esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### SWC and Environmental Factors  
The higher mean SWC observed in the top layer of the coal mining area (25.21%) suggests enhanced moisture retention, likely due to the disturbance of soil layers that can trap water [13]. Firstly, mining activities often cause significant disturbances to soil structure, leading to soil compaction, which reduces pore space and impairs the soil's ability to drain water. Specifically, the compacted layers can form barriers that slow down water percolation, allowing water to accumulate in the upper layers. Secondly, mining  
Figure 5: Wavelet coherency between rainfall and correlation coefficients of SWC at 50 cm with (**a**) altitude, (**c**) slope, (**e**) GCVR, (**g**) CGI, (**i**) clay, (**k**) SBD, (**m**) SOM, and (**o**) pH in the coal mining area and (**b**) altitude, (**d**) slope, (**f**) GCVR, (**h**) CGI, (**j**) clay, (**l**) SBD, (**n**) SOM, and (**p**) pH in the non-coal mining area. The X axis indicates the number of days since the measurement started and the Y axis indicates temporal scale (months); the color legend indicates the strength of correlation, the black solid line indicates 95% significant level, and the direction of the arrow indicates the type of correlation.  
can modify the natural soil horizons by mixing them or creating artificial layers. For instance, deeper horizons might be brought to the surface or foreign soil may fill the cracks created by mining. These alterations could create a more favorable environment for water retention, especially in the top layers where the interaction between water and soil is most direct. However, the lower SWC at the second layer in coal mining areas, alongside greater temporal variation, indicates a complex interaction between soil structure and moisture dynamics [14; 15]. The pronounced spatial variations in SWC, particularly in coal mining areas, highlight the impact of mining on soil heterogeneity, which may affect ecosystem functions and agricultural practices in the region [16; 17].  
Interestingly, despite similar slope values and TWI between the two areas, the distinct chemical compositions suggest that mining alters nutrient dynamics. The increased nutrient levels (SAN and SAP) in coal mining areas could be attributed to runoff from disturbed landscapes, which may enhance fertility but also pose risks of nutrient leaching into surrounding ecosystems [18]. Specifically, mining activities often result in significant disruption to the landscape, including the removal of vegetation, soil disturbance, and alteration in natural drainage patterns. These disturbances increase surface runoff, as soil's natural ability to absorb and retain water is reduced. Runoff can carry nutrients, such as nitrogen and phosphorus, from disturbed soils into the surrounding environment, leading to higher concentrations of SAN and SAP in topsoil. In contrast, the higher potassium levels in non-coal mining areas reflect a more balanced nutrient profile, potentially supporting sustainable soil health [19; 20].
## 3 Experimental Setup and Results
### Results from Standard Stacking Experiment  
This experiment is similar to the proposed approach except that we use only the predictions from the Base-Level classifiers as an input to the Meta-Level classifier (in the novel proposed approach we used the Base-Level predictions and the extracted features from the development set). F1-scores of the standard stacking are given in Table 10 on each dataset. The results achieved by this approach are very low on the HatEval dataset with 59.43% as the highest F1-score for the (SVM, LR, E-tree) combination. On the Davidson, COVID-HATE, and ZeerakW datasets, the best results were obtained by the (SVM, LR, XGB), (LR, NB, RF), and (KNN, LR, NB) combinations, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Base-Classifiers Combination** & **HatEval** & **Davidson** & **COVID-HATE** & **ZeerakW** \\ \hline SVM, LR, XGB & 0.6296 & 0.9521 & 0.7050 & **0.7221** \\ KNN, SVM, NB & 0.6170 & 0.9509 & 0.7188 & 0.7005 \\ LR, NB, RF & 0.6135 & 0.9309 & 0.6950 & 0.6280 \\ KNN, LR, NB & 0.6167 & 0.9547 & **0.7208** & 0.6289 \\ SVM, LR, E-tree & **0.6307** & **0.9552** & 0.7081 & 0.7210 \\ \hline \hline \end{tabular}
\end{table}
Table 9: F1-score of the majority voting ensembles using different base classifiers on test sets. Bold entries show the highest performance for each classifier on each dataset.
# Donning/Doffing and Arm Positioning Influence in Upper Limb Adaptive Prostheses Control
## Materials and Methods  
### Data Acquisition  
A Myo Armband from Thalmics was used for the data acquisition process. The sensor is a flexible hardware that can be used in upper limbs with 7.5 to 13 inches forearm circumference. It includes 8 bipolar EMG electrodes sampling at 200 Hz with an 8 bit resolution. With these specifications, simple signal amplitude features are obtainable. A Bluetooth connection is opened to send the data and process it in a MATLAB environment. We used MATLAB 16a 64 version running on a 2.6 GHz personal laptop with 8 GB RAM. The Myo Armband was always placed in the right forearm of the participant, as in Figure 1, in the same location and orientation for all participants (LED light of the sensor pointing to the hand).  
This experimentation would be reproducible in patients with limb deficiencies with a stump of at least 3 inches from the elbow to place the armband. In our previous work [49], the system was used in amputees that satisfied that specification. The armband is placed to measure the superficial EMG of the flexor carpi radialis and ulnaris muscles, extensor carpi radialis longus and the brachioradialis muscle, among other residual EMG measurements. In that paper the experiment was only conducted with forearm EMG activity so there are no evidences for EMG signals obtained from other muscles.  
The feature used as the input vector \(\mathbf{x}(t)\) is the moving root mean square (RMS) of the digitized EMG signals \(\mathbf{\hat{x}}(t)\):  
\[\mathbf{x}(t)=\sqrt{\frac{1}{N}\sum_{k=t_{1}}^{t}\mathbf{\hat{x}}^{2}(k)} \tag{1}\]  
where \(t_{1}=t-N+1\), \(\mathbf{\hat{x}}(k)\in\mathbb{R}^{8\times 1}\) is the eight dimensions digitized muscular activity, and \(N\) is the sample size. This is the discrete version counterpart of the continuous moving RMS value:  
\[\mathbf{x}(\tau)=\sqrt{\frac{1}{T}\int_{\tau-T}^{\tau}\mathbf{\hat{x}}^{2}(t)dt} \tag{2}\]  
where \(N=T\cdot f_{s}\) and \(f_{s}\) is the sampling frequency. In our case, \(f_{s}=200\) Hz, \(T=200\) ms and \(N=40\). A new feature vector is calculated every 40 ms, resulting in a 25 Hz feature rate with 160 ms overlapping between consecutive input vectors.  
The two studies were conducted independently with different participants, so we can guarantee that there was no learning contamination from one experiment to the other; 8 able-bodied participants (5 males and 3 females) executed the donning/doffing experiment and 4 participants (2 males and 2 female) the arm position experiment. Participants were in between 20-50 years old. All individuals provided written informed consent before the experiment. The experiments were performed in accordance with the declaration of Helsinki and were approved by the UPV ethics committee, approval number P11-23-03-18.
## 1 Introduction
On the other hand, fish vocalizations can be monitored through autonomous hydrophone arrays or vector sensors [22]. The vector hydrophone measures the direction of sound vibration and sound intensity, so it presents advantages in the detection of underwater acoustic targets emitting low and ultra-low frequency signals [23] such as fish sounds. These technologies are relatively inexpensive and possess remote monitoring capabilities for long-term data acquisition. Thus, they have become a valuable tool for biological studies, ecological conservation and fish population estimation. The aim of this work is to design and implement a robust and novel automatic system for the identification of fish from its acoustic signals. Therefore, it could be used to map species and detect changes in fauna composition. For this purpose, four types of features have been extracted for each call, which have been fused into a single vector. To the best of our knowledge, this research is the first to fuse frequency (Mel and Linear Frequency Cepstral Coefficients) and temporal (Shannon entropy and call duration) fish acoustic features. The main novelty of this work comes from the fact that these features are combined to achieve a robust representation of the sound. Moreover, the results of three widely used pattern matching techniques have been compared to test the approach: KNN [24], Random Forest (RF) [25] and SVM [26]. This system has been validated on a dataset composed of 102 marine animals from two public sound collections that were previously labeled by experts. The remainder of this paper is organized as follows: Section 2 describes the proposed system and introduces the acoustic data. The classification methods used (SVM, RF and KNN) are described in Section 3, particularized for acoustic recognition. Then, Section 4 contains the experimental methodology. Section 5 provides details and discusses the results obtained. Finally, in Section 6, the conclusions of this work are shown.
## 3 Materials and Methods
### Models and Experimental Set-Up
For comparative benchmarking and cross-domain generalization analysis, we also evaluated two publicly available state-of-the-art hate speech detection models:  
* piuba-bigdata/beto-contextualized-hate-speech, [[https://huggingface.co/piuba-bigdata/beto-contextualized-hate-speech](https://huggingface.co/piuba-bigdata/beto-contextualized-hate-speech)]([https://huggingface.co/piuba-bigdata/beto-contextualized-hate-speech](https://huggingface.co/piuba-bigdata/beto-contextualized-hate-speech)), (accessed on 16 June 2025) [12]: This BETO-based transformer model, originally fine-tuned on Spanish news comment sections, provided insights into model performance when applied to conversational data from different domains. * unitary/multilingual-toxic-xlm-roberta, [[https://huggingface.co/unitary/multilingual-toxic-xlm-roberta](https://huggingface.co/unitary/multilingual-toxic-xlm-roberta)]([https://huggingface.co/unitary/multilingual-toxic-xlm-roberta](https://huggingface.co/unitary/multilingual-toxic-xlm-roberta)), (accessed on 16 June 2025) [59]: Based on XLM-RoBERTa, this model was trained on multilingual Wikipedia talk page comments for toxic vs. non-toxic classification. Its broad multilingual scope enables evaluation of cross-lingual generalization to Spanish without fine-tuning, providing a benchmark for assessing how well generic models handle context-sensitive toxicity in domain-specific forums like Mediavida. Model performance was systematically evaluated using four established classification metrics: precision, recall, F1 score, and ROC-AUC. Precision and recall measure model accuracy and sensitivity, particularly relevant given the dataset's imbalanced classes. The F1 score offered a balanced assessment combining precision and recall. ROC-AUC assessed overall discriminative power independently of classification thresholds, providing robust comparative insights across models. Our comprehensive experimental framework enabled a rigorous comparative analysis of model effectiveness, interpretability, and domain transferability, delivering critical insights into the capabilities and limitations of contemporary Spanish-language hate speech detection systems, particularly within complex and context-dependent online dialogues. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Dataset** & **Training** & **Oversampled Training** & **Development** \\ \hline Racism & 792 & 1096 & 197 \\ Sexism & 800 & 1258 & 199 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Dataset splits: number of trainings, oversampled training, and development examples for racism and sexism detection tasks.
## 5 Conclusions  
In our FSI-based system, the ECL has been used for five years. Thus, the SNR of the ECL is low, and the mode-hopping signals often occur. In addition, the influence of the interfering signals cannot be neglected when the system measures non-cooperative targets. To ensure the precision of our system, it is essential to delete the erroneous sampling points in the auxiliary interferometer and the interfering signals and phase jump in the measurement interferometer. So, the adaptive filtering method, the phase-matching mosaic algorithm, and the segmentation mosaic algorithm are proposed to solve the problems and improve the data processing speed. The results of our simulation prove the efficiency and applicability of our methods. In the experiments, the target was a coin and the laser interferometer (Renishaw XL80) was used to verify the efficiency of our method. The stability of our system was 5.7 \(\upmu\)m over 20 measurements. The maximum deviation of the measured results between the FSI-based system and the He-Ne interferometer was 9.6 \(\upmu\)m, and the maximum mean square error of our method was 2.4 \(\upmu\)m, which approached the Cramer-Rao lower bound (CRLB) 0.8 \(\upmu\)m.  
**Author Contributions:** Conceived the Method and Wrote the Paper, X.-T.X.; Designed the Experiments, X.-H.Q.; Improved the Experimental Results, F.-M.Z.  
**Funding:** Natural National Science Foundation of China (NSFC) (No. 51675380, No. 51775379), National Key Research and Development Plan(2018 TFF0212702).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Discussion  
The proposed method was applied to a case study of usability evaluation, and we confirmed that the method could extract the target system's problems and could propose improvements. By using step coding and 70 design items, the process of extracting usability problems from textual data was explicit, and the problems were extracted analytically. In other words, the proposed method was less ambiguous. By incorporating 70 design items into the interpretation of the problem points, the usability evaluation was performed from various viewpoints. In addition, since the items were exhaustive and designed to be used by novice designers, they could be easily linked to specific problems.  
However, the extraction of keywords to be focused on from the textual data depended on the experience of each analyst. In order to solve this problem, the use of keyword extraction algorithms should be considered. In addition, with the development of deep learning and other technologies, methods to automatically export text from verbal data have been proposed. For example, [PERSON] et al. [18] have worked on the extraction of words and phrases to be focused on from textual data. In the future, it would be possible to improve the efficiency of this method by using it together with automated tools for text export and keyword extraction. In addition, the proposed method should be evaluated by using it in many practical situations. In such practical situations, it is necessary to verify the effectiveness of the proposed method and to improve it by comparing it with conventional methods and by listening to the opinions of practical experts. The author himself analyzed the proposed method in Chapter 3 and found it could be carried out smoothly based on the proposed procedure. However, this study has also mentioned the possibility of using non-experts as one advantage and usage of the proposed method. In this regard, the fact that the procedures are specified does not leave much room for the analyst to think on his or her own, so non-experts can use it. However, since 70 design items are many, it might be difficult for a non-expert to analyze them. The ease of use of the proposed method needs to be examined through actual use by non-experts. In addition, the number of items in the 70 design items could be narrowed down according to the analysis target.  
This research was partly funded by JSPS KAKENHI, grant number 19K15252.  
Not applicable.  
Data sharing is not applicable to this article.  
The author declares no conflict of interest.
## 2 Materials and Methods
### Methods
#### 2.3.4 Machine Learning Model Implementation  
In this study, LSMs were generated by applying XGBoost due to the good results obtained in the previous stage of this research. In this sense, the values obtained for the area under the ROC curve (AUC) with the test data show a greater predictive power of the XGBoost models, both using 15 and 6 factors (0.87 and 0.85), compared to those obtained with RF (0.79 and 0.80) and ANN (0.63 and 0.73). In this research and to analyze the accuracy of the model considering the different combinations of factors and sampling methods, hyperparameter tests were carried out to determine the most accurate models based on the characteristics described above, as well as on this computational aspect. Next, the main characteristics of XGBoost are described and then the hyperparameter configuration elaborated with the corresponding tuning process is detailed.  
Extreme Gradient Boosting (XGBoost)  
This algorithm belongs to Gradient Boosting Models (GBM). The main feature of GBMs is to build a set of shallow trees sequentially. Each tree built learns from the previous one and improves it [32]. Shallow trees are weak predictive models in themselves, however, they are boosted and adjusted appropriately, and it is this feature that provides much power to boosting-based algorithms. One of the main features of boosting is that each sequentially trained model is imparted with additional knowledge, which reduces bias and improves overall model performance [35], while gradient boosting implies that parameter tuning is performed iteratively to reduce the cost of the function [32]. XGBoost is one of the most powerful variants of gradient boosting and is itself a decision tree-based algorithm; it  
Figure 3: Sampling points in the study area: (**a**) Sample 1, (**b**) Sample 2.  
is fast, especially when implemented with parallel computing, efficient [33] and versatile since it is useful for classification and regression tasks. Its popularity has increased in recent years for LSM development, although it is used for several applications due to its superior performance compared to other supervised learning algorithms [24]. One of the main features of XGBoost is regularization. This method is useful in tree building process as it prevents the parameters of a model from becoming too large, it also prevents individual predictors from having a large impact on the overall prediction which helps to avoid overfitting (or reduces it), allows to control the complexity of the model [24; 35] and improves its computational efficiency [29].
## 2 Materials and Methods
### Enrichment of N-glycopeptides from Human Serum with MSP@PNAMAm  
In order to evaluate the enrichment capability of the MSP@PNAMAm magnetic hybrid microspheres, we further used MSP@PNAMAm to enrich glycopeptides in human serum from normal volunteers. The tryptic digest of 5 \(\upmu\)L human serum was treated according to the above procedure after reduction and alkylation. Then the eluate was collected and sent for nano-LC-MS/MS analysis. The human serum protein sample was analyzed by a LC-20 AD system (Shimadzu, Tokyo, Japan) connected to a LTQ orbitrap mass spectrometer (Thermo Electron, Bremen, Germany) equipped with an online nanoelectrospray ion source (Michrom Bioresources, Auburn, CA, USA). The lyophilized deglycosylated peptides were redissolved in solution containing 5% ACN containing 0.1% FA. Then the sample solution was loaded on a CAPTRAP column (0.5 mm \(\times\) 2 mm, MICHROM Bioresources, Auburn, CA, USA) in 4 min with a flow rate of 20 \(\upmu\)L/min. For a gradient separation, the gradient elution was performed as follows: Acetonitrile from 5% to 45% (95% ACN in 1% FA) over 100 min at a flow rate of 500 nL/min. For each cycle of duty, full mass scan was acquired from 400 to 2000 \(m/z\). The MS/MS spectra were obtained in data-dependent ddMS2 mode. The 12 most intense ions with charge 2, 3 or 4 were selected for the MS/MS run, and the a dynamic exclusion duration was 90 s. Finally, three parallel enrichment operations were performed as technical repeats.
## 6 MODIS LE product
### Methods
#### 2.2.2 Evaluation Methods  
We used three statistical metrics to evaluate six satellite-based LE products by comparing satellite-based LE estimates with flux tower observations and WBET in our study. The metrics include the coefficient of determination (R\({}^{2}\)), root mean square error (RMSE) and bias. The mathematical equations of the three indicators are as follows,  
\[R^{2}=[\frac{\sum_{i=1}^{n}(X_{i}-\overline{X})\big{(}Y_{i}-\overline{Y} \big{)}}{\sqrt{\sum_{i=1}^{n}(X_{i}-\overline{X})\big{(}Y_{i}-\overline{Y} \big{)}}}]^{2} \tag{3}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(X_{i}-Y_{i})^{2}} \tag{4}\]  
\[Bias=\frac{\sum_{i=1}^{n}(X_{i}-Y_{i})}{n} \tag{5}\]  
where \(X_{i}\) are the observed values and \(Y_{i}\) are the estimated values, \(\overline{X}\) and \(\overline{Y}\) are the average of \(X_{i}\) and \(Y_{i}\), respectively, and \(n\) is the total number of the samples.  
Taylor diagrams were also used to evaluate six satellite-based LE products [58]. A Taylor diagram is a polar-style graph and it has a standard deviation (SD) between the estimations and the observations, a correlation coefficient (R) and a centered root-mean-square-difference (RMSD). Here, SD refers to the radial distance from the origin, R is reflected by the cosine of the azimuth angle, and RMSD is the radial distance from the observed point.
## 4 Network Anomaly Forecasting
### The Structure of Network Anomaly Forecasting
#### 4.1.2 Anomaly Forecasting Function  
Utilizing the method introduced in Section 3, we conduct anomaly detection on these predicted network data. This involves comparing the predicted network performance with expected performance levels. Any deviations from the expected performance that exceed a certain threshold are considered anomalies.  
Similarly, by calculating an anomaly score for each predicted time window, we are able to evaluate the quality of the future network and detect any potential anomalies. This anomaly score serves as a quantifiable measure of network performance, allowing us to objectively evaluate the health of the network.  
By utilizing the anomaly score of the future network, we are able to achieve the function of anomaly warning. This proactive warning system alerts network administrators to potential problems before they have a significant impact on network performance. Specifically, if the anomaly score exceeds a certain threshold, the system can issue an alert to network administrators. These alerts indicate that corrective action may be necessary to prevent network downtime or degradation. This enables administrators to take proactive steps to address potential issues before they become more serious problems.