esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Problem Formulation
### Problem Formulation  
Define the misalignment channel vector as \(\mathbf{v}(n)\triangleq\mathbf{w}(n)-\mathbf{w}\) and \(\mathbf{C}(n)\triangleq E\{\mathbf{v}(n)\mathbf{v}^{T}(n)\}\) as the second moment matrix of \(\mathbf{v}(n)\), Equation (4) can be rewritten as \(e(n)=z(n)-\mathbf{v}^{T}(n)\mathbf{x}(n)\). To verify the performance, the convergence analysis of SLMS-RL1 algorithm is derived via mean convergence and excess MSE. Based on independent assumptions, in [23], the authors derive that SLMS-RL1 is stable if  
\[0<\mu<\sigma_{n}\sqrt{\pi(1+(T-1)\phi)/2}/\lambda_{\max} \tag{9}\]  
where \(\lambda_{\max}\) denotes the maximal eigenvalue of \(\mathbf{R}=E\{\mathbf{x}(n)\mathbf{x}^{T}(n)\). Then the mean estimation error \(E\{\mathbf{w}(n+1)\}\) is derived as  
\[\mathbf{w}(\infty)=\mathbf{w}-\sqrt{\pi/2}\mu^{-1}\rho\mathbf{R}^{-1}\sigma_{ e}^{-1}\mathrm{sgn}\big{(}\mathbf{w}(\infty)\big{)}/(\varepsilon+\mathbf{w}( \infty)) \tag{10}\]  
where \(\sigma_{e}(n)\approx\sigma_{n}\sqrt{1+(T-1)\phi}\) and \(\mathbf{w}(\infty)=\lim\limits_{n\rightarrow\infty}\mathbf{w}(n)\). Similarly, excess mean square error (MSE) is approximated as  
\[\sqrt{\pi/8}\big{(}\mu\lambda_{\max}+\mu^{-1}(\alpha-\beta)\big{)}\sigma_{e} \tag{11}\]  
where \(\alpha\)and \(\beta\) are defined as  
\[\alpha\triangleq\rho^{2}N/\varepsilon^{2} \tag{12}\]  
and  
\[\beta\triangleq 2\rho\big{(}1-\sqrt{2/\pi}\mu\sigma_{e}^{-1}\lambda_{\max} \big{)}E\{\|\mathbf{w}(\infty)/(\varepsilon+\mathbf{w}(\infty))\|_{1}-\| \mathbf{w}/(\varepsilon+\mathbf{w}(\infty))\|_{1}\} \tag{13}\]  
respectively. Here, \(\|\cdot\|_{1}\) denotes \(\ell_{1}\)-norm constraint. Both mean estimation error and excess MSE imply that the reweighted factor \(\varepsilon\) adjusts performance of SLMS-RL1 algorithm. Hence, it is necessary to develop effective method to choose agreeable reweighted factor for further reinforce the proposed SLMS-RL1 algorithm.
## 2 Methods and Materials
### Prediction of Linear and Conformational B-Cell Epitopes  
The ABCpred webserver ([[http://www.imtech.res.in/raghava/abcpred](http://www.imtech.res.in/raghava/abcpred)]([http://www.imtech.res.in/raghava/abcpred](http://www.imtech.res.in/raghava/abcpred))) was used to provide a linear B-cell epitope prediction using a recurrent neural network algorithm trained with BeiPep and SwissProt databases ([[https://www.ebi.ac.uk/](https://www.ebi.ac.uk/)]([https://www.ebi.ac.uk/](https://www.ebi.ac.uk/))) that contained 700 experimentally detected B-cell epitopes. The best results were acquired with the aid of a window length of 16 residues [18] with a threshold for predictions of 0.80 (i.e., without an epitope overlapping filter). BCpreds ([[http://ailab.cs.iastate.edu/bcpreds/predict.html](http://ailab.cs.iastate.edu/bcpreds/predict.html)]([http://ailab.cs.iastate.edu/bcpreds/predict.html](http://ailab.cs.iastate.edu/bcpreds/predict.html))) was employed to predict overlapping epitopes with 80% specificity for flexible length, linear B-cell epitopes [19]. BepiPred ([[http://www.cbs.dtu.dk/services/BepiPred](http://www.cbs.dtu.dk/services/BepiPred)]([http://www.cbs.dtu.dk/services/BepiPred](http://www.cbs.dtu.dk/services/BepiPred))) was utilized to additionally predict B-cell epitopes from antigen sequences (threshold parameter as 0.8 (80%)), while ElliPro ([[http://tools.immuneepitope.org/tools/ElliPro/iedb](http://tools.immuneepitope.org/tools/ElliPro/iedb)]([http://tools.immuneepitope.org/tools/ElliPro/iedb](http://tools.immuneepitope.org/tools/ElliPro/iedb))) predicted both linear and conformational B-cell epitopes. Compared to six other structure-based methods that can be used for epitope prediction, ElliPro was reported to perform the best [20].  
We also explored DiscoTope ([[http://www.cbs.dtu.dk/services/DiscoTope/](http://www.cbs.dtu.dk/services/DiscoTope/)]([http://www.cbs.dtu.dk/services/DiscoTope/](http://www.cbs.dtu.dk/services/DiscoTope/))) as a novel tool to predict conformational B-cell epitopes from three-dimensional structural data of the protein with the aid of spatial information, amino acid statistics, and surface accessibility. The predictions were used for the compilation of discontinuous epitopes as ascertained by X-ray crystallography of antibody/antigen protein complexes [21]. In addition, we predicted conformational epitopes based on the Ellipro server. To predict conformational B cell epitope in ElliPro, its prediction parameter was selected in the range of 0.5 to 8. Peptides that were predicted to be epitopes were selected by their high epitope density.
## 4 Results and Discussion  
### Droplet Manipulation  
We fabricated a demonstration device with six electrodes, each with a 2 mm\({}^{2}\) jagged tile as the terminal shape; the interval between the tiles was 300 \(\upmu\)m. As shown in Figure 5, the dimple structure was 47 \(\upmu\)m in depth and the shape was a gently sloping spherical cap. The 2 mm\({}^{2}\) tiles corresponded to a 10-20 \(\upmu\)L droplet manipulation. In fact, the lower limit of the operable droplet size was between 2 \(\upmu\)L and 5 \(\upmu\)L and the upper limit was between 20 \(\upmu\)L and 25 \(\upmu\)L, regardless of the presence or absence of dimples.  
Figure 6 shows the paths of the median point of the droplets, traced at every second. On the device with a dimple structure, the droplet was stabilized on the dimple structure and moved accurately to the desired position, as shown in Figure 6A. All droplets moved correctly between tiles as commanded. This showed that the dimple structures enabled highly accurate manipulation of the  
Figure 4: Photo and schematic of experimental setup for the sliding experiment. The EWOD device with and without the dimple structure was placed on the tilt stage. A cross-sectional view of the droplet motion was captured by a camera to measure the sliding angle \(\alpha\) (\(\alpha^{\prime}\)). The tilted angle of the stage was gradually increased from 0 to 90 degrees.  
Figure 5: Three-dimensional image of the dimple structure. The dimple structure was 47 \(\upmu\)m deep, with the shape of a spherical cap. The boundary between the dimple structure and the flat surface was gently sloping.  
droplets, independent of minor conditional differences (Video S1). Additionally, even if the wiring paths and electrode tiles were fabricated on the same layer (with a lower cost), the droplets could be stabilized at the desired positions without being affected by the wiring path.  
On the other hand, when using the device without the dimple structure, minor differences in the environmental conditions appeared to greatly affect the motion because of the weak electrostatic force of the EWOD, since the droplets often moved in an unintended direction, as shown in Figure 6B (Video S2). The percentage of droplets correctly moving between tiles as commanded was 70.8% for 10 \(\upmu\)L droplets, and 16.7% for 20 \(\upmu\)L droplets. In particular, in the case where the tile and the electrode path were designed on the same layer, the droplets often drifted along the path towards the voltage source without staying on the tile. Therefore, to avoid this drift phenomenon, according to conventional theory, the wiring path connecting the tile and the voltage source would need to be raised apart from the surface. However, a three-dimensional structure complicates the fabrication process, and the time and cost become enormous.
# DeepST-Net: Leveraging Spatiotemporal Data Refinement for Enhanced Travel Time Prediction
## 6 Results and Discussions
### Evaluation Against Established Models
Additionally, accurate travel time predictions are critical for emergency services, where reduced errors can directly impact response times, potentially saving lives and minimizing property damage. Beyond these applications, the improved accuracy of travel time predictions contributes to the efficiency of urban transportation systems and supports environmental sustainability by lowering fuel consumption and emissions associated with traffic congestion and idling vehicles.
## 5 Discussion
### Preliminary Results and Mesh Sensitivity Analysis
* Velocity field: Both methodologies predict higher velocity fields than experimental data, but these estimates are consistent with prior research. * Computational efficiency: Given similar computational costs, twoPhaseEulerFoam lower sensitivity to mesh quality allows for coarser meshes, reducing computational expenses without compromising accuracy. The evaluation of the boundary layer influence in the dynamic pressures at the plunge pool led to more extended simulations without a significant increment in the quality of the results. The mesh with 16 cells on the boundary layer required 2.2 times longer simulation time than the D24 mesh without a boundary layer. The obtained pressure values do not follow a trend as the boundarylayer has more layers. The mean dynamic pressure reduction observed in the meshes with 4 and 16 cells in the boundary layer can directly relate to the capacity to simulate the velocity patterns near the bottom. Given the minimal differences in pressure field predictions and the implementation of wall functions at the domain's bottom and side walls, it was concluded that boundary layers were unnecessary for this study. [PERSON] [11] performed pressure fluctuation measurements at the insure section of a jet with the same diameter and obtained a value below 8% for jets with initial velocities lower than 12 m/s. Older studies of [PERSON] and [PERSON] [45] obtained values of turbulence intensity of 2% for turbulent jets with velocities lower than 5 m/s. [PERSON] and [PERSON] [6] performed measurements with a total-head Piot tube, a value between 5.5 and 5.8% of turbulence intensity for jets with velocities from 4.9 m/s and 6.6 m/s. Using a laser Doppler velocimeter, [PERSON] and [PERSON] [4] obtained a value of 5% for rough turbulent plunging jets with velocities in the 3.3 to 29.0 m/s range. The initial turbulence intensity sensitivity analysis performed in the present study led to a value of 7% at the jet initial section, which better fit the experimental data. Looking for the values in the literature, it is possible to verify that flow characteristics depend on the type of experimental facility where it is measured. However, the numerical obtained value is in the range of the values measured in experimental facilities by other authors.
# Effects of Resonant Electromagnetic Fields on Biofilm Formation in _Pseudomonas aeruginosa_
## 3 Results
### The Effect of the Ambient Electromagnetic Field on Bacterial Biofilm Formation Is Not Constant  
To test the effect of the ambient electromagnetic field on bacterial biofilm formation, PAO1 cells grown under ambient or shielded (hypomagnetic) conditions were investigated. Under shielded conditions, not even the influence of the static geomagnetic field (typically 30 to 50 microTesla) should be sensed by the bacteria. If ambient and shielded conditions are different but constant, we should expect to see a constant ratio of biofilm formation between the two conditions.  
Figure 3 shows the relative biofilm formation from several series of experiments performed over a period of several months. The calculated relative biofilm formation at ambient conditions performed at different dates, ranging from 102% (series A) to 133% (series P) of control (\"Zero\").  
Figure 2: Effect on planktonic bacterial growth (measured as optical density at 600 nm/OD600) for three different growth conditions, “Ambient/room”, “Zero/shielded”, or “Exposed/3.9 Hz”. Y-axis show normalized OD600 values relative to the “Zero/shielded” condition (value 100) for each series. Average and error bars are for \(n=2\) (Ambient), \(n=5\) (Zero), or \(n=5\) (Exposed) experimental series, respectively.  
In conclusion, the relative biofilm formation of PAO1 cells grown under ambient conditions did not appear constant relative to the shielded condition. Based on this finding, subsequent experiments used the cells grown under shielded (Zero) condition as the control.
# Forward-Looking Element Recognition Based on the LSTM-CRF Model with the Integrity Algorithm
## 4 Experiment
### Evaluation Indicators  
The recognition effectiveness can be evaluated based on the precision (P), recall (R), and F-score (F). To better assess the experimental results, accuracy and coverage evaluations were conducted.  
Accuracy evaluations: The recognition results are compared to manual markings and are required to be fully compliant.  
Coverage evaluation: This requires that the recognition result partially overlaps with the corpus of the tag. However, due to the different lengths of the constituent elements of the entities, attributes, and attribute values in this paper, the coverage evaluation cannot be unified.  
In most cases, entities and attributes are formed by combining two nouns. The standard for coverage evaluation of entities and attributes is a partial overlap between the recognition results and manual markers. Denoted as \" (Profit rate growth), the recognition results of \" (Profit rate) and \" (growth) are considered to match.  
The composition of the attribute value is longer than that of other elements, and partial matching can lead to unclear results. Therefore, the coverage evaluation of attribute values requires the recognition results to contain the manual markers, which is considered a correct match. For example, for the label \" (increased by about 2.5 percentage points year-on-year), \" (will increase by about 2.5 percentage points year-on-year) is the correct match.
## 2 Materials and Methods
### Transformer Neural Networks
#### 2.4.1 BERT Models  
Transformer neural network architectures emerged recently with top results in many tasks in natural language processing, such as natural language inference, question answering, sentiment analysis, and several others. BERT models [16] are perhaps the most well-known examples of transformer neural networks.  
A key factor in the success of these types of models is pre-training, which allows them to gain knowledge about the natural language. This is achieved by training on relatively straightforward tasks with a huge amount of data: BERT models are pre-trained on masked language modeling (predicting a missing word in a sentence) and next sentence prediction (predicting if one sentence follows another). By pre-training over a large corpus, as, for example, Wikipedia and thousands of books, the BERT model comes to any new task with a substantial understanding of language. Adaptation to the new task is called fine-tuning.  
In our experiments, pre-trained BERT models were fine-tuned on the deception detection dataset. The final layer of the BERT-based neural network was a sigmoid activation, as our problem of distinguishing true and false texts is binary. We evaluated the classification models in a 10-fold cross-validation. In each of the folds, the training set was further randomly sub-divided into actual training (90% of data) and validation (10%) sets. The role of the validation subset was to select models: after every training epoch, each model was evaluated on this validation subset, and the best overall model was selected to avoid possible overfitting. Models were trained for 10 epochs using the Trainer API from the Transformers [17] library. We used the following parameters: 500 warm-up steps, weight decay of 0.01, the Adam optimization algorithm with weight decay (AdamW, Transformers library default setting). For each BERT variant, we used the BertForSequenceClassification implementation: Bert Model transformer with a text classification head on top (a linear layer on top of the pooled output).  
We used three pre-trained models to fine-tune on the deception detection data:  
* PolBERT (Polish only) [18]. The model was trained on Polish subsets of Open Subtitles and ParaCrawl, Polish Parliamentary Corpus, and Polish Wikipedia as of February 2020. The batch size was set to 4.
* HerBERT (Polish only) [19]. The model was trained on six different corpora available for Polish: CCNet Middle and Head, The National Corpus of Polish, Open Subtitles, Polish Wikipedia, and Wolne Lektury (a collection of books). The batch size was set to 8.
* BERT-base by Google (multilingual) [16]. The model was trained on texts (BooksCorpus and Wikipedia) in 102 languages, including Polish. The batch size was set to 8.
# Design of Low-Power ECG Sampling and Compression Circuit
## 4 Results and Discussion
### Circuit Simulation  
The Monte-Carlo simulation is performed 1000 times for the offset voltage of the calibrated comparator of the SAR ADC. The results indicate that the offset voltage \(\sigma\) is 0.29 mV, and the average value of the offset voltage is \(-0.02\) mV, as shown in Figure 8.  
Figure 7: The PRD of reconstruction signal of ten Records for two different calculation methods.  
The PVT simulation is completed to evaluate the dynamic performance of the 12-bit SAR ADC at the sampling frequency of 10 kHz, and the simulation results are plotted in Figure 9. Under the condition of TT_12V_25 \({}^{\circ}\)C, the ENOB is 11.1 bits, SNR is 68.7 dB, and SFDR is 71.1 dB. The performance of ADC at TT_12V_25 \({}^{\circ}\)C is better than that of other processes, and at SS_1.08V_80 \({}^{\circ}\)C is the worst.  
To evaluate the robustness of the SAR ADC against device mismatch, the Monte-Carlo simulation of the circuit is performed 100 times. The simulation results in Figure 10 show an mean of 9.85 bits with a standard deviation of 0.97 bits range.  
Figure 8: The Monte-Carlo simulation of the calibrated comparator.  
Figure 9: The PVT simulation of the SAR ADC.  
Figure 11 presents the functional simulation of the sampling and compression circuit, where adc_to_cs is the 12-bit parallel ECG signal after ADC conversion, and cs_to_receiver is the compressed serial data of the high 8-bit ECG data. The system clock sys_clk is 100 kHz, and the sampling clock adc_sample_clk is 10 kHz. The CF is set to 8, so the dimension of the measurement matrix is 50 \(\times\) 800, and 50 elements, namely prbs in Figure 11, are generated in each sampling clock cycle. The compressed data are transmitted according to the SPI protocol, and outputs include the enable signal ss_to_receiver, clock signal clk_to_receiver, and serial data cs_to_receiver. For every 8 bit of data transmission, each compression round requires 100 transfers.
## 3 Structured Sparse Bayesian Approach for Channel Estimation  
In this section, according to the sparse characteristics of the channel in the DD domain, we use Equation (8) to introduce the improved sparse Bayesian algorithm in detail. First, we briefly introduce the design of the pilot pattern at the receiver and transmitter. Second, we consider the sparse characteristics of the channel and design an improved sparse Bayesian algorithm. Finally, the complexity of the proposed algorithm is discussed.
# From Scores to Predictions in Multi-Label Classification: Neural Thresholding Strategies
## 2 Problem Statement
### Two Phases of Multi-Label Classifiers  
Let us denote a vector of real-valued scores for \(i\)-th object to classify as \(\mathbf{s}_{i}\). Because there is one score per label, the number of elements in this vector is the same as in \(\mathbf{y}_{i}\) and is equal to \(L\). The transition from the \(\mathbf{x}_{i}\) input to \(\mathbf{s}_{i}\), can be referred to as the _scoring_ phase of a classification system.  
As mentioned in Section 1, most state-of-the-art extreme classifiers (see, e.g., [2; 30; 31; 27; 2; 32; 33; 2]) in the evaluation are treated as _rankers_, and scoring is their only phase. For evaluation purposes, typically, only the top \(k\) of the returned scores is used.  
However, in classic classification systems, to fulfill the definition of a multi-label classification, classifiers return subsets of labels based on score vectors. Alternatively, the label subset can be expressed by a binary predicted label vector--\(\hat{\mathbf{y}}_{i}\). This transition is called the _thresholding_ phase.  
Let us add two more symbols to our notation:  
* \(s:\mathbb{R}^{D}\mapsto\mathbb{R}^{L}\): scoring phase
* \(t:\mathbb{R}^{L}\mapsto\{0,1\}^{L}\): thresholding phase  
Then, the MLC classifier mapping can be decomposed into two cascaded phases:  
\[\hat{\mathbf{y}}=f(\mathbf{x})=t(s(\mathbf{x})). \tag{1}\]
# Responses of Seasonal Indicators to Extreme Droughs in Southwest China
###### Abstract  
Significant impact of extreme droughs on human society and ecosystem has occurred in many places of the world, for example, Southwest China (SWC). Considerable research concentrated on analyzing causes and effects of droughs in SWC, but few studies have examined seasonal indicators, such as variations of surface water and vegetation phenology. With the ongoing satellite missions, more and more earth observation data become available to environmental studies. Exploring the responses of seasonal indicators from satellite data to drought is helpful for the future drought forecast and management. This study analyzed the seasonal responses of surface water and vegetation phenology to drought in SWC using the multi-source data including Seasonal Water Area (SWA), Permanent Water Area (PWA), Start of Season (SOS), End of Season (EOS), Length of Season (LOS), precipitation, temperature, solar radiation, evapotranspiration, the Palmer Drought Severity Index (PDSI), the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), Gross Primary Productivity (GPP) and data from water conservancy construction. The results showed that SWA and LOS effectively revealed the development and recovery of droughs. There were two obvious drought periods from 2000 to 2017. In the first period (from August 2003 to June 2007), SWA decreased by 11.81% and LOS shortened by 5 days. They reduced by 21.04% and 9 days respectively in the second period (from September 2009 to June 2014), which indicated that there are more severe droughs in the second period. The SOS during two drought periods delayed by 3-6 days in spring, while the EOS advanced 1-3 days in autumn. All of PDSI, SWA and LOS could reflect the period of droughs in SWC, but the LOS and PDSI were very sensitive to the meteorological events, such as precipitation and temperature, while the SWA performed a more stable reaction to drought and could be a good indicator for the drought periodicity. This made it possible for using SWA in drought forecast because of the strong correlation between SWA and drought. Our results improved the understanding of seasonal responses to extreme droughs in SWC, which will be helpful to the drought monitoring and mitigation for different seasons in this ecologically fragile region.  
**Keywords:** extreme drought; periodicity; multi-source data; vegetation phenology; surface water; Southwest China; Google Earth Engine