esa-sceva/satcom-chunk-collection huggingface dataset:
# Abstractive Sentence Compression with Event Attention
## 4 Abstractive Sentence Compression with Event Attention
The two attention weights are then combined by a weighted sum. That is, the final attention weight \(\gamma_{i}^{t}\) of a hidden state \(h_{i}\) at the \(t\)-th word generation is  
\[\gamma_{i}^{t}=(1-\lambda)\alpha_{i}+\lambda\beta_{i}^{t}, \tag{7}\]  
where \(\lambda\) (\(0\leq\lambda\leq 1\)) is a hyper-parameter to control the ratio of the event attention and the global attention. The final attention weights, \(\gamma_{i}^{t}\)'s, are used to compute the context vector \(c_{t}\) as  
\[c_{t}=\sum_{i=1}^{n}\gamma_{i}^{t}h_{i},\]  
and the context vector is used in generating \(y_{t}\), the \(t\)-th word by the decoder. Since the decoder depends on \(c_{t}\) as well as \(s_{t}\) in generating \(y_{t}\), Equation (4) is rewritten as  
\[P_{vocab}=softmax(W_{v}[s_{t};c_{t}]+b_{v}).\]  
Then, the probability of choosing a word \(w\) from a vocabulary set \(V\) is \(P(w)=P_{vocab}(w)\). The proposed model is trained to minimize the risk \(\mathcal{R}\) over training set \(T\) defined as  
\[\mathcal{R}(\theta)=\sum_{(X,Y)\in T}-\log P(Y|X;\theta),\]  
where \(\theta\) is the set of all parameters of the proposed model. Since the model above is based on a sequence-to-sequence model, it suffers from the chronic out-of-vocabulary problem [37]. To solve the problem, the model is extended to host the copy technique of the pointer generator [19]. The copy technique deals with the out-of-vocabulary problem by allowing the model to copy unknown words of a source sentence into the target sentence. That is, after expanding the vocabulary set \(V^{\prime}=V\cup U\) where \(U\) is a set of unknown words in the source sentence, \(P(w\in V^{\prime})\) is updated as  
\[P(w)=P_{gen}\cdot P_{vocab}(w)+(1-P_{gen})\sum_{i:w_{i}=w}\gamma_{i}^{t}.\]  
by introducing a soft switch \(P_{gen}\) which either generates a word from \(P_{vocab}\) or copies a word from the source sentence. If \(P_{gen}\) is higher than \(1-p_{gen}\), the word \(w\) is generated from \(P_{vocab}\). Otherwise, it is copied from the source sentence where the word to be copied is determined by the final attention \(\gamma_{i}^{t}\). The switch \(P_{gen}\) can be expressed as a generation probability computed by a network with two linear layers. Thus, \(P_{gen}\) is computed using the context vector \(c_{t}\), the decoder state \(s_{t}\), and the previous decoder output \(y_{t-1}\) as  
\[P_{gen}=\sigma(W_{c^{\prime}}(W_{c}[s_{t};c_{t}]+b_{c})+b_{c^{\prime}}),\]where \(W_{c}\), \(W_{c^{\prime}}\), \(b_{c}\) and \(b_{c^{\prime}}\) are the learnable weight parameters of the network and \(\sigma\) is the sigmoid function.
# Research on Wellbore Trajectory Prediction Based on a Pi-GRU Model
## 2 Materials and Methods
### Dataset  
The data used in this experiment originated from a certain mining area in Shanxi Province. In this mining area, an exploration and remediation project was implemented in the bottom plate area of the southern wing. A \"comb-shaped + branch-shaped\" drilling layout was adopted, and a total of 28 directional drilling holes were successfully constructed. The application of directional drilling technology in this area is relatively mature, and the accuracy and credibility of the construction data are relatively high, providing a good foundation for experimental research. In this study, the data for this experiment were sourced from the No. 2 mine field of the No. 2 drilling hole in this mining area. The drilling data within the range of 0 to 300 m were taken as the training set, as shown in Table 2, for training the neural network model. The drilling data within the range of 300 to 450 m were taken as the test set, as shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case** & **Learning Rate** & **GRU Nerves** & **Batch Size** & **Dropout Rate** \\ \hline
1 & 0.001 & 32 & 16 & 0.2 \\
2 & 0.001 & 64 & 32 & 0.3 \\
3 & 0.001 & 128 & 48 & 0.4 \\
4 & 0.005 & 32 & 32 & 0.4 \\
5 & 0.005 & 64 & 48 & 0.2 \\
6 & 0.005 & 128 & 16 & 0.3 \\
7 & 0.01 & 32 & 48 & 0.3 \\
8 & 0.01 & 64 & 16 & 0.4 \\
9 & 0.01 & 128 & 32 & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Orthogonal test results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Depth (m)** & **Azimuth (\({}^{\circ}\))** & **Dip Angle (\({}^{\circ}\))** & **Tool Face (\({}^{\circ}\))** & **Formation Types** \\ \hline
0 & 78.48 & 10.37 & 333.00 & limestone \\
3 & 79.11 & 10.29 & 20.00 & limestone \\
6 & 78.61 & 10.31 & 21.00 & limestone \\
9 & 78.63 & 10.30 & 22.00 & limestone \\
12 & 78.63 & 10.30 & 23.00 & limestone \\
15 & 78.63 & 10.30 & 24.00 & limestone \\ \hline \hline \end{tabular}
\end{table}
Table 2: Training dataset.  
A time series dataset was constructed using a sliding window approach, in which the window was progressively moved through the dataset. Starting from time point 'i', a sequence of time'steps' was extracted as sequential features. The shape of the time series features was an array of sizes (steps, 4), containing the borehole depth, dip angle, azimuth angle, and tool face angle at the current time step for the previous time steps. The non-time series features were represented as a one-dimensional array of shapes (geo_encoded_shape [1]), encoding the lithology type of the prediction point using one-hot encoding. The geological characteristics of the non-time series features refer to the stratum at the prediction location, which was input into the model as prior knowledge. For example, during the drilling process, the lithological conditions at different depths were already known, so the stratum information at the prediction point could be used as part of the model input. In this case, the model could utilize the stratum information to assist in making predictions.
## 2 Materials and Methods  
### Reagents, Standards, and Samples  
Analytical standards of PBS (96%) and BZ4 (\(\geq\)97.0%) were purchased from Sigma Aldrich (Steinheim, Germany). Methanol (MeOH, 99.8%) and acetic acid (99.5%) were obtained from Carlo Erba (Arese, Italy), sodium chloride (NaCl, 99.5%) was provided from Merck (Darmstadt, Germany), sodium hydroxide (NaOH, 98.0%) was purchased from BDH Chemicals (London, UK), hydrochloric acid (HCl, 37%) was provided from Panreac (Barcelona, Spain). Ultra-pure water was obtained from Mili-Q water Milipore purification systems (Billerica, MA, USA). Stock solutions of individual UV filters (1000 mg L\({}^{-1}\)) were prepared in MeOH, and standard mixtures for instrumental calibration were prepared by appropriated dilution of the previous stock solutions. The polymeric phases used were \(N\)-vinylpyrrolidone-divinylbenzene (HLB; particle size 30, surface area 810 m\({}^{2}\) g\({}^{-1}\) and pH stability 1-14, Waters, Milford, MA, USA) and polystyrene-divinylbenzene (PS-DVB; particle size 40-120 um, surface area 1200 m\({}^{2}\) g\({}^{-1}\) and pH stability: 1-13, Merck, Kenilworth, NJ, USA). The activated carbons, AC1 (surface area 1400 m\({}^{2}\) g\({}^{-1}\); pH\({}_{\rm{PZC}}\): 2.2) and AC2 (surface area 1400 m\({}^{2}\) g\({}^{-1}\); pH\({}_{\rm{PZC}}\): 6.4) were obtained from Salmon & Cia (Lisbon, Portugal). All water samples were obtained in the metropolitan area of Lisbon (Portugal). The tap water samples were obtained from our lab; the estuarine and sea water samples from the coast, and the wastewater samples from Alcantara wastewater treatment plant.
# Visual Navigation Systems for Maritime Smart Ships: A Survey
## 3 Visual Perception Navigation Technology
### Integrated Perception
In addition to the two-by-two combination of sensing technologies, some scholars have also studied the combination of multiple sensing data to enhance the navigation safety of smart ships. The use of any one ship motion sensing method alone may have problems such as inconsistent information and inaccurate data. Therefore, [PERSON] et al. (2022) proposed a multi-sensor integrated perception system for motion detection of ships and constructed a multi-sensor integrated ship motion perception system hardware platform consisting of radar, AIS, camera and other attachments, which significantly improved the consistency of the information and the accuracy of the data for monitoring ship motion [115].
# Research into Robust Federated Learning Methods Driven by Heterogeneity Awareness
## 3 Heterogeneity-Aware Federated Optimization Framework
### Mechanisms for Measuring Heterogeneity
#### 3.2.1 Local Heterogeneity Based on Feature Statistics  
After completing a local forward pass, each client extracts feature embeddings for a batch of data using the feature extractor \(f(x)\), resulting in an embedding matrix \(F\in R^{B\times D}\), where \(B\) denotes the batch size and \(D\) the feature dimensionality. We first compute the mean \(\mu_{k}\) and variance \(\sigma_{k}\) of the batch embeddings as follows.  
\[\mu_{k}=\frac{1}{B}\sum_{i=1}^{B}f_{i},\sigma_{k}=\frac{1}{B}\sum_{i=1}^{B} \left(f_{i}-\mu_{k}\right)^{2} \tag{1}\]  
Next, incorporating a center shift term, we define a weighted variance-based heterogeneity metric.  
\[H_{k}^{\text{var}}=\frac{1}{D}\sum_{j=1}^{D}\left(\sigma_{k}^{j}+\delta\right) +\gamma\cdot\|\mu_{k}-c\|_{2}^{2} \tag{2}\]  
where \(\delta\) is a small constant added to prevent instability due to zero variance, \(\gamma\) is a weighting factor controlling the influence of center deviation, and \(c\) represents the client's current feature center, which may be historical or predefined as a global reference.  
This metric jointly considers the dispersion (variance) and deviation (mean shift) of feature distributions, effectively capturing the overall variation trend of the client's local data.
## 2 Overview of AC Analysis Methods
### Linear Least Squares Sine-Fit (LSQ)  
Linear least squares for spectral analysis, also known as Vanicek method [12] and Sinewave-fit [13; 30] uses linear regression, also known as ordinary least-squares to approximate the signal as the sum of sine and cosine signals. For a signal \(x[k]=A\cdot sin(2\pi fk\delta t+\varphi)\), to which the amplitude and the phase of the frequencies \(\{f_{1},\ldots,f_{N_{f}}\}\) are desired, the linear algebra system describing the sum of sine could be written as follows:  
\[A_{LSQ}p=X \tag{14}\]  
where \(A_{LSQ}\) is a \(N\times 2N_{f}\) matrix, which contains the trigonometric coefficients, as shown in (15), \(X\) is a column vector of N elements containing the measurements and \(p\) is a column vector of \(2N_{f}\) elements containing the linear combination coefficients of the cosine and sine signals, whose sum equals to the signal, which is analogous to the real and imaginary part of the spectrum.  
\[A_{LSQ}=\begin{bmatrix}cos(2\pi f_{1}\delta t)&sin(2\pi f_{1}\delta t)&\ldots &cos(2\pi f_{N_{f}}\delta t)&sin(2\pi f_{N_{f}}\delta t)\\ cos(4\pi f_{1}\delta t)&sin(4\pi f_{1}\delta t)&\ldots&cos(4\pi f_{N_{f}}\delta t )&sin(4\pi f_{N_{f}}\delta t)\\ \vdots&\vdots&\vdots&\vdots&\vdots\\ cos(2N\pi f_{1}\delta t)&sin(2N\pi f_{1}\delta t)&\ldots&cos(2N\pi f_{N_{f}} \delta t)&sin(2N\pi f_{N_{f}}\delta t)\end{bmatrix} \tag{15}\]  
In this case, resolving \(p\) could be according to (16)  
\[p=(A_{LSQ}^{T}A_{LSQ})^{-1}A_{LSQ}^{T}X \tag{16}\]  
As we are using the multisine as the sum of sine, i.e., not cosines, the amplitudes and phase components of a frequency \(f_{k}\) could be defined according to (17) and (18)  
\[A_{k}=\sqrt{p[2k]^{2}+p[2k+1]^{2}}) \tag{17}\]  
\[A_{k}=atan2(p[2k],p[2k+1])) \tag{18}\]
# Performance Analysis of a Forecasting Relocation Model for One-Way Carsharing
## 4 Result and Discussion  
The relocation models for one-way carsharing are evaluated by the simulation model. The impact of the number of cars and reservations was also analyzed. The simulation models were applied for one week of reservation data with three evaluation criteria: relocation cost, acceptance ratio, and utilization ratio. A trip generator was developed to provide the artificial reservation data from real carsharing data. The reservation data was a combination of round-trip and one-way reservations. Since the carsharing company in Korea only operates round-trip services, the real proportion of one way reservations cannot be presented. As a solution, in this study the trip generator generated the same amount of round-trip and one-way reservations for the week, i.e., 50% round-trip, 50% one-way. In addition, regarding the distribution of one-way and round-trip reservations, [PERSON] et al. has revealed its impact on the system performance [25]. The study showed that as the distribution of one-way reservations increases, the utilization ratio as well as the acceptance ratio will be reduced. The high number of one-way reservations leads to imbalance in the distribution of cars at each station  
Figure 7: Car status over time (**a**) without relocation; (**b**) rebalancing relocation; and (**c**) proposed forecasting relocation.  
as most cars end up in unpopular departure stations (low demand stations) and most customers (from high demand stations) are unable to make a reservation.
###### Abstract  
This paper addresses the issues of Beidou navigation signal interference and blockage in complex substation environments by proposing an intelligent collaborative navigation model based on Beidou high-precision navigation and binocular vision recognition. The model is designed with Beidou navigation providing global positioning references and binocular vision enabling local environmental perception through a collaborative fusion strategy. The Unscented Kalman Filter (UKF) is used to integrate data from multiple sensors to ensure high-precision positioning and dynamic obstacle avoidance capabilities for robots in complex environments. Simulation results show that the Beidou-Binocular Cooperative Navigation (BBCN) model achieves a global positioning error of less than 5 cm in non-interference scenarios, and an error of only 6.2 cm under high-intensity electromagnetic interference, significantly outperforming the single Beidou model's error of 40.2 cm. The path planning efficiency is close to optimal (with an efficiency factor within 1.05), and the obstacle avoidance success rate reaches 95%, while the system delay remains within 80 ms, meeting the real-time requirements of industrial scenarios. The innovative fusion approach enables unprecedented reliability for autonomous robot inspection in high-voltage environments, offering significant practical value in reducing human risk exposure, lowering maintenance costs, and improving inspection efficiency in power industry applications. This technology enables continuous monitoring of critical power infrastructure that was previously difficult to automate due to navigation challenges in electromagnetically complex environments.  
Beidou navigation; binocular vision; multi-sensor fusion; dynamic obstacle avoidance; path planning
## 3 Antenna Structure
A small rectangular hole is formed in the ground plane PCB for electrical insulation between the via pad and the ground plane conductor. The via pad can be electrically connected to the microstrip line by soldering. The box 3 in Figure 3a shows a method for connecting an input of the QHC to a coaxial cable. Its magnified view is presented in Figure 3d. The RHCP port of the QHC input is connected to one coaxial cable, while the other input port (for LHCP operation) is connected to another coaxial cable for the \(L\)-band satellite phone application. The coaxial center conductor is routed to the input end of the quadrature hybrid coupler through a via, while the coaxial outer conductor is electrically connected to the ground plane (GP) conductor on the top surface of the GP PCB. Again, soldering can be used for the electrical connection of conductor surfaces. Figure 4a shows the microstrip QHC realized on the backside of the GP PCB, while Figure 4b shows the vias and the via pads on the top side of the GP PCB. To achieve an impedance bandwidth large enough for worldwide GNSS signal reception, a three-branch-line QHC is employed. To reduce the size, the main lines are meandered. The output lines of the QHC are routed in a circular shape to the position of the dipole feed line.
# Valorization Potential of Oilseed Cakes by Subcritical Water Extraction
## 4 Discussion
### Amino Acid Composition  
The content of individual amino acids (mg/100 g dry extract) in pumpkin, hemp and flax seed cake extracts is shown in Table 3. Cystine and cysteine were not detected by this method of analysis. A strong correlation (r = 0.833; \(p<\) 0.05) was observed between the results obtained by Kjeldahl and RP-HPLC methods. In general, the extraction in a carbon dioxide atmosphere resulted in higher (\(p<\) 0.05) amino acid recoveries from all the analysed matrices. The sum of total amino acids in pumpkin, hemp and flax seed extracts were, respectively, 75.09; 409.51 and 65.23 mg/100 g dry extracts. Lower (\(p<\) 0.05) contents of total amino acids were found for extractions under a nitrogen atmosphere with pure water: 20.40 (pumpkin); 219.50 (hemp) and 18.84 (flax) mg/100 g dry extract. The addition of hydrochloric acid under nitrogen atmosphere potentiated the release of amino acids from the peptides/proteins. The amino acid recoveries (8.48-38.4% total protein content) obtained by different subcritical extraction conditions were in line with the values reported for other vegetable sources (5-53%) [32].  
All oilseed cake extracts presented high levels of essential amino acids (EAA: Thr, His, Lys, Val, Met, Trp, Phe, Ile and Leu). The pumpkin seed extracts presented significantly higher (\(p<\) 0.05) %EAA (51.49-58.58%) than flax (35.91-40.66%) and hemp (22.60-27.28%) seed cake extracts. The highest content of essential amino acid Lys (12.37 \(\pm\) 0.10 mg/100 g dry extract) was observed in pumpkin seed extracts obtained in carbon dioxide atmosphere, while Thr (34.70 \(\pm\) 0.12 mg/100 g dry extract) and Leu (5.31 \(\pm\) 0.06 mg/100 g dry extract) were the most abundant in hemp and flax seed extracts, respectively, applying same extraction mode. In general, these amino acids are also dominant in the raw materials [33; 34; 35]. High concentrations of EAAs in the characterized oilseed cake extracts suggest their potential use as food ingredients with beneficial amino acid profile.  
Hemp seed cake extracts showed high content of the flavour amino acids (FAA) Asp, Glu and Ala, with respective mean ranges for three extraction conditions of 49.17-72.67 (Asp); 41.98-77.86 (Glu) and 10.77-17.83 (Ala) mg/100 g dry extract. In contrast, the levels of Gly were significantly (\(p<\) 0.05) higher in pumpkin (4.82 \(\pm\) 0.02 mg/100 g dry extract) and flax (6.08 \(\pm\) 0.05 mg/100 g dry extract) seed extracts, especially in extracts obtained in a carbon dioxide atmosphere. The %FAA were as follows: 39.85-47.63% (hemp extracts) \(>\) 27.95-41.20% (flax extracts) \(>\) 16.56-24.88% (pumpkin extracts). The high levels of FAA may contribute to desirable sensory properties of the three oilseed cake extracts.
## 4 Discussion
### Head Yaw Predicts the Geographical Turning Point at a Constant but Unknown Distance
This anticipation can be relevant for human-robot teams interacting at an intimate distance (up to 0.46 m), but also at a personal distance (0.46 m to 1.2 m) or even at a social distance (1.2 m to 2.4 m) [41]. However, for larger distances of separation between the human and the robot, turn anticipation from these variables is not expected to suppose a relevant improvement. Many technical alternatives are available for the estimation of Head Yaw and Trunk Roll in human-robot interaction applications. Optical motion capture systems require a precise deployment of cameras and complex calibration procedures, which can be a limitation in some unstructured interaction scenarios. As an alternative, wearable sensors attached to the body in clothes, hats, headbands, glasses, etc., have been successfully applied to the early detection of turns [10; 32]. The downside is that inertial sensors suffer from signal drift issues. However, this problem is diminishing due to technological advances in the sensors and in the processing methods [42].
## 2 Related Works
### Graph-Based Approaches  
Graph-based approaches first encode high-dimensional data into a neighborhood graph to preserve the local structures of data and embed a neighborhood graph into a low-dimensional space, generally a two-dimensional space. Laplacian eigenmaps (LEs) [34] are one of the classical methods in graph-based approaches and they derive a low-dimensional representation as the eigenvectors of the graph Laplacian of the neighborhood graph. An LE is a linear method with rich theoretical properties for its convergence and loss function [35; 36]. t-Distributed stochastic embedding (t-SNE) [10] is a long-standing nonlinear graph embedding approach that provides a low-dimensional representation by matching the neighborhood structure. Specifically, t-SNE minimizes the Kullback-Leibler (KL) divergence between neighborhood graphs in high- and low-dimensional space. For its strong visibility, t-SNE is frequently used to visualize complex structures, such as biological data and the middle layers of deep neural networks, and researchers have developed a computationally efficient version of t-SNE introducing the Burnes-Hut algorithm [11] or hierarchical optimization [37]. UMAP [12] is a state-of-the-art method in graph-based approaches and is a more efficient algorithm than t-SNE. UMAP derives a \(k\)-nearest neighborhood graph as a fuzzy topological representation of data. Then, UMAP minimizes the cross-entropy of neighborhood graphs in high- and low-dimensional space, which does not necessitate the normalization of the graph, in contrast to t-SNE which necessitates this normalization for the calculation of KL divergence. Furthermore, the cross-entropy objective is considered as a combination of the attraction force of neighbors and the repulsion force of non-neighbors, allowing negative-sampling-based optimization to boost the scalability of the UMAP algorithm. LE, t-SNE, and UMAP have similar properties [38] in their loss functions and produce visible two-dimensional representations compared with the other dimensionality reduction approaches. However, their representations do not preserve the global structures of data because of the encoding process of data into neighborhood graphs. Researchers have developed approaches for global preservation by introducing a graph construction scheme based on the triplet [39; 40] or diffusion process [41]. However, they estimate global structures by connecting local information, which inevitably causes failure to preserve the global structure. In this study, we focus on PCA-based approaches, which are suited for global preservation, and introduce them in the following subsection.