esa-sceva/satcom-chunk-collection huggingface dataset:
# Backstepping Based Formation Control of Quadrotors with the State Transformation Technique
## 2 Dynamic Model of a Quadrotor
 \tag{6}\]  
Here, \(\Omega_{i}(i=1,2,3,4)\) is the propeller speed of the \(i\)-th rotor, \(l\) is the distance between the center of a quadrotor and the center of a propeller and \(b\) and \(d\) are the thrust and drag factors of a quadrotor. The dynamics of a quadrotor represented by (5) can be expressed in state-space form (7) using the following states:  
\[ \begin{split}(\chi_{1},\chi_{2},\chi_{3},\chi_{4},\chi_{5},\chi_{ 6},\chi_{7},\chi_{8},\chi_{9},\chi_{10},\chi_{11},\chi_{12})=(X,\dot{X},Y, \dot{Y},Z,\dot{Z},\phi,\dot{\phi},\theta,\dot{\theta},\psi,\dot{\psi})\\ \dot{\chi}_{1}=&\chi_{2}\\ \dot{\chi}_{2}=&\big{(}cos(\chi_{7})sin(\chi_{9}) cos(\chi_{11})+sin(\chi_{7})sin(\chi_{11})\big{)}\frac{u_{1}}{m}\\ \dot{\chi}_{3}=&\chi_{4}\\ \dot{\chi}_{4}=&\big{(}cos(\chi_{7})sin(\chi_{9}) sin(\chi_{11})-sin(\chi_{7})cos(\chi_{11})\big{)}\frac{u_{1}}{m}\\ \dot{\chi}_{5}=&\chi_{6}\\ \dot{\chi}_{6}=&\big{(}cos(\chi_{7})cos(\chi_{9}) \big{)}\frac{u_{1}}{m}-g\\ \dot{\chi}_{7}=&\chi_{8}\\ \dot{\chi}_{8}=&\dot{\chi}_{9}\dot{\chi}_{11}\frac{(I _{yy}-I_{zz})}{I_{xx}}-\frac{J}{I_{xx}}\dot{\chi}_{9}\Omega_{r}+\frac{u_{2}}{I_ {xx}}\\ \dot{\chi}_{9}=&\chi_{10}\\ \dot{\chi}_{10}=&\dot{\chi}_{7}\dot{\chi}_{11}\frac{(I _{zz}-I_{xx})}{I_{yy}}+\frac{J}{I_{yy}}\dot{\chi}_{7}\Omega_{r}+\frac{u_{3}}{I _{yy}}\\ \dot{\chi}_{11}=&\dot{\chi}_{12}\\ \dot{\chi}_{12}=&\dot{\chi}_{7}\dot{\chi}_{9}\frac{(I _{xx}-I_{yy})}{I_{zz}}+\frac{u_{4}}{I_{zz}}\end{split}  \tag{7}\]  
In this paper, to design the leader-follower formation controller, we use the following assumption:  
**Assumption 1**. : _In the quadrotor formation control, the followers can estimate perfectly the states of the leader such as position, velocity, and so on._
## 3 Results
### Evaluation of the Leading Cause of Odor Offensiveness Reduction with UV-A Photocatalysis  
We observed a significant change in the perceived overall odor 'character' (i.e., 'what it smells like') for UV-A-treated swine barn emissions. The research team working at the swine farm test site described the smell of UV-A treated air as a mix of a less-offensive 'disinfectant' or'swimming pool' scents with a weaker smell of swine manure in the background. Therefore, we investigated which compounds (generated by UV-A treatment) were responsible for adding the less-offensive scents. It should be mentioned that neither the odor measurement (by dilution olfactory, Section 2.4) or the mitigation of targeted VOCs (Section 2.5) could answer the key question of why the smell is subjectively less offensive. In general, the 'disinfectant' smell similar to'swimming pool' would likely be preferred compared to raw swine manure, even if the odor concentration is the same.  
The initial assessment of the simultaneous chemical and sensory analyses (Figure 3) was consistent with the overall percent reduction of odor and odorous VOCs (Tables 1, 2 and 11). The overlaid chromatograms (black lines) and aromagrams (red lines) illustrate the difference in the GC-separated peak number, height and areas between the control and UV-treated air for panelist 1 (results for panelist 2 and 3 are presented in Figures S1 and S2. The lower number of aromagram peaks and smaller peak heights are consistent with the weaker (less intense) smell of manure in the UV-A treated air.  
The one compound (benzoic acid) generated in the UV-A photocatalysis is known to have the characteristic smell of 'faint, pleasant odor', which appears to be consistent with the panelist's perception. Two panelists indicated that benzoic acid (eluting from GC column at ~15.6 min had a 'toothgate, mouthwash and pleasant' smell and'mint, neutral' smell, respectively (Table 14).  
Figure 3: An overlay of the chromatogram (black line) and aromagram (red line). The height of aromagram peaks represents measured odor intensity (percent relative scale). The TIC signal is collected simultaneously, enables linking odors to specific chemicals in the mixture. Several unpleasant (out of 31 total) odors with medium-to-strong intensity were recorded during analysis with GC-MS-O. The ‘toothgate’, ‘mouthwash’, ‘mint’ scents are emerging in UV-A treated air.  
Benzo acid is the oxidation product of common compounds with the C6H5-C in the structure, such as a toluene or other (mono) alkylbenzenes. Previous studies report on toluene present in the headspace of slurry pit manure [27, 28]. The generation of VOCs with the UV treatment and the apparent reduction of the odor offensives associated with treated air deserves a follow-on experiment to make a comprehensive evaluation focused on the photochemical reactions, generation of other odorants, and the effect on odor.
## 5 Conclusions  
The swelling characteristics of MB specimens after different swelling times at different initial dry densities and under different loads were studied. The hydraulic conductivity testing of MB was carried out in both fresh and saline water conditions. The following conclusions are drawn:  
1. The improvement efficiency of MB is remarkable, the MB material exhibits good swelling characteristic and a low permeability in saline water, and the hydraulic conductivity of MB is less than \(3\times 10^{-12}\) cm/s.
2. The unloading swelling strain on MB in saline water increases over time and there is a linear relationship between the ratio of time to swelling strain and swelling time; the final unloading swelling strain of MB increases linearly with increasing initial dry density.
3. The loading swelling strain of MB decreases with the increase of vertical pressure and increases with the initial dry density; a formula for the loading swelling strain that was related to initial dry density and vertical pressure was obtained.
4. With increasing swelling time, the swelling pressure increases rapidly at first and then decreases, the ratio of time to swelling pressure has a linear relationship with swelling time, and the relationship between swelling pressure and initial dry density of MB specimens is exponential and increasing.
5. Relationship affecting the swelling capacity of bentonite was deduced, and all such equations can provide a theoretical basis for the use of modified bentonite as an impermeable material in civil engineering practice.
6. The empirical relationships obtained above are only applicable to the specific conditions of this study. Swelling capacity can be influenced by other factors, for example, mixing of polymer and bentonite, and chemical compositions of the permeant. In future studies, these factors will be explored to facilitate the characterization of universal empirical relationships.  
[PERSON] conceived the project theme and central ideas and designed the experiments. [PERSON] and [PERSON] performed the experiments and wrote the paper. [PERSON] checked the accuracy of experimental results and [PERSON] supervised the whole work.  
This research received no external funding.  
This research was supported by a grant (17 CTAP-C132706-01) from the Infrastructure and transportation technology promotion research Program funded by the Ministry of Land, Infrastructure and Transport of Korean government. The research was also part of the project entitled \"Development on technology for offshore waste final disposal\", funded by the Ministry of Oceans and Fisheries, Korea.  
The authors declare no conflict of interest.
## 4 Experimental Analysis
### Experimental Setup  
For a fair comparison with previous works [11; 13], our model is evaluated to initialize the encoder's network parameters using the pre-training model Swin-B [27] and allow the model parameters to be updated and optimized during the training process. In the WGA swin transformer, block \(G\) is defined as 49 in WGA. The encoder contains 12 swin transformer blocks, where \(N_{1}=6\) and \(N_{2}=3\). Additionally, in the decoder, the number of window tokens \(n\) is set to 64, and the decoder is built with Swin 3D LR Blocks and Swin 3D HR Blocks, which progressively up-samples voxel features from a low-resolution grid \(4^{3}\) to a high-resolution grid \(32^{3}\) with skip connections used to maintain fine-grained spatial details during reconstruction. To validate the reconstruction performance, we provide two models, namely, WGA-SWIN and WGA-SWIN+, which share the same network architecture but are different in the number of views [11] used for training (three views for WGA-SWIN and eight views for WGA-SWIN+). The experimental code is implemented in PyTorch 2.4.1 and trained on two NVIDIA RTX 2080 Ti GPUs with 24 GB memory. All input images are resized to \(224\times 224\), and 3D voxel grids are normalized before processing. Following the previous works [13], for our WGA-SWIN and WGA-SWIN+, the training process uses the AdamW [34] optimizer, with \(\beta_{1}=0.9\) and \(\beta_{2}=0.999\). The threshold \(t\) for voxel binarization is set to 0.5 for WGA-SWIN and 0.4 for WGA-SWIN+. The training batch size is 16, and the model is trained for a total of 150 epochs. The primary learning rate is set to 0.0001 and decreased by a ratio of 0.1 after the 70 th and 100 th epochs. The training batch size is 16, and the model is trained for a total of 150 epochs. To evaluate reconstruction performance, we use Dice Loss as the loss function to handle imbalanced voxel occupancy. During inference, both WGA-SWIN and WGA-SWIN+ can adapt to an arbitrary number of input views, with performance evaluated using IoU and F-Score@1% metrics.
# Comparing Speaker Adaptation Methods for Visual Speech Recognition for Continuous Spanish +
## 6 Experimental Setup
### Implementation Details  
All of our VSR systems and the training and inference processes were implemented using the open-source ESPNet toolkit [61]. Experiments were conducted on a GeForce RTX 2080 GPU with 8 GB memory.  
**Data Pre-processing.** Similar to [22], using the state-of-the-art RetinaFace face detector [62] and Face Alignment Network [63], grayscale bounding boxes centered on the speaker's mouth were cropped to form images of 96 \(\times\) 96 pixels. These Regions Of Interest (ROIs) covered the mouth as well as the complete jaw and cheeks of the speaker, a wider area that has shown benefits when addressing the VSR task [64].  
**Data Augmentation.** This data augmentation process was influenced by [22]. First, after the ROIs were normalised with respect to the overall mean and variance of the training set, a random cropping of 88 \(\times\) 88 pixels was applied. Then, additional techniques were considered, such as horizontal flipping and time masking [22], a method inspired by the work carried out in the field of auditory-based ASR [65].  
**Pre-Training.** As mentioned in Section 4, both the LM and all the VSR systems considered in our experiments were pretrained or initialised using the weights of the models publicly released by [PERSON] et al. [22] for the Spanish language. It should be noted that the LM was not re-estimated in any of our experiments.  
**Training Setup.** The pretrained VSR systems were re-estimated during five epochs using the AdamW optimiser [66] and a linear one-cycle scheduler [67]. In all the cases, the learning rate was set to \(5\times 10^{-4}\). This optimum was found after carrying out preliminary experiments, as described in Section 7. Regarding the CTC/Attention loss, the \(\alpha\) weight specified in Equation (1) was set to 0.1. For those experiments where the proposed fine-tuning strategies were applied, the batch size was set to only 1 sample due to our GPU memory constraints. It should be noted here that we explored the accumulating gradient strategy [68]; however, no significant differences were found. We argue that despite applying this technique, the normalisation layers were affected by the actual reduced batch size. When using the light-weight Adapters-based method, the significantly reduced number of learnable parameters allowed us to use a batch size of four samples.  
**Inference Setup.** As Equation (2) reflects, different weights were used to model the influence of each component during the inference process. In all cases, the \(\beta\) weight was set to 0.4. The word insertion penalty was set to 0.0, while due to memory limitations the beam size was set to 10. Regarding the CTC/Attention balance, the \(\lambda\) weight was set to 0.1. As described in Section 4.4, all of these hyperparameters were set according to [22].  
**Evaluation Metric.** All the results reported in our experiments were evaluated using the well-known Word Error Rate (WER) with 95% confidence intervals obtained by the bootstrap method, as described in [69].
## 5 Future Work
### Enhanced Collaboration with Mobile Network Operators  
To advance the development of rainfall estimation techniques using CMLs, in the first place, we need to strengthen our collaboration with mobile network operators who are the maintainers and data providers of CMLs. Firstly, although the current sampling strategy for most CML data is not that satisfactory, it can be indeed improved. One solution is to install data loggers on cell towers, which can sample RSLs with free temporal resolution and accuracy [28]. However, the high expense of deploying and maintaining data loggers for a large number of CMLs undermines their low-cost advantage. In fact, some research groups [14; 54] have already realized data acquisition with high temporal resolution via simple network management protocol (SNMP), which is a standard network management protocol widely used in TCP/IP networks. It is capable of supporting NMS for monitoring CMLs connected to the cellular network. Depending on the customized SNMP data acquisition software, RSL and TSL can be polled at a set sampling step as low as a few seconds. Despite the fact that the data quantization is still limited by the CML hardware, such CML data with high sampling frequencies are favorable for investigating variation patterns of signal attenuation. And, thanks to [PERSON] et al. [174], the related software has been open-sourced at GitHub. However, SNMP must be used only under the license of the mobile network operators as it requires access to their intranet. Therefore, trust between the research groups and the CML operators is a must.  
Secondly, for commercial confidentiality reasons, mobile network operators usually permit the use of CML data only to the relevant research group; thus, CML data are generally not shared among different research groups. However, open access to high-quality CML data may be able to bring more insights into the issues. Researchers need to convince CML operators and guarantee that the data will be used without jeopardizing their interests.  
Thirdly, the CML data now used for rainfall inversion are mainly signal strength information. In fact, many CMLs are configured with multiple sub-links due to communication needs, which may lead to additional opportunities for identifying hydrometeors [118], improving the accuracy of rainfall inversion [175] and humidity estimation [100] if phase information of microwave signal is available.  
Finally, it is unrealistic for mobile network operators to provide CML data at no cost or for only researchers to pay for it. In fact, as operators provide CML data, hydrometeological researchers can utilize expertise to assist operators in the design and real-time decision making of CML networks, as described in [176]. In addition, considering the practical value of CML rainfall measurement technology, researchers can help CML operators to operationalize and productize it for sale to fields such as agriculture, water resource management and meteorological research.
## 5 Limitations and Future Work  
This article presents an experimental analysis of an unbalanced Bengali dataset to detect fake news on social media sites using BERT. The proposed AugFake-BERT model can monitor numerous posts, comments, and messages in multiple social media networks to detect fake news. This model can be used by anyone willing to incorporate artificial intelligence into their systems to detect fake news. The proposed AugFake-BERT model had the highest accuracy in the standard Bengali language. As Bengalis speak differently depending on their location, this method may provide fairly low accuracy in such situations. In the future, multimodal learning might offer an extended version of the proposed work. Multimodal learning perceives images combined with textual content for the classification of fake news.
## 2 Materials and Methods
### Sequencing on GridION and Adaptive Sampling  
Before initiating the sequencing run, a flow cell check was performed using the MinKNOW software (ONT, Oxford, UK) to assess the number of available nanopores; R9.4.1 flow cells with fewer than approximately 800 active pores were typically not used, following recommendations. After confirming the flow cell quality, priming, and loading, the sequencing run was started via the MinKNOW interface. Adaptive sampling (ReadUntil) was enabled by selecting the corresponding option in the software setup for the AS Library. A BED file containing the genomic coordinates of the reference mitochondrial genome sequence, and another 121 ADME genes was uploaded to direct the instrument to selectively sequence reads originating from this target region. For the WGS baseline library (prepared from untreated native DNA), sequencing proceeded using a standard WGS run protocol without the adaptive sampling feature enabled. The following softwareversions from Oxford Nanopore were used during the sequencing run: MinKNOW24.02.16, Bream7.9.8, Configuration5.9.18, Dorado 7.3.11, and MinKNOW Core5.9.12.
# Architectural Simulation of Hybrid Energy Harvesting: A Design Experiment in Lanzarote Island
## 4 Design Development
### Architectural Design Based on Energy Harvesting Using Micro-Energy Sources
#### 4.2.1 Wind Direction Analysis of the Research Subject Location, Lanzarote Dynamic Square  
To install piezoelectric materials on the pergola on Lanzarote Dynamic Square, it is important to analyze the wind direction of the site. According to prior studies, piezoelectric materials must be installed in a manner that is perpendicular to the wind direction to maximize energy generation efficiency. Figure 10 is a visualization of the average annual wind direction and wind speed of the study area using Autodesk's Formit [42], wind direction and speed analysis software. The analysis of the average annual wind direction shows that the predominant wind direction at this site is north-northeast (NNE), north (N), and northeast (NE), in that order. According to this analysis of wind direction and wind speed, the piezoelectric materials to be installed on the pergola on Lanzarote Dynamic Squares must run in their longitudinal direction from south to north and cover the entire pergola roof.
## 3 Results
### Structural Equation Modeling for Effluent T-P
#### 3.2.1 Path Model of Effluent T-P
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Classification**} & \multirow{2}{*}{**Goodness-of-Fit Criterion**} & \multicolumn{2}{c}{**Initial Model**} & \multicolumn{2}{c}{**Modified Model**} \\ \cline{3-6}  & & **Result** & **Validation** & **Result** & **Validation** \\ \hline \multirow{2}{*}{Q value} & \multirow{2}{*}{below 3} & 1.263 & 1.659 & 1.902 & 1.949 \\  & & (Fitness) & (Fitness) & (Fitness) & (Fitness) \\ \hline \multirow{2}{*}{GFI} & \multirow{2}{*}{above 0.9} & 0.981 & 0.974 & 0.969 & 0.967 \\  & & (Fitness) & (Fitness) & (Fitness) & (Fitness) \\ \hline \multirow{2}{*}{AGFI} & \multirow{2}{*}{above 0.8} & 0.949 & 0.931 & 0.913 & 0.908 \\  & & (Fitness) & (Fitness) & (Fitness) & (Fitness) \\ \hline \multirow{2}{*}{RMSEA} & \multirow{2}{*}{below 0.05(below 0.1)} & 0.040 & 0.063 & 0.074 & 0.076 \\  & & (Fitness) & (Fitness) & (Fitness) & (Fitness) \\ \hline \multirow{2}{*}{CFI} & \multirow{2}{*}{above 0.9} & 0.995 & 0.981 & 0.987 & 0.982 \\  & & (Fitness) & (Fitness) & (Fitness) & (Fitness) \\ \hline \end{tabular}
\end{table} 
Table 7: Fitness results of the developed path models for effluent T–P concentration. Figure 5: Developed path model for effluent T-N concentration. (**a**) Initial path model; (**b**) modified path model. on the initial model. The significance of the path coefficient results indicated four unreliable paths, \"B_in_BOD\(\rightarrow\)Anaero_PO\({}_{4}\)-P\", \"B_in_BOD\(\rightarrow\)B_Sludge return ratio\", \"B_in_T-P\(\rightarrow\)B_Sludge return ratio\", and \"B_DO\(\rightarrow\)Oxic PO\({}_{4}\)-P\". Except for these, all path coefficients were significant, especially the two paths of \"B_in_T-P\(\rightarrow\)Anaero_PO\({}_{4}\)-P\" and \"Oxic_PO\({}_{4}\)-P\(\rightarrow\)Effluent T-P\", which coincided with the results of the initial model. Fitness was all acceptable with a Q value of 1.949, GFI of 0.967, 0.908 of AGFI, 0.076 of RMSEA, and 0.982 of CFI.
## 4 Experiments
### Evaluation  
Although Figure 9 shows that loss values (box loss, class loss, and object loss) stabilize rather than continuously decrease over the training period, this phenomenon can be attributed to the following factors:  
* Small dataset size: The limited size of the dataset (80 images) results in a lack of diverse training samples, which can constrain the model's ability to generalize. With such a small dataset, the model quickly learns the patterns present in the data, leading to the early stabilization of loss values.
* Class imbalance: The dataset may have inherent imbalances in the distribution of object categories, leading to slower improvement in class loss as the model struggles to correctly classify underrepresented classes.
* High initial learning rate: The use of a relatively high initial learning rate may cause loss values to plateau as the optimizer approaches local minima, rather than continuing to decrease steadily.
* Overfitting: With prolonged training on a small dataset, the model risks overfitting to the training data. This could result in a lack of improvement in loss metrics, as the model focuses on minor variations in the training set rather than generalizing effectively. To address this, future work will incorporate techniques such as the following:
* Data augmentation: Expanding the dataset through synthetic data generation to increase diversity.
* Regularization: Introducing stronger regularization techniques (e.g., dropout) to prevent overfitting.
* Fine-tuning learning rates: Experimenting with a cyclical or adaptive learning rate to improve convergence and optimize the loss metrics.