trade-off occurs between the fairness and the delay of the 20-MHz frame. In alternative 20-MHz channel access schemes, a joint design between fairness and delay performance was applied in order to provide an acceptable trade-off.  
In next-generation WLAN-based V2X communication, with the proposed scheme, the requirements of future V2X applications can be met by providing the maximum throughput performance and minimum impact on the extending channel.  
Conceptualization, H.H.; Data curation, H.H.; Formal analysis, H.H. and W.A.; Funding acquisition, R.Y.K.; Investigation, H.H. and Y.Y.K.; Methodology, H.H.; Project administration, W.A.; Software, H.H.; Supervision, R.Y.K. and W.A.; Validation, Y.Y.K., R.Y.K., and W.A.; Writing--original draft, H.H.; Writing--review and editing, Y.Y.K., R.Y.K., and W.A. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT& Future Planning (NRF-2017R1A2B4003987).  
The authors declare no conflict of interest.
# Dynamic Vertical Mapping with Crowdsourced Smartphone Sensor Data
## 4 Evaluation
### Evaluation
#### 4.4.2 Building 2: Building B  
Building B consisted of five floors and an additional ground floor. The true height for each floor is available in Table 8. We have collected data following the same strategy as mentioned above. Twenty-five datasets were collected from Building B in Munich. All collected datasets are available in [20]. After following the procedure described above, for smoothing, clustering and identifying the OITransition, we extracted the reference pressure and then the altitude of every pressure reading that belonged indoors. Finally, once we translated all the pressure readings into altitudes, they were imported to the elbow method for floor number identification.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Floors** & **0** & **1** & **2** & **3** & **4** & **5** \\ \hline Real floor altitude (m) & 0 & 5.3 & 10.68 & 15.05 & 19.47 & 24.41 \\ Estimated floor altitude (m) & 0 & 4.81 & 10.03 & 14.48 & 18.86 & 23.74 \\ Error & 0 & 0.48 & 0.65 & 0.57 & 0.61 & 0.66 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Ground truth, estimated altitude and error for Technical University of Munich (TUM) Main Campus.  
Figure 15: Elbow method result for three test buildings (**a**) TUM Main Campus, (**b**) Building B, (**c**) Deutsche Akademie.  
As can be seen in the elbow method results, in Figure 15b, the number of floors (i.e., clusters, \(K=6\)) in our dataset has been identified correctly for the aggregated dataset as well as for the May and June datasets, for which all floors of the building were visited. The threshold selected for all datasets was 99.12% for the distortion percentage, and for the clustering algorithm, the K-means algorithm was selected. Additionally, it can be seen that for the July dataset, the number of floors predicted was two (\(K_{temp}=1\)), as the four higher floors were not visited during this month.  
Regarding the height estimation, the corresponding estimated floor altitude and ground truth are presented together in Figure 16c, as well as in Table 8. As can be seen, in the aggregated dataset, the maximum error was at 1.12 m, while the minimum error was at 0.31 m. In the figure, it can also be seen that for the July dataset, only two floors were visited.
## 3 Design of SOCA
### Construction of Candidate Slot List  
The ship station constructs a candidate slot list for the TDMA channels by considering the NIT and VDES frame maps. This overall candidate slot list encompasses the individual candidate slot lists for each TDMA channel. The individual candidate slot list for each TDMA channel can consist of free, available, and allocated slots, in compliance with the candidate slot selection rules specified in the VDES standard [22]. The candidate slot list (**S**) for the TDMA channels can be represented by  
\[\textbf{S}=\left[\textbf{CS}_{1},\textbf{CS}_{2},\cdots,\textbf{CS}_{i},\cdots, \textbf{CS}_{N_{ch}}\right], \tag{1}\]  
where \(\textbf{CS}_{i}\) represents the candidate slot list of the \(i\)-th TDMA channel. \(N_{ch}\) represents the total number of TDMA channels within a TDMA frame. \(\textbf{CS}_{i}\) can be represented by  
\[\textbf{CS}_{i}=\left[sn_{i,1},sn_{i,2},\cdots,sn_{i,j},\cdots,sn_{i,n_{cs},i} \right]\ (0\leq n_{cs,i}\leq 15), \tag{2}\]  
where \(sn_{i,j}\) represents the slot number of the \(j\)-th candidate slot in the candidate slot list for the \(i\)-th TDMA channel. \(n_{cs,i}\) represents the number of candidate slots in the \(i\)-th TDMA channel.  
Upon completing the construction of the candidate slot list for the TDMA channels, the ship station classifies the candidate slots for each TDMA channel into interference and non-interference slots and calculates the number of each for every TDMA channel. The interference slot refers to the candidate slot that is included in the SI of its own or at least one neighboring station, while the non-interference slot is not included in any of these SIs. Subsequently, it constructs lists \(\textbf{N}_{IS}\) and \(\textbf{N}_{NIS}\), containing the number of interference slots and non-interference slots for each TDMA channel, respectively. \(\textbf{N}_{IS}\) and \(\textbf{N}_{NIS}\) can be represented by  
\[\textbf{N}_{IS}=\left[n_{IS,1},n_{IS,2},\cdots,n_{IS,i},\cdots,n_{IS,N_{ch}} \right], \tag{3}\]  
\[\textbf{N}_{NIS}=\left[n_{NIS,1},n_{NIS,2},\cdots,n_{NIS,i},\cdots,n_{NIS,N_{ch} }\right], \tag{4}\]  
where \(n_{IS,i}\) and \(n_{NIS,i}\) refer to the number of interference slots and non-interference slots included in \(\textbf{CS}_{i}\), respectively. The ship station then performs TDMA channel selection for its VDE-TER data transmission.
# Central Kurdish Text-to-Speech Synthesis with Novel End-to-End Transformer Training
## 2 TTS Literature Review
### Two-Stage Systems  
Two-stage TTS systems introduce an intermediate step between text processing and waveform generation, typically predicting a mel-spectrogram before synthesizing the final speech output. This approach separates the linguistic and acoustic modeling stages, allowing for more fine-grained control and potentially higher-quality synthetic speech.  
The SC-GlowTTS system [18] is a flow-based multi-speaker text recognition system that takes the predicted parameters of an external speaker embedding into account. SNAC [19], on the other hand, utilizes a coupling layer to explicitly normalize the input.  
The basic Glow-TTS [20] architecture consists of a flow-based determinate duration predictor, a transformer-equipped encoder, and a flow-dependent decoder. The transformer-based encoder produces a linear approximation of the prior distribution mean by translating the input tokens' phonetic embedding into a representation with an 80-dimensional structure. The z-sampling method can also be utilized to express the distribution's z-sampled value:  
\[z=\mu+T\epsilon, \tag{1}\]  
In training, the duration predictor only predicts the mean \(\upmu\) and the temperature T, while at inference time, it chooses a value of T that is usually smaller than 1. A latent representation of the distribution is then sampled from the prior data to generate a mel-spectrogram.  
In 2017, [PERSON] et al. introduced the concept of transformer TTS [12]. In 2019, [PERSON] et al. [7] tested the effectiveness of this technology by developing an algorithm that can predict the mel-spectrogram for English phonemes. The evaluation of the transformer TTS system showed that it was very promising, but it was not feasible to use it in a production setting because the auto-regressive approach was time-consuming. The evaluation of the mean opinion score (MOS) of a phonemicized dataset using a non-autoregressive model was not significantly different from the results when using the transformer TTS system. The authors also used a pitch prediction module with FastPitch [21] to complement the duration predictor in their work from the Tactron2 model that they introduced [1]. The authors claimed that the quality of their results was similar when using durations and phonemes from a Montreal forced alignment (MFA) model [22].  
Diff-TTS [23] uses a diffusion probabilistic model to first generate mel-spectrograms, which are then converted to speech using advanced vocoder models like HiFi-GAN [9]. More recently, models such as WaveGrad 2 [24] and EfficientTTS 2 [25] focus on optimizing the two-stage process for faster and more efficient synthesis without compromising on quality. In 2023, the MelStyleTTS [26] proposed a style transfer technique for mel-spectrograms, allowing for greater expressiveness in synthetic speech. Two-stage systems have been shown to produce more natural and expressive speech compared to their one-stage counterparts, although they may introduce additional latency and complexity in the synthesis pipeline.
# Unsupervised Anomaly Detection and Segmentation on Dirty Datasets
## 3 Proposed Method
### Trimming Patch-Level Gaussian Distribution
\(U\) is the number of consecutive occurrences of the target window, which can be used to ensure that the sliding window can pass the jump points in the scatter plot. \(\alpha\) and \(\beta\) are hyperparameters, \(T_{i,j}\) is the Mahalanobis distance threshold at patch position\((i,j)\). \(W_{i,j}\) is the first window in the target window sequence \(SEQ\) at patch position \((i,j)\). \[Label^{n}= \begin{cases}0,&\text{if }M(x_{i,j}^{n})<T_{i,j},i\in[1,h^{ \prime}],j\in[1,w^{\prime}]\\ 1,&\text{else}\end{cases} ,n\in[1,...,N] \tag{12}\]  
\[D_{clean}=\{x^{n}|Label^{n}=0,n\in[1,N]\} \tag{13}\]  
After determining distance thresholds, we can select normal samples from the dirty dataset. In Equation (12), \(Label^{n}\) is the label of sample \(n\) in the dataset, 0 indicates normal sample, 1 indicates anomaly sample. Considering that anomaly regions have different scales, fixed-scale patch regions do not apply to all anomaly region scales. If an anomaly region is slightly larger than the patch scale, it is difficult to ensure that adjacent patches containing subtle anomalies are filtered out. It is insurance that we exclude this sample once a sample contains an anomaly patch. When the Mahalanobis distance of all patches of the sample \(x^{n}\) is less than the corresponding threshold, we consider sample \(x^{n}\) as a normal sample. Otherwise, we consider sample \(x^{n}\) as an anomaly sample. According to this criterion, we can select some samples from the dirty dataset, and these selected samples are considered normal samples which constitute the clean set \(D_{clean}\) shown in Equation (13).
## 1 Introduction
### Contributions of This Paper  
This paper focuses on lightweight learning algorithms that can be implemented on IoT devices. MAB algorithms are the simplest reinforcement learning method. In the MAB algorithm-based channel selection methods, the channel can be selected only based on ACK information. The communication performance may be improved by using some other information besides ACK information. However, it takes time to obtain the information, which may increase energy consumption. Since the state information other than ACK information is necessary for the Q learning method or deep reinforcement learning method in related work, which may reduce energy efficiency. Hence, compared to the other reinforcement learning method, the MAB methods considered in this paper may achieve higher energy efficiency and are more suitable for battery powered IoT devices. On the other hand, although there are several works on MAB-based channel selection that are implemented on IoT devices, channel selection under massive heterogeneous IoT networks is not considered in the related work.  
To support substantial IoT devices in the next-generation communication systems, the effectiveness of the MAB-based channel assignment methods in massive heterogeneous IoT networks is verified in this paper. Specifically, the effectiveness of the MAB-based channel assignment method in a massive heterogeneous IoT network consisting of 10,000 devices is firstly evaluated by simulations. Subsequently, the performance in frame success rate (FSR) is evaluated using the 50 Wi-SUN IoT devices with IEEE 802.15.4g/4e protocol in the IoT networks coexisting with the LoRa devices. The contributions of this paper can be summarized as follows.  
* The channel assignment problem is formulated as a MAB problem and apply MAB algorithms to solve the formulated problem in massive heterogeneous IoT networks.
* The effectiveness of the MAB-based channel assignment methods in FSR is evaluated and verified under a massive IoT network with 10,000 IoT devices.
* The MAB-based channel assignment methods is implemented on actual Wi-SUN IoT devices and evaluates the performance in FSR under the IoT heterogeneous network coexisting with LoRa devices.  
The remainder of this paper is organized as follows. Section 2 summarizes the frequency standards of major countries and LPWA networks that are widely deployed worldwide. Section 3 describes the system model and problem formulation. Section 4 presents the MAB-based channel assignment methods. Section 5 evaluates the performance in FSR of the MAB-based channel assignment methods through simulations, assuming a massive heterogeneous IoT networks with 10,000 IoT devices. Section 6 describes the experiments conducted in a real IoT network where Wi-SUN IoT devices coexist with LoRa devices. Finally, Section 7 provides the concluding statement.
# Adaptive Point Learning with Uncertainty Quantification to Generate Margin Lines on Prepared Teeth
## 2 Related Work
### Point Cloud Generation  
Three-dimensional point cloud generation is a critical task in computer vision. Various DL models have been developed to tackle this problem, as comprehensively reviewed in [33]. Point-based methods, such as PointNet [34] and PointNet++ [35], directly process the raw 3D coordinates of point clouds and have shown significant promise due to their ability to handle unordered point sets and learn local and global features effectively. Convolution-based methods [36] apply 3D convolutions to voxelized representations of point clouds, benefiting from the regular grid structure of voxels but are computationally expensive and limited by resolution constraints. Graph-based methods [37] represent point clouds as graphs, leveraging graph neural networks to capture relational information between points, thus achieving more accurate completions. Generative model-based approaches, including generative adversarial networks (GANs) [38], generate plausible completions by modeling the underlying data distribution. Recent improvements have been made in the field of shape generation [39; 40] and specifically in dentistry [1]. Conditional GANs were used for generating crowns that reduce the occlusal problems seen in restorations [41]. Recently, transformer-based architectures have been applied to point cloud generation tasks, adeptly capturing long-range dependencies within point clouds. Notable examples include 'point transformer', which uses self-attention mechanisms to learn interactions across long distances; PT2 PC [42], which combines transformers with point cloud processing techniques for fine-grained completions; PoinTr [43], which employs a transformer encoder-decoder architecture to model global features for point cloud completion; and AdaPoinTr [44], which leverages transformer architectures with geometry-aware blocks and adaptive query generation for point cloud completion. Despite these advancements, challenges such as learning localized details from unstructured point clouds and generating fine-grained predictions remain.
## 2 Data and Methodology
### Methodology
#### 2.3.4 Statistical Metrics  
To fully evaluate the performance of the estimated SSM, three metrics were used, including the determination coefficients (R\({}^{2}\)), the mean absolute error (MAE), and the root mean square error (RMSE). The R\({}^{2}\) indicates the fit degree of the model, and the larger the R\({}^{2}\), the better the fitting effect, and the stronger the robustness of the model. The RMSE indicates the prediction accuracy of the model, and the smaller the RMSE, the higher the prediction accuracy of the model. The MAE is the average of the absolute value of the deviation between all individual measured values and estimated values. Compared with the mean error, the MAE will not be offset by positive and negative because the deviation is absolute. Therefore, the MAE can better reflect the actual situation of the predicted error, and the smaller the MAE, the better fitting effect of the model.  
The statistical metrics are calculated as follows:  
\[\text{R}^{2}=\frac{\sum\limits_{i=1}^{n}\left(S_{i}-\overline{S}\right)^{2}}{ \sum\limits_{i=1}^{n}\left(M_{i}-\overline{S}\right)^{2}} \tag{12}\]  
\[\text{MAE}=\frac{1}{n}\sum\limits_{i=1}^{n}\left|S_{i}-M_{i}\right| \tag{13}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}\left(S_{i}-M_{i}\right)^{2}} \tag{14}\]  
where \(n\) is the number of validation values during the day, and \(M_{i}\) and \(S_{i}\) are the in situ measured and estimated SSM, respectively.
## 5 Conclusions  
The flow past three equilateral-triangular-arranged cylinders is investigated for spacing ratios \(L/D=1.1\)-3.5 at a super-critical Reynolds number \(Re=3\times 10^{6}\). Improved delayed-detached eddy simulation (IDDES) based on SST \(k-\omega\) model is utilized to solve the flow field. With the aim to better understand the fluid dynamics of the configuration of three cylinders, the spanwise vorticity (\(\omega_{2}\)), force coefficients, and its RMS, Strouhal numbers (\(S_{t}\)), and pressure distribution (\(C_{p}\)) are discussed. The following is a summary of the conclusions:  
1. Five flow regimes are identified depending on the spacing ratios, i.e, single bluff body flow (\(L/D=1.1\)), deflected gap flow (\(L/D=1.2\)-1.4), anti-phase flow (\(L/D=1.5\)-2), in-phase flow (\(L/D=2\)-3.5), and co-shedding flow (\(L/D>3.5\)).
2. When in deflected gap flow, the downstream cylinder with a wide wake experiences lower drag and higher lift (absolute value and RMS), compared to the cylinder with narrow wake. However, their vortex shedding frequencies are identical. In addition, the sum of lift forces of the three cylinders at the deflected gap flow is not zero, either a positive or negative lift may exist.
3. When \(L/D<1.5\), the separation points on the free-flow-side of the downstream cylinders are always lower than that of the single cylinder, indicating that the violent flow at the gap also causes the flow separation on the outer side of the cylinder to advance. When \(L/D>3.5\), the separation point of the cylinder in upper stream is close to that of single cylinder, indicating that the minimum \(L/D\) for negligible interaction among the cylinders is 3.5.  
In conclusion, in this paper, we systematically study the characteristics of different flow regimes of three cylinders with equilateral-triangular arrangement. Fluid forces, flow patterns, vortex shedding frequencies, phase differences, and wake interactions are discussed in detail. Compared with previous studies, this paper focuses on the super-critical Reynolds number regime and a more detailed \(L/D\) division is completed. The results of this paper deepen the understanding of the fluid interference of flow past cylinders.  
Figure 22: Variation of separation angles of cylinders with \(L/D\).  
Author Contributions: Methodology, B.Y.; Validation, Z.J.; Data curation, M.Z.; Writing--original draft, M.Z.; Writing-review & editing, B.Y.; Supervision, D.G. and G.Y. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Class A) (Grant No. XDA22040203) and the Strategic Priority Research Program of the Chinese Academy of Sciences (Class B) (Grant No. XDB22020000). Conflicts of Interest: The authors declare no conflict of interest.