esa-sceva/satcom-chunk-collection huggingface dataset:
### Quality Parameters
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Treatment** & **Ash Contents (\%)** & **Oil Contents (\%)** & **N (\%)** & **Protein (\%)** & **Zn (\(\mathbf{\mu}\mathbf{g}\)\(\mathbf{g}\)\(\mathbf{}^{-1}\) DW)** \\ \hline No Zn & 2.9 \(\pm\) 0.09 c & 0.04 \(\pm\) 0.004 b & 1.80 \(\pm\) 0.02 c & 10.5 \(\pm\) 0.23 d & 34.5 \(\pm\) 0.2 f \\ ZnSO4 & 5.7 \(\pm\) 0.09 ab & 0.07 \(\pm\) 0.004 ab & 2.50 \(\pm\) 0.09 a & 14.3 \(\pm\) 0.2 b & 43.5 \(\pm\) 0.3 b \\ ZSB & 4.0 \(\pm\) 0.09 bc & 0.07 \(\pm\) 0.002 ab & 2.20 \(\pm\) 0.04 b & 12.5 \(\pm\) 0.3 c & 37.5 \(\pm\) 0.2 e \\
1.5\% Zn coated urea & 5.0 \(\pm\) 0.3 ab & 0.07 \(\pm\) 0.002 ab & 2.40 \(\pm\) 0.04 a & 13.9 \(\pm\) 0.3 b & 38.2 \(\pm\) 0.09 d \\
1.5\% bio-activated Zn coated urea & 6.0 \(\pm\) 0.4 a & 0.09 \(\pm\) 0.002 a & 2.60 \(\pm\) 0.14 a & 14.9 \(\pm\) 0.2 a & 45.2 \(\pm\) 0.09 a \\
1.5\% Zn blended urea & 4.1 \(\pm\) 0.04 bc & 0.05 \(\pm\) 0.002 b & 2.43 \(\pm\) 0.03 ab & 13.8 \(\pm\) 0.2 b & 42.2 \(\pm\) 0.094 c \\ LSD & 1.8047 & 0.0318 & 0.2374 & 0.5976 & 0.3986 \\ \hline \hline \end{tabular} Note: Values show means \(\pm\) S.E. where \(n\) = 3. Means sharing the same letters within the column do not differ significantly (\(p<0.05\)).  
\end{table} 
Table 1: Comparative effectiveness of the optimum level of zinc blended, zinc coated, and bioactivated zinc-coated urea to improve quality parameters under field conditions in wheat. compared to the control. The bacterium ZSB i.e., Bacillus sp. AZ6 increased Zn contents up to 8% with respect to the control. As compared to recommended Zn (ZnSO\({}_{4}\)), 3.6% increase in grain Zn concentration was observed with 1.5% bio-activated Zn coated urea. The application of 1.5% bio-activated Zn coated urea caused an increase of 18.3 and 7.1% in grain Zn concentration as compared to 1.5% Zn coated and 1.5% Zn blended urea, respectively.
# Anticipating Future Behavior of an Industrial Press Using LSTM Networks
## 5 Experiments and Results
### LSTM Models and Dataset Partition  
After processing the data, experiments were performed with an LSTM model. The model included an encoder and decoder, with one hidden LSTM layer in the middle and a dense layer at the output. The model was used to train and predict, with six variables that represent data coming from the paper press sensors. The goal was to forecast the value of those variables with the highest possible level of confidence so that it brings added benefits in predictive maintenance.  
Figure 5 describes the architecture of one of the network models used. The models were implemented in Python using the TensorFlow library and Keras.  
The experiments were performed aiming to obtain a prediction for all variables one month in advance, from a window of a number of past samples.  
The LSTM models received, as an input, a sequence consisting of the composition of a number of samples for each variable. The number of samples depended on the window size and the resampling rate used. The output sequence is composed of the values predicted for each of the variables.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Window 70 Days**} \\ \hline  & **C. Intensity** & **Hydraulic** & **Torque** & **VAT** & **Velocity** & **Temperature** \\ \hline MAPE & inf & 8.46 & inf & 98.19 & inf & 11.59 \\ MAE & 3.52 & 6.57 & 24.73 & 10.53 & 14.88 & 4.21 \\ \hline  & **Train** & **Test** & & & & \\ \hline RMSE & 79.52 & 79.64 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Prediction results without cleaning discrepant data in the database, with a window of 70 samples and 40 LSTM units.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Window 70 Days**} \\ \hline  & **C. Intensity** & **Hydraulic** & **Torque** & **VAT** & **Velocity** & **Temperature** \\ \hline MAPE & 2.52 & 3.02 & 2.44 & 13.10 & inf & 2.48 \\ MAE & 0.76 & 2.28 & 0.37 & 1.32 & 0.57 & 0.94 \\ \hline  & **Train** & **Test** & & & & \\ \hline RMSE & 1.71 & 1.97 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Forecast results with treatments in the database with 40 LSTM units.  
To train and test the models, the dataset was divided into train and test subsets. Validation was performed using the test set, but those samples were not incorporated into the training set. The training set contained 85% of the samples and the test set the remaining 15% of samples. These values are adequate for convergence during learning. As an example, Figure 6 shows a learning curve for a model with 70 units in the middle layer and a window of 30 lag samples. The figure shows that learning converges and takes fewer than 10 epochs. The remainder experiments were performed using 100 epochs.
## 3 Materials and Methods
### Validation Procedures  
The framework's validity can be assessed through:  
* Validation of metrics across multiple datasets and scenarios.
* Expert review of qualitative outputs and framework applications.
* Triangulation of insights derived from visualization techniques, statistics, and AI outputs to ensure consistency and reliability.  
Our mixed-methods approach integrates qualitative insights and quantitative metrics within a cohesive analytical framework. Qualitative elements include an interpretive modeling of institutional theory, examination of communication structures, and the use of socio-organizational concepts (e.g., [PERSON]'s collective action principles). Quantitative components encompass statistical trend analyses, advanced visualization (e.g., sunburst charts, geographic maps), and AI-driven modeling techniques like graph neural networks. By combining these methods, we leverage the strengths of both rigorous data-driven computation and rich, context-sensitive theoretical understanding. This synthesis allows us not only to identify patterns and predict outcomes but also to interpret these findings through the lens of institutional theory, thereby yielding more robust and actionable policy insights.  
Our framework validation strategy emphasizes practical implementation in real-world scenarios, with a specific focus on the hospitality sector [32; 33] as a representative case study. This domain was chosen for its complex interplay of temporal, spatial, and hierarchical factors--characteristics that align well with our framework's multi-layered approach. Through this implementation, we demonstrate how our theoretical components manifest in practical applications, from data collection through to intervention design, while incorporating advanced techniques such as graph neural networks into the dashboard, and we discuss the use of large language models.
# Topic Detection Based on Sentence Embeddings and Agglomerative Clustering with Markov Moment
## 5 Discussion  
As we have demonstrated above, the proposed method shows high efficiency against the baseline; however, it is not deprived of shortcomings.  
As for data pre-processing and the dataset, the most notable downside to models, such as USE, are their requirements in terms of hardware and their time-consuming nature. This is especially true then the model is trained from scratch, but even the inference process can be rather slow then dealing with large corpora. Additionally, News20 has very well-defined classes and, as such, may not be representative of the performance on the real data. However, it provides a good enough estimate of the model performance by giving us the ability to check the clustering performance via comparing obtained cluster labels with the true labels defined for this set by human coding.  
As for the clustering procedure with the Markov moment, it still possesses some residual subjectivity, as sensitivity of the stopping criterion may be changed by the researcher. As we noted above, clustering _per se_ is a subjective instrument when used for topicality or sentiment detection, as almost all of the stopping criteria (that is, the criteria for selection of the optimal number of clusters) are subjective by nature, being based on heuristic approaches to problem resolution. The use of \"e-2 hypothesis\" that we have suggested above aims at reducing the level of subjectivity. Additionally, more experiments are needed to collect empirical data on the optimal values of the stopping criterion for the real-world data. In order to overcome the problem of subjectivity, the data might as well undergo further experiments with various procedures for stopping, while the results might be compared to expert opinion, given that the level of agreement of 0.7-0.8 among the human coders is reached. However, we need to note that our prior experiments with topic models and human coding have shown that manual coding should not be taken as baseline. In particular, for pairs of human coders, topic interpretability and proximity varied for over 10 times, depending on prior familiarity with the dataset and coding experience [33]. Thus, the quality assessment of clustering remains an issue. Our approach significantly helps researchers to reduce subjectivity but does not eliminate it completely. The advantage of our approach is that it allows for testing subtle changes in topicality distribution by nuanced variances of the stopping criterion, rather than changing the number of topic slots in arbitrary manner.  
Possible limitations of our work may also lie in two aspects. The utilization of transformer-based sentence embeddings and USE may be advised to scholars interested in text clustering, but application of USE may be technically unfeasible for some of them. Additionally, the limitations might be linked to the nature of the datasets, which, as mentioned before, stands between regularized text collections and highly noisy data from social media.
# Handover-Driven Interference Management for Co-Channel Deployment of Femto- and Macro-Cells
## 3 System Model
### Handover Procedure  
Figure 2 illustrates the procedure of a macrocell-to-femtocell handover considered in this paper, which is analogous to that of 5G NR [23]. A user measures the pilot signal strength of neighbor cells and reports the measurement to his serving macrocell according to the rules set by the system information (SI). If a target femtocell is not uniquely identified due to _physical cell ID confusion_ (due to the typical cell size of femtocells being much smaller than macrocells, there can be multiple femtocells within the coverage of the source macrocell that have the same physical cell ID, which leads to a condition referred to as _physical cell ID confusion_, where the source macrocell is unable to determine the  
correct target femtocell for handover [24]), the macrocell requests the user to report the femtocell's global cell ID by acquiring the SI of the femtocell. This operation requires the user to suspend reception and transmission with the source macrocell for a certain time duration, thus degrading the user's performance. Based on a new measurement report, the source macro BS makes a handover decision and sends a handover request to the target femtocell. Upon reception of the acknowledgment (Ack) for the handover request from the femtocell, the macro BS transmits a handover command to the user, which triggers his service interruption immediately. The user accesses the target femtocell via a random access channel and gets allocated new radio resources from it. This implies the user experiences a performance loss due to a handover, which is further aggravated by the additional SI acquisition step.
# Design of a Multi-Condition Emotional Speech Synthesizer
## 4 Multi-Condition Emotional Speech Synthesizer  
### Model  
Tacotron consists of four parts: an Encoder, which extracts numerical features representing the input text; a Decoder, which generates a mel-spectrogram based on the output  
Figure 4: Results of post-processing.  
Figure 3: Two actors during recording, using conversation between a male and a female.  
of the Encoder; an Attention module, which creates a relationship with the Encoder output value when creating the spectrogram in the Decoder; and a Vocoder, which generates speech waves from the generated spectrogram. In this paper, we propose multi-conditional emotional synthesizer by providing additional information to Tacotron when synthesizing speech. The objective of the proposed method was to train a TTS system using speech data from multiple speakers, so that the emotional states of different speakers complement each other.  
Unlike previous studies, we extended a single embedding vector, such as a speaker embedding vector or an emotional embedding vector, so that the model can reference multiple conditions. Each of the multi-condition embedding vectors were initialized randomly with a uniform distribution over \([-0.1,0.1]\) and the network was trained using backpropagation:  
\[z=concat(s,e), \tag{2}\]  
\[Multicondition=\tanh\left(w_{f,n}\times z\right), \tag{3}\]  
where \(s\) is a speaker embedding vector, and \(e\) is an emotional embedding vector. We extended the model by adding a multi-condition embedding vector (Equation (3)) to synthesize speech according to multiple conditions. Unlike previous studies, the information is stored in low-level vectors according to speakers and emotions. Each embedding vector is independent, but the input data can complement each other, although the model learns according to the embedding vector. For example, if there is an untrained pronunciation 'a' in emotion B of speaker A, the model can train the pronunciation of 'a' by training the model from the pronunciation of 'a' in emotion C of speaker B. Previous studies have shown that simply adding an embedding vector to the input layer is not effective [6]. Therefore, a multi-condition embedding vector was used at several locations, so that each speaker and emotion affected the model. The whole structure and the example of inputting the multi-condition embedding vector are shown in Figure 5.
# Impact Analysis and Optimization of Key Material Parameters of Embedded Water-Stop in Tunnels
## 2 Optimization of Key Parameters for Water-Stop Materials
### Analysis of the Influence of Water-Stop Hardness on Its Deformation Force State
It can be concluded that after working condition 6, the bond strength between the water-stop and the concrete increases, and the bond failure range decreases (the bond of the water-stop ribs is not failed). In the process of stress, with the increase in deformation, the stress of the water-stop increases continuously before the bond failure between the wing plate and the concrete lining. Once the bond between the wing plate and the lining fails, the stress of the water-stop drops sharply, and then due to the next part of the bond not failing, the stress of the water-stop increases with the increase in deformation, and the change is more dramatic. Similarly, since the final stress is borne by the water-stop ribs after deformation, the stress of the water-stop deformation tends to be similar in all working conditions before working condition 7. In working condition 8, due to the excessive bond strength, the wing plate between the first water-stop ribs on both sides still bears the deformation, and the stress of the water-stop deformation is relatively large at this time. Figure 12 shows the bonding failure area between the water-stop and concrete lining after settlement deformation for Cases 4 to 8. As the bonding strength increases, the bonding  
Figure 11: Bond failure area of partial working conditions in tensile deformation. (**a**) Working condition 4. (**b**) Working condition 5. (**c**) Working condition 6. (**d**) Working condition 7. (**e**) Working condition 8.  
failure area gradually decreases. Before Case 6, the bonding failure area includes or is close to the water-stop ribs, and the water-stop ribs participate in the stress, so there is no obvious relationship between the water-stop deformation stress and the bonding strength at this time. However, after case 6, the bonding failure area is only within the water-stop wing, not including the water-stop ribs, so the water-stop deformation stress is relatively large at this time, and as the bonding strength increases, the water-stop deformation stress will continue to increase. In summary, when the bonding force reaches a certain value, the bonding failure area between the water-stop and the concrete lining will decrease with the increase in the bonding force, except for the compressive deformation. The deformation stress of the water-stop will increase with the increase in the bonding force. When the bonding force is relatively large, the deformation stress of the water-stop exceeds the critical stress value. This indicates that increasing the bonding force between the water-stop and the concrete is an effective optimization method, but it may lead to excessive deformation stress of the water-stop. When carrying out such optimization, it should be combined with the optimization of the geometric structure of the water-stop.
## 5 Water Pool Experiment
### Analysis of Experimental Results
#### 5.2.3 CSK Spread-Spectrum Coding System  
The experimental results for the CSK spread-spectrum communication system under different coding methods are shown in Figure 19. Analyzing the results of pool experiments, for channel coding methods with similar code lengths and consistent communication rates, spread-spectrum communication systems incorporating CCSDS codes have certain performance advantages over spread-spectrum communication systems incorporating other coding methods.
# Explainable Artificial Intelligence: Advancements and Limitations
## 4 Visual Explanations
### Pixel Attribution
#### 4.2.2 Class Activation Mapping (CAM)
The main difference of this approach from GradCAM is taking into account only positive partial derivatives of feature maps at the last convolutional layer. ScoreCAM [64], presented by [PERSON] et al., gets rid of the dependency on gradients of the GradCAM method by masking input images according to feature maps to obtain perturbed images. Scores obtained by forward passes of these images are used as weights, for instance, \(w_{c}^{k}\) for feature map \(k\). This method claims to reduce noise in the saliency maps caused by gradients. Advanced CAM methods, including Grad-CAM and Grad-CAM++, have been widely adopted for visualising the important regions of an image that contribute to a model's decision, but they also come with notable limitations. One key issue is that while they can be applied to a broader range of architectures than traditional CAM (which requires GAP), the visualisations they produce are often still relatively coarse and low-resolution. This can obscure fine details that might be critical in tasks such as medical image analysis or detailed object recognition, where understanding subtle features is essential. Moreover, CAM-based methods generally highlight large regions of an image, making it difficult to differentiate between important and unimportant features within these highlighted areas. Additionally, CAM methods typically focus on explaining a model's final decision for a specific class, offering little insight into the intermediate layers or the broader decision-making process across the network. This narrow focus limits their ability to provide a holistic understanding of how the model processes and interprets input data. Overall, while CAM methods offer valuable insights, their lack of spatial precision and limited scope reduce their effectiveness in generating deep, fine-grained explanations.
# An Electroglottograph Auxiliary Neural Network for Target Speaker Extraction
## 3 Materials and Methods
### Methods
#### 3.2.4 The Electroglottograph Pre_Processing Algorithm  
The original EGG signals had baseline drift interference that was caused by irrelevant vibrations. To obtain EGG signals with less interference, the EGG signals were filtered in the time domain and frequency domain. High-pass filtering was performed first. A high-pass filter was used to remove interference from the power supply.  
Figure 5: A comparison between the spectrograms of EGG signals and speech signals from the same utterance.  
The frequency-domain features of EGG signal were also utilized in the neural network. However, the EGG signals were input after undergoing STFTs, which could negelect time-domain features. As a result, considering the relevance of EGG signals and speech signals in the time domain, we used the signal envelopes [51] from the Hilbert transforms to identify the speech of the target speaker and extract it from the EGG signals. The process is shown in Figure 7.  
Since the EGG signals were recorded at the same time as the speech signals, they were similar in some parts of the time domain. In silent segments of speech, there were no vibrations in the throat, so the EGGs showed silent segments. The same occurred in other segments as well and the trend of the waveforms was cons  
Figure 6: The configuration of our EGG_auxiliary network, which utilized a Softmax layer to extract the features of a target speaker via an attention mechanism. The mixed speech and EGG signals were processed simultaneously.  
Figure 7: An example of a waveform in CDESD. The top panel shows the speech of the speaker, the middle panel shows the EGG signals, and the bottom panel shows the EGG signals after high-pass filtering.  
were correlations between the EGG signals and the speech signals in some of the speech segments, which meant the time-domain features of EGG signals could be used to solve the speech extraction problems. So, we also developed a pre_processing algorithm to deal with the correlations between the EGG and speech signals and applied it to the speech extraction task. The method filtered the silent segments and processed other relevant parts of speech. The average value of the filtered envelope signals was taken as the threshold, with values greater than the threshold considered to be 1 and those less than the threshold were considered to be 0.5, according to the window function method. Finally, we obtained the target speaker's speech. The division method was accurate for voiced segments involving vowels but could lose information from the original speech waveforms of unvoiced and silent sounds.  
As shown in Figure 8, the envelopes of the EGG signals were calculated to process the mixed speech and the new mixed speech was then preliminarily processed (the silent segments from the target speech were filtered out and some of the unvoiced segments were processed before being input into the network).
# Design of Parabolic Off-Axis Reflector Optical System for Large Aperture Single Star Simulators
## 5 Experimental Verification
### Focal Length Test of Optical System  
In the optical system focal length test, the theodolite and Bolo plate are used to coordinate each other. The theodolite is placed at the optical outlet of the optical system, and the Bolo plate is placed at the focal plane position of the optical system. Using theodolite to aim at the engraved lines on the Bolo plate, observe the line pairs with a spacing of 10 mm on the Bolo plate, record the azimuth angle difference between the lines at this moment, and then calculate the focal length value by using the focal length calculation formula.  
\[f=\frac{L/2}{\tan(\theta/2)} \tag{11}\]  
where \(L\) is the distance between pairs of lines on the Bolo plate, and \(\theta\) is the azimuthal angle difference between the lines at this moment. Repeatability measurements were taken for each test result, and the obtained test values were recorded. The results of the focal length test are shown in Table 12.  
From Table 12, it can be obtained that the measured focal length value of the optical system is 5007.131 mm, which is more than 5000 mm after taking the average value and meets the requirements of the technical specifications.