esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Inertial Navigation and IMU Error Model
### Motion Estimation  
After the refinement of feature matching by implementing the multilayer RANSAC scheme, we also compute the most reasonable estimate of the matrix \(\mathrm{F}\). According to the primary principles of epipolar geometry and the properties of fundamental matrix [30], we have the following relationship between the motion of the camera and fundamental matrix:  
\[\mathrm{E}=\mathrm{K}^{\mathrm{T}}\mathrm{F}\mathrm{K}\mathrm{=}(\vec{l} \times)\cdot\mathrm{R} \tag{13}\]  
where \(\vec{l}\times\) is the skew symmetric of camera translation; \(\mathrm{R}\) is the rotation matrix; \(\mathrm{K}\) contains the intrinsic calibration parameters and \(\mathrm{E}\) is the essential matrix. By implementing the singular value decomposition (SVD), the camera rotation \(\mathrm{R}\) and unit translation vector \(\vec{l}\) regarding two adjacent images can be finally retrieved and serve as the vision-based navigation parameters for integration purposes. It is worth noting that since the essential matrix \(\mathrm{E}\) has only five degrees of freedom, the 3\(\times\) 1 vector \(\vec{l}\) is resolved up to scale and is accordingly subject to an ambiguity issue in terms of the translation magnitude. If the absolute magnitude of the translation between two successive images is extracted by other approaches, which we will elaborate in Section 5, the scale ambiguity of \(\vec{l}\) can be finally resolved.
# A Predictive Reliability Model to Assess the Performance of Photovoltaic Systems
### Output and System Responses
They used nine variables of the duration of sunshine (n), (N) as the maximum of (n), maximum, minimum and average ambient temperatures, water vapor pressure (VP), relative humidity (Rh), extraterrestrial radiation (Ho) and sea level pressure (P). The results they obtained showed that the optimum sets of inputs differed from one city to another due to different solar radiation and climate conditions. [PERSON] et al. [15] developed an artificial neural network (ANN) model to predict solar radiation. They used meteorological data for the previous 10 years from databases of different locations. The ANN model they used is based on root mean square error (RMSE), minimum mean absolute error (MAE), and maximum linear correlation coefficient (R). The authors of this work confirmed the accuracy of this ANN model to predict the monthly average global radiation. An efficient ANN model was proposed in [16] to predict photovoltaic power. They used different learning algorithms and training data from different databases to predict the power production one day ahead with short computational time. The results they obtained showed an enhancement of up to 1%, 1.5% and 15% for the MAE (mean absolute error), R2 (coefficient of determination) and RMSE (root mean square error) as performance metrics, respectively. In [17], an optimization technique based on a genetic algorithm and wavelet ANN was provided to forecast the daily solar radiation. They used the genetic algorithm to optimize the weights inputs, outputs, translation factors and scale factors. The daily radiation data, cleanness index and temperature were used for training the ANN using the gradient descent method. They obtained satisfactory results for the daily solar radiation. Statistical regression techniques were suggested in [18] to demonstrate the robustness of the day of the year-based models for solar radiation prediction. The authors evaluated the performance of the selected models by analyzing different statistical indicators. This paper is organized as follows: An introduction to the PV system with a brief review is given in Section 1. The proposed methodology and used materials are described in Section 2. The results are presented and discussed in Section 3. At the end of this paper, the work is concluded.
# An Overview of Indoor Positioning and Mapping Technology Standards
## 5 Conclusions  
The emergence of indoor based applications and markets promotes advances in IPMN technologies and standards. Different techniques, technologies, mechanisms, methodologies, systems, platforms, and standards have been developed for indoor localization services and applications. However, IPMN applications still face several technical and non-technical challenges, such as quality of positioning service, availability of indoor maps,  
Figure 11: Sequence diagram of location service process (From GB/T 35629-2017 [103]).  
Figure 12: Metadata of cooperative positioning service.  
and location privacy. Currently there is no proposed solution that can satisfy all indoor application requirements. One of the biggest challenges is a lack of standardization. The existing IPMN standards published by different SDOs, such as ISO, IEEE, and OGC differ from each other, and they focus on different viewpoints of indoor positioning, mapping, and navigation, such as automatic identification and data capture techniques (RTLS), communications technologies in transportation (ITS), city and indoor models (CityGML and IndoorGML), building information model (BIM), and robot navigation (MDR).  
This paper updates the review of indoor positioning and mapping technologies and standards. As we re-think the results, we present an architecture for IPMN, and standards based on the architecture for our project. We found by our project experience that the coordinate IPMN standards system needs to be formed from the concepts, models, and technologies to the industrial application schemas. Thus, we proposed several standards including hybrid indoor positioning, content model for indoor map, data collection, GIS model, symbols, and indoor positioning service. The main purpose here is to provide readers and researchers with guidance to IPMN technologies and standards; and, for standard developers to have a way to develop complementary standards to form the backbone for an integrated IPMN standards system.  
Conceptualization and standards review, Y.D.; indoor positioning methodology review, H.A.; writing--original draft preparation, Z.D.; content model for indoor maps, W.G.; data collection standard, J.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Programs, grant number 2016 YFB0502201, and National Natural Science Foundation of China, grant number 61971316.  
Not applicable.  
The authors declare no conflict of interest.
## 5 Pathological Speech Detection  
The main aim of this work is to design a machine learning model that is able to discriminate pathological speech. As was indicated above, from the machine learning point of view, this is a binary classification task. The following list describes the sequence of steps for creating the system for pathological speech detection:  
1. _Export of data:_ Recordings of the vowels _/a/_, _/i/_, and _/u/_ were obtained from the freely available Saarbrucken Voice Database.
2. _Feature extraction:_ From the exported samples, it was necessary to extract speech features that express voice quality and potential pathological disorders present in the voice. Whole feature extraction was performed in Python. The types of features are described in Section 3.
3. _Dimensionality reduction:_ In order to improve the accuracy of the classification, the selection of the features and principal component analysis were performed. Both methods are described in more detail in Section 3. The most relevant features are depicted in Table 1.
4. _Visualization of features:_ In order to get a better view of the data structure, we visualized features using the PCA method. These visualizations are shown in Figures 1-3. For this visualization, we used the first three principal components from the PCA output.
5. _Model training:_ Each machine learning model used in this work (SVM, KNN, RFC) which was designed for the classification of samples must be trained prior to classification. A combination of different features and genders was tested. After this step, we should have classifiers with their optimal parameters, because tuning the hyperparameters of the model is also included in this step. The detailed description of the training and cross-validation procedure is provided in the Experimental Results section.
6. _Model evaluation:_ The created model was tested on new test data, which means that these data were not part of the training process and the classifier did not come into contact with them. The graphical design of the sequence of steps is shown in Figure 4.  
_Appl. Sci._**2018**, \(8\), 1927  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Feature** & **Vowel** & **Intonation** & **FS Score** \\ \hline
1 & Shimmer:APq5 & A & N & 0.166 \\
2 & Shimmer:APQ5 & A & LHL & 0.156 \\
3 & Jitter:DDP & A & H & 0.139 \. & Jitter:RAP & A & H & 0.139 \. & Spectral Roll-off (min.) & U & H & 0.139 \\
6 & MFCC-6 (mean) & A & LHL & 0.138 \\
7 & Jitter:PPQ5 & A & H & 0.131 \\
8 & Jitter:LOCAL & A & N & 0.130 \\
9 & Jitter:PPQ5 & A & LHL & 0.129 \. & Jitter:DDP & A & L & 0.129 \\ \hline \hline \end{tabular}
\end{table}
Table 1: /hIThe most important features, as selected by feature selection, including the vowel being pronounced and intonation (L—low, N—neutral, H—high, LHL—changing low-high-low).  
Figure 1: Visualization of mixed gender samples, with 1560 features for each sample.  
Figure 2: Visualization of female samples, with 1560 features for each sample.
## 6 Conclusions  
In conclusion, these findings suggest that U-Net with MPT is a highly effective and computationally efficient approach for CBCT bone segmentation. While SegNet also performs well, its higher parameter requirements pose a significant drawback.  
The integration of accurate segmentation with advanced 3D modeling and a user-friendly interface significantly enhances the practical utility of the entire workflow. The tool not only technically improves the bone segmentation process in CBCT images, but also represents a significant advancement in terms of usability. By reducing the need for user intervention, the process is more accessible and practical for everyday clinical use. This can lead to broader adoption of the technology in clinical settings. The positive results from the preliminary experiments suggest that this bone segmentation and modeling tool can be generalized to various anatomical regions and different types of medical imaging, including veterinary. Future work will involve further validation on larger datasets and additional anatomical regions to fully establish the generalizability of this method. Moreover, additional case studies will be investigated to apply the tool for fracture visualization, bone growth assessment, and preoperative planning for other surgical procedures.  
Conceptualization, E.T.; Methodology, E.T.; Software, E.T.; Data curation, E.T.; Writing--original draft, E.T.; Writing--review & editing, E.T.; Project administration, L.B.; Funding acquisition, L.B. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The informed consent was waived because this study used ex vivo anatomical preparations. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy. The authors declare no conflict of interest. The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CBCT & cone beam computed tomography \\ MPT & multiplanar training \\ MV & majority voting \\ JI & Jaccard index \\ DC & Dice coefficient \\ \end{tabular}
## 3 Results and Discussion
### Optimization of Single HAP Conditions
#### 3.1.2 Precipitation Temperature  
Precipitation temperature was a crucial factor for the solubility and crystallization rate, the influence of which was evaluated with various levels, \(-12\), \(-6.5\), \(0\), \(4.6\), \(12\), \(15\), \(21\), and \(25\ ^{\circ}\)C. Other conditions adopted were a spiramycin concentration of \(10\) mg/mL, shear rate of \(5000\) rpm, volume ratio of antisolvent to solvent of \(5\) mL/mL, precipitation time of \(10\) min, and a dripping speed of solvent of \(50\) mL/h. From Figure 3b, the MPS decreased slowly and then increased drastically when the precipitation temperature ranged from \(-12\) to \(4.6\ ^{\circ}\)C and \(4.6\)-\(25\ ^{\circ}\)C, respectively. The spiramycin solubility in DMSO and water-mixed solution was improved under high temperatures, thus leading to a low supersaturation and a slow nucleation rate. Larger numbers of nuclei formed hindering the diffusion between solvent to antisolvent, causing the aggregation of spiramycin particles [36; 37]. Supersaturation highly influenced nucleation, thus further affecting the particle size of spiramycin. Under low temperatures, an improvement of the supersaturation and nucleation rate occurred, resulting in smaller particles. The minimum MPS was achieved at the precipitation temperature of \(4.6\ ^{\circ}\)C; thus, the optimum precipitation temperature was \(4.6\ ^{\circ}\)C.
# Predicting Wheat Potential Yield in China Based on Eco-Evolutionary Optimality Principles
## 2 Material and Methods  
### Data  
This study encompasses the entirety of China, where the wheat is cultivated in most provinces. The representation of the wheat-planted area proportion is adopted from the EARTHSTAT database (accessible at: [[http://www.earthstat.org/](http://www.earthstat.org/)]([http://www.earthstat.org/](http://www.earthstat.org/)), last accessed: 12 November 2024). This particular dataset is an amalgamation of diverse data sources, encompassing local agricultural surveys and RS images [38]. Throughout the future projection, we assume a consistent wheat cultivation area, i.e., utilizing an unchanging map depicting the proportion of wheat-planted areas. Notably, the water supplement strategies for irrigated and rainfed wheat diverge; precipitation imposes growth restrictions on nonirrigated wheat, whereas the impact of this constraint can be mitigated through irrigation practices. We obtained the map of the two irrigation schemes from the MIRCA2000 dataset, which integrates observations from a variety of data sources [6]. The spatial distribution of China's wheat-planted area proportion and the two irrigation schemes is visually depicted in Figure 1.  
The future projection hinges on climate data extracted from forthcoming scenarios. The prediction of wheat PY necessitates the inclusion of six climate indicators: air temperature (Ta), vapor pressure deficit (VPD), ambient CO\({}_{2}\) concentration (Ca), precipitation (Pre), surface pressure (Press), and downwelling shortwave radiation (SW). The annual Ca values corresponding to the two selected pathways were provided by [PERSON] et al. [39]. We sourced meteorological metrics from the Inter-Sectoral Impact Model Intercomparison Project (ISMIP-3b). This dataset underwent bias correction through comparison with the corrected ERA5 observational data [40]. For this study, two plausible SSPs are considered [41]: the SSP126 pathway characterized by low radiative forcing (2.6 W/m\({}^{2}\) in 2100) and the SSP370 pathway representing a medium to high radiative forcing (7.0 W/m\({}^{2}\) in 2100). To drive the potential yield prediction, the average outputs of five models are employed: GFDL-ESM4, MPI-ESM1-2-HR, MRI-ESM2-0, IPSLCM6A-LR and UKESM1-0-LL.  
To evaluate the resulting PY from this study, we also collect wheat PY prediction from other typical crop models, including CLMcrop [42], EpicBoku [43], EpicIIASA [44], EpicTAMU [45], Gepic [46], and ORCHIDEE-crop [47].
## 3 Interference Situational Aware Beam Pointing Optimization
### Interference Situational Awareness  
For a given UT, the visible satellites change dynamically, as shown in Figure 3. Thus, the visible satellite set is as follows:  
\[\mathcal{V}=\{\mathcal{S}|el^{\mathcal{S}}\geq el_{th}\}. \tag{8}\]  
where the elevation angle of satellite \(el^{\mathcal{S}}\) observed by UT can be calculated using the location of satellite and UT, \(el_{th}\) is the minimum visibility elevation angle. According to the visible satellite set \(\mathcal{V}\), the serving satellite \(S\) is selected based on the maximum remaining service time, while the other satellites are interfering satellites \(I\). Due to satellite movement, the interfering satellites observed by the UT are time-varying. The time-varying interfering satellite set at the time index \(n\) is expressed as \(I(n)=\{I_{1}(n),I_{2}(n),\ldots,I_{Q}(n)\}\), where \(Q\) is the total number of interfering satellites and varies with time. To obtain the AoA and AoD of communication and interfering links, coordinate conversion is implemented. Firstly, local coordinate systems (LCSs) of the satellite and UT are defined. Then, the location information of the satellite and UTs in the global coordinate system (GCS) represented by the Earth-centered Earth-fixed (ECEF) coordinate system should be converted to the LCS for each satellite and UT. The detailed conversion steps are provided in 3 GPP TR 38.901 [34]. As shown in Figure 4, \(S\), \(I\), and \(U\) denote the serving satellite, interfering satellite, and UT. \(O\)-\(XYZ\) is the GCS, \(O\)-\(X_{S}Y_{S}Z_{S}\) is the local coordinate system of serving satellite (LCS-S), \(O\)-\(X_{I}Y_{I}Z_{I}\) is the local coordinate system of interfering satellite (LCS-I), and \(O\)-\(X_{U}Y_{U}Z_{U}\) denotes the local coordinate system of the UT (LCS-U). Additionally, \(C_{S}\) and \(C_{I}\) denote the beam center of the serving and interfering satellite, respectively. \(C_{U}\) is the beam center of the UT antenna. Figures 5 and 6 show the serving satellite and UT antenna placed in LCS-S and LCS-U, respectively. The total number of antenna elements of the satellite and UT are \(N_{t}=N_{tx}\times N_{ty}\) and \(N_{r}=N_{rx}\times N_{ry}\). \(N_{tx}\) and \(N_{ty}\) are the number of antenna elements along  
Figure 3: The variation in visible satellites. Figure 2: The scheme of interference situational aware beam pointing optimization. x-axis and y-axis at the transmitter, while \(N_{rx}\) and \(N_{yy}\) are the numbers of antenna elements along x-axis and y-axis at the receiver. \(d_{rx}\), \(d_{ty}\), \(d_{rx}\), and \(d_{ry}\) are the distance between adjacent antenna elements along x-axis and y-axis at the transmitting and receiving antenna. \(\varphi_{t}^{S}\) and \(\theta_{t}^{S}\) are defined as the azimuth and elevation angle of AoD of the communication link, while \(\varphi_{r}^{S}\) and \(\theta_{r}^{S}\) are the azimuth and elevation angle of AoA of the communication link. Figure 4: The GCS and LCS coordinate systems. Figure 5: The serving satellite antenna placed in LCS-S.  
Figure 6: The UT antenna placed in LCS-U.
# Small Language Models for Speech Emotion Recognition in Text and Audio Modalities
## 2 Related Work
### Architectures of SLMs
#### 2.1.2 Decoder-Only Models  
Decoder-only models are characterized by their auto-regressive approach, where each new token is predicted based only on the elements previously generated in the sequence. Although encoder-only architectures have been very successful in text processing, they are not typically used for audio classification. These models are particularly well suited for text generation tasks, but can be used for any NLP task. Within this category, GPT [12] and its variants are the most well-known family of SLMs. Just as BERT inspired the development of several encoder-only models, GPT has inspired the emergence of several notable decoder-only models, including:  
* GPT-2 [34]: Second version of OpenAI's GPT model. The model's architecture is built by stacking multiple transformer decoder blocks composed of self-attention and linear layers.
* GPT Neo [35]: Trained on a larger dataset, it uses a similar architecture to GPT-2, but differs by using local attention in a different layer with a window size of 256 tokens.
* Pythia [36]: Uses a similar architecture to the previous ones, optimized for scalability. This model features changes from GPT-2, such as the use of rotary embeddings instead of positional embeddings.
* OPT [37]: Uses an architecture similar to GPT, but optimized for efficiency. Some versions of this model achieve accuracy comparable to GPT-3 on various NLP tasks, with a substantially lower carbon footprint during training.
* XLNet [38]: Introduces a permutation approach to an auto-regressive model, allowing it to learn bidirectional context without the need to mask tokens during training, as is done in BERT-like models.
# Analytic Combinatorics for Computing Seeding Probabilities
## 4 Results
### Accuracy of the Approximations
The reason is that the theoretical accuracy of the estimates increases with the read size \(k\), as justified by Theorem 2, but the errors in numerical approximations also increase and they finally dominate the error. In spite of this artefact, it is clear that the relative error of the estimates remains low for very large read sizes. This means that the estimates are sufficiently close to the exact value, and that the calculations are numerically stable. Figure 10 also confirms that the value of \(p\) has a large influence on the convergence speed of the approximation. For high values of \(p\), the relative error drops faster than for low values of \(p\), as argued above. The main reason for the numerical stability of the estimates is that the polynomial equations to solve can be properly represented with double precision numbers. For instance, in the extreme case of seeds of size 30 with a substitution rate \(p=0.5\), the leading term of \(Q(z)\) in Proposition 2 is \(-p(1-p)^{29}z^{30}\approx-10^{-9}z^{30}\). This is several orders of magnitude above the machine epsilon (approximately equal to \(2.2\times 10^{-16}\) on most computers), sufficient to guarantee that this term will not underflow during the calculations, and thus that the dominant singularity will be computed with an adequate precision. The same applies for the other error models, as long as the rate of sequencing errors remains above 0.5, which is the case in the vast majority of practical applications. In summary, the estimates presented above are accurate and numerically stable. In the case of short reads with low error rate, the precision may be limiting for some applications, but the approximations can be replaced by exact solutions. The approach presented here is thus a practical solution for computing seeding probabilities. Figure 9: _Example worst case for seeding with substitutions only._ The analytic combinatorics estimates of Proposition 2 are benchmarked against 10,000,000 random simulations. Shown on both panels are the probabilities that a read of given size contains a seed of size \(\gamma=17\), either estimated by random simulations (dots), or by Proposition 2. The curves are drawn for \(p=0.005\), \(p=0.010\) or \(p=0.015\) (from top to bottom). The largest difference between the estimates and the simulations is around 0.015.
# A Survey on Optimal Channel Estimation Methods for RIS-Aided Communication Systems
## 3 Channel Estimation in the RIS-Assisted Communication Systems  
In this section, we will present some optimized algorithms proposed for channel estimation in the MISO and MIMO model systems for RIS-assisted systems. First, the utility of S-CSI and I-CSI is explained.