esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Forecasting Methods
### RNN-Based Models  
LSTM-NAT [25] represents an innovative application of RNNs for spacecraft telemetry analysis, emphasizing the use of LSTM [64] to identify anomalies within complex data. Unlike traditional methods, LSTM-NAT employs a non-parametric dynamic thresholding method, utilizing an unsupervised approach to assess residuals and determine whether prediction errors indicate spacecraft anomalies. However, its reliance on one-dimensional outputs limits its ability to handle high-dimensional data in multivariate scenarios. Building upon this foundation, LGMAD [26] integrates LSTM with the Gaussian Mixture Model (GMM) [65], providing a comprehensive solution for low-dimensional system characteristics. This method not only enhances the efficiency of anomaly detection through the health factor but also expands its application to the processing of MTS by leveraging the strengths of both LSTM and GMM in capturing temporal dynamics and analyzing feature correlations. Another notable advancement in capturing temporal dynamics is the THOC [27]. By introducing the Temporal Hierarchical One-Class network, which uses a dilated RNN with skip connections [66], the limitations of LSTM in capturing multi-scale temporal dynamics are addressed. The hierarchical clustering process of THOC and the introduction of a self-supervision task in the temporal domain significantly enhance the model's ability to detect anomalies in real-world time series data. However, these schemes may require adjustments for specific types of multi-seasonal time series data to ensure their accuracy and efficiency. [PERSON] et al. [28] proposed an unsupervised scheme with multi-seasonality. AD-LTI introduced a new metric--Local Trend Inconsistency (LTI), which can be computed efficiently and robustly scores the probability of anomalies for each data point. It overcomes the challenges of requiring labeled data and the computational intensity of processing large volumes of data, making it suitable for a variety of event-sensitive scenarios.
# Adaptive Local Context and Syntactic Feature Modeling for Aspect-Based Sentiment Analysis
## 1 Introduction
To solve the above aspect-based sentiment analysis problem, this paper proposes the adaptive aspect-based sentiment analysis model (A-ABSA), which combines local information and adaptive local context methods to achieve the best performance on the commonly used SemEval-2014 Task 4 dataset [3]. The main contributions of this paper are highlighted as follows:  
(1) For the C1 problem, we propose the A-ABSA model to gradually train the two tasks of aspect term extraction and aspect sentiment classification, to enhance the interaction between tasks, to improve the model performance simultaneously, and to solve the aspect level sentiment analysis problem end-to-end. (2) For the C2 problem, our aspect term extraction model integrates local context information by the equal-width convolutional neural network, aggregates global information and local information by the gating unit, enriches word vector information, and adds constraints to determine aspect term labels by Bi-LSTM and CRF to ensure the validity of terms. (3) For the C3 problem, the adaptive semantic relative distance is introduced in the aspect sentiment classification task to determine the appropriate local context for aspect terms in each text, accurately analyze the sentiment polarity of aspect terms, and exclude the influence of irrelevant words on the sentiment judgment of aspect terms. Figure 1: ABSA task procedure. (4) In the SemEval-2014 Task 4 restaurant and laptop dataset, the proposed A-ABSB model improved by 5.12% on the laptop domain dataset F1, with a comparable performance on the restaurant domain dataset regarding the aspect term extraction compared to other advanced methods. For the aspect sentiment polarity classification on the laptop domain dataset, the accuracy is improved by 1.11% and F1 is improved by 1.06%, the accuracy on the restaurant domain dataset is improved by 0.73% and F1 is improved by 1.11%.
# True Equalization of Polarization-Dependent Loss in Presence of Fast Rotation of SOP +
## 1 Introduction
Then, we analyze the combined effect of PDL and RSOP in an optical fiber. In Section 3, we simplify the polarization model, and based on the simplified model, we design a scheme that can truly equalize the combined impairments of PDL and ultra-fast RSOP, which enables the PDL compensation by the polarization-time code and RSOP tracking by the Kalman filter. The method performance is assessed in Section 4. At last, a conclusion is drawn in Section 5.
# Stability Assessment of Gob Side Entry at the Steeply Inclined Mining Face
## 5 Conclusions  
The 3308 track entry at Yangcheng Coal Mine serves as the project context. The mining depth, along with the significant inclination and thickness of the coal seam, results in a complex stress environment. The stability of surrounding rock is examined through numerical investigation. The conclusions are encapsulated as follows:  
1. Analyzing the pillar width in the context of a significant inclination angle. When the breadth of the coal pillar ranges from 3-6 m, the surrounding rock pressure is consistently asymmetric. The pillar and roof possess the primary reinforcement zone for surrounding rock stabilization. The displacement of the roadway exhibits an asymmetric characteristic relative to the roof, solid coal side, and pillar side. The  
Figure 11: Displacement monitoring result. (**a**) Section #1; (**b**) Section #2; (**c**) Section #3; (**d**) Section #4.  
plastic zone consistently exhibited an unequal distribution in its width. A thorough assessment of stress and plastic area parameters indicates that the optimal coal pillar width is 4 m.
2. Proposing control measures for the gob side entry. The proposed essential technology for surrounding rock control is based on the features of stress and the plasticity state. Utilizing high-stiffness (500 MPa) rock bolt, the size is \(\Phi\) 22 \(\times\) 2400 mm and the spacing measures 800 \(\times\) 800 mm. The stiffness of cable is 1860 MPa and the spacing measures 1600 \(\times\) 1600 mm. The size of roof cable and pillar cable is \(\Phi\) 21.8 \(\times\) 7400 mm and \(\Phi\) 21.8 \(\times\) 4500 mm, respectively. After 70 days of support application, the maximum displacement is measured on roof, with a reasonable value of 130 mm.  
Conceptualization, Z.Q.; Data curation, N.L.; Investigation, F.Z.; Methodology, Y.L.; Resources, Z.Q.; Validation, F.Z. and N.L.; Visualization, Y.L.; Writing--original draft, Y.L. All authors have read and agreed to the published version of the manuscript.  
This work is financially supported by the \"Natural Science Foundation of Shandong Province, China\" (Grant No. ZR2021 ME248).  
Not applicable.  
All data generated or analyzed during this study are included in this article or available from the corresponding author upon reasonable request.  
Yangcheng Coal Mine provides materials and places for the research, for which we express our gratitude.  
There are no conflicts in the contribution, data, or achievement of the authors of this manuscript.
# Efficient Optimization-Based Trajectory Planning for Truck-Trailer Systems
### Path-Based Trajectory Search  
Trajectory search based on a pre-existing path is most often used when the goal is to achieve a real-time implementation. This approach allows for the use of a local NLPsolver. Using a local solver provides faster solution-finding than a global solver. In the case of trajectories, additional requirements arise: the usual goal of moving a vehicle from location A to location B is complemented by the requirement of admissible trajectories for the system; furthermore, the vehicle following the found trajectories must avoid all obstacles. In the case of a truck with trailers, it is necessary to avoid the problem of the vehicle hitting its own trailer (jackknife effect).  
Existing papers have presented a large variety of solutions, with each team of authors approaching the problem individually; thus, it is not possible to clearly identify any one solution as the best. For example, in [2; 3] the authors decided to split the solution into several smaller optimization problems and link them together such that the trajectories found in the previous problem could be used as the initial estimate of the next problem. This approach allows for finding initial trajectories that do not respect collisions with obstacles, which can then be recomputed in the next optimization problem, which takes collisions with obstacles into account. If optimal trajectories still cannot be found, the next step is to change the size of the obstacles and run the optimization problem in a cycle until the desired collision-free trajectories are found.
# Polarimetric Detection of Chemotherapy-Induced Cancer Cell Death
## 3 Materials and Methods
### Samples Description  
The samples used in this experiment corresponded to two myelomonocytic leukemia cell lines, THP1 and U937, and a colorectal adenocarcinoma cell line, HT29, all provided by ATCC (American Type Culture Collection). The number of deposited cells was chosen so that a uniform layer of cells was achieved, without gaps or piles of cells. Cells were treated with 20 \(\upmu\)g/mL of cisplatin, a commonly-used chemotherapy drug that induces cell death. Cell viability at 4 different time points was determined by both the trypan blue exclusion test [37], which discriminates between live and dead cells, and the Alamar Blue assay [38], an indicator of cell metabolic activity. Both assays gave us robust information about the cellular health following the cytotoxic treatment. Then, the polarimetric measurements were carried out at 24, 48, and 72 h after the drug was applied. For each period of time, cells were centrifuged, resuspended in Phosphate-Buffered Saline (PBS), and placed on a microscope slide by applying a cytospin technique [39] at 500 r.p.m for 5 min (a high-speed centrifugation to remove the liquid and concentrate the cells on a slide in a layer of 6 mm in diameter). In order to maintain a humid environment for cells (so that they were stable over time) and to prevent the formation of salt crystals, cells were covered with 60 \(\upmu\)L of 0.3% agarose, which also kept the cells still during the measurement process.  
An important issue concerning the samples was the reduction of the surface covered by cells during the treatment. Because of this, as the treatment progressed, the polarimetric signal came from both the background (agarose) and the cells, according to the filling factor reached by the cells in the image, \(f_{c}\). In order to account for this effect, we introduced a corrected signal \(S_{c}\) that verified:  
\[S_{i}=S_{c}f_{c}+S_{b}(1-f_{c})\quad, \tag{12}\]  
where \(S_{i}\) is the total value of the polarimetric signal obtained in the measured image and \(S_{b}\) is the polarimetric signal coming from the \"empty\" background (agarose in our case). The filling factor was calculated for each image by converting them to 8 bit. Then, the image was binarized selecting a threshold that converted pixels occupied by a cell to black and pixels from the background to white (this was done with the free software ImageJ, and the result was double checked by eye inspection).
To date, there are numerous studies confirming their role in neutralizing reactive oxygen species (ROS), and thus indirectly affecting the activation of different signaling metabolic pathways, reducing oxidative damage of biological macromolecules, modulating gene expression, and, thereby, regulating redox status in the cells [28; 29]. However, a prooxidant character of polyphenols was also reported in terms of the ability to generate ROS in cell systems and cell-free media [30]. Compared to the dual character of some of the most investigated phenolic compounds, e.g., phenolic acids, flavonoids, and _Camelia sinensis_ extracts [31; 32], phenylethanolamine glycosides as relatively understudied phenolic compounds in terms of biological activity, shown to have a primarily protective role in suppressing the ROS formation [33], inhibiting the proliferation of cancer cells by downregulating the PI3K/AKT pathway [34], increasing the activity of endogenous antioxidant enzymes and inflammatory marker production [35], etc. It is a common misconception among consumers that the consumption of herbal remedies as a natural source of phytochemicals does not pose a health risk. Aside from expected adverse effects as the consequence of the numerous factors, such as inherent toxic substances in plants, misidentification of plant species, inappropriate doses, interactions with other drugs, and the presence of potentially hazardous substances, etc., limited knowledge of the appropriate preparation of herbal remedies and safe doses are other drawbacks related to the consumption of medicinal herbs [36]. Hence, expanding the literature base for herbal remedies in the context of their potential in provoking or disabling the free radical formation, inflammatory processes, cell apoptosis, and changes in DNA structure, etc., represents an important step for minimizing the long-term and dose-dependent toxicity. Since herbal preparations are traditionally consumed orally, cytotoxic, pro-oxidative, and genotoxic effects were investigated on the hepatocellular (HepG2), tongue (CAL 27), gastric (AGS), and colorectal (Caco-2) continuous human cancer cell lines, as well as the influence of oxidative potential on the cellular protein model and DNA plasmid. Additionally, the influence of Mountain Germander extract on _L. plantarum_, _E. coli_, and _S. aureus_ survival was determined. Results of this study could give an insight into the ability of Mountain Germander extract to influence cancer cell or bacterial strain survival, to act as an antioxidant and/or pro-oxidant, or to cause DNA damage.
## 5 Statistical Characterization of Seismic Activity
### Progression of Earthquake Activity  
For a comprehensive analysis of the temporal and seismic magnitude progression in the Red Sea zone, the final selected seismicity catalog was divided into four distinct time intervals and corresponding magnitude ranges. The temporal categorization was as follows: the period from 1997 to 2002, with an occurrence of 6238 seismic events; 2002 to 2008, encompassing 20,337 events; 2008 to 2014, reporting 10,120 events; and 2014 to 2020, recording 1035 events. In terms of magnitude, the segmentation was delineated into four ranges: a magnitude interval of 1.00-1.55, accounting for 16,750 events; 1.55-2.10, comprising 14,703 events; 2.10-2.65, capturing 4843 events; and 2.65-3.20, with a total of 1434 events. This bifurcation facilitated a granular analysis of seismic activity over time and across varying seismic intensities.
# Femtosecond Laser Textured Surfaces for Radiative Cooling: Black Metals
## 2 Experimental: Materials and Methods
### Structural and Spectrum Characterizations  
Surface structures were captured with scanning electron microscopy (SEM) using the RAITH 150 TWO electron beam lithography writer. The ablation volume and depth were measured using a Bruker Contour GT-K 3D Optical Profiler. The ablation volume was measured with the Bruker profiler built-in Vision64 software (version 5.41; [PERSON], 2013) and calculated as \(V=\int\!\!\int D(x,y)dA\), where \(D(x,y)\) is the local depth function over the measured region, and \(dA\) is the infinitesimal area element, the same as \((dx\,dy)\). The ablation depth is the average value of the depth (distance from the bottom of the ablated area to the xy-plane). The Bruker profiler has a vertical uncertainty of \(\pm 10-20\)  nm and lateral uncertainty of \(\pm 0.5\) \(\upmu\)m. The total error is estimated to be \(\pm 10\%\) considering the profiler's instrumental uncertainty and the systematic error of measurements. Fourier transform infrared (FTIR) spectra were measured using a BRUKER Hyperion 1000/2000 Fourier-transform infrared spectrometer equipped with a microscopy unit.
## 2 Notation and Background
### Homogeneous Spaces  
A homogeneous space is a particular type of quotient manifold that arises as a smooth manifold endowed with a transitive smooth action by a Lie group \(G\). The homogeneous space is called a \(G\)-homogeneous space to indicate the Lie group action. All \(G\)-homogeneous spaces arise as a quotient manifold \(G/H\), for some closed subgroup \(H\subseteq G\). \(H\) is a closed subgroup of the Lie group \(G\), which makes \(H\) a Lie group. Any homogeneous space is diffeomorphic to the quotient space \(G/G_{x}\), where \(G_{x}\) is the stabilizer for the point \(x\). The dimension of the \(G\)-homogeneous space is equal to \(\dim G-\dim H\); the quotient map \(\pi\colon G\to G/H\) is a smooth submersion, i.e., the differential of \(\pi\) is surjective at every point. This implies that the fibers \(\pi^{-1}(x)\), \(x\in M\), are embedded submanifolds of \(G\). We assume throughout that \(G\) acts on itself by left-multiplication.  
**Example 2**.: _The rotation group \(\mathrm{SO}(n)\) acts transitively on \(\mathbb{S}^{n-1}\); therefore, \(\mathbb{S}^{n-1}\) is an \(\mathrm{SO}(n)\)-homogeneous space. Consider a point in \(\mathbb{S}^{-1}\) as a vector in \(\mathbb{R}^{n}\). Rotations that fix the point occur precisely in the subspace orthogonal to the vector. Thus, the stabilizer or isotropy group is the rotation group \(\mathrm{SO}(n-1)\) and \(\mathbb{S}^{n-1}=\mathrm{SO}(n)/\mathrm{SO}(n-1)\). The set of invertible matrices with positive determinant \(\mathrm{GL}_{+}(n)\) acts on symmetric positive definite matrices \(\mathrm{SPD}(n)\). The isotropy group is the rotation group \(\mathrm{SO}(n)\), and thus, \(\mathrm{SPD}(n)=\mathrm{GL}_{+}(n)/\mathrm{SO}(n)\). A particular type of homogeneous space arises when the subgroup is a discrete subgroup of \(G\). For example, the space \(\mathbb{T}^{n}=\mathbb{R}^{n}/\mathbb{Z}^{n}\) defines the \(n\)-torus as a homogeneous space._
## 3 MLP Neural Network and Training
### Training for Fundamental Harmonic Detection
#### 3.2.2 Design, Training and Testing  
After many tests with different combinations of hidden layers and neurons per layer, the best configuration found was 50-10-10-2. That is, 50 input units, 2 hidden layers containing 10 neurons each, and the 2-neuron output layer. The transfer functions are tan-sigmoid in the hidden layers, and linear in the output one. These decisions were based on the authors' experience and on many trials, reducing the number of neurons as much as possible. This allows us to increase the number of patterns used in the learning process, without excessively long training times.  
Both the LM and the SCG backpropagation algorithms were used for training, but in general, the LM algorithm allowed us to achieve lower values of the goal _mse_ parameter. After a training of 100 epochs with LM, a value of _mse_ = 2\(\cdot\)10\({}^{-7}\) could be reached. The SCG method carried out thousands of epochs in the same time intervals, yet it did not reach the same low errors. So, the definitive ANN used was trained with LM. Concerning the conditions to end the last and definitive training, the key aspect is to observe the evolution of the _mse_ with the increase in the number of epochs. Once we knew that in different trials, the _mse_ value stopped decreasing after 80 or 90 epochs, taking values around 2\(\cdot\)10\({}^{-7}\), we set a number of 100 epochs as training limit, and run the last training.  
After training the ANN, the generalization capabilities and the accuracy of the outputs were checked. For these performance tests, we generated a high number of waveforms with random combination of the different harmonics and different fundamental frequency values. Many of these inputs included coefficients \(A_{i}\) and \(B_{i}\) exceeding the limits shown in Table 1. In addition, the fundamental frequency was used, exceeding the limits applied by the pattern generation. In particular, frequencies from 45 to 55 Hz were tested. The results of the generalization tests were satisfactory, proving that the ANN was accurate enough to be used in practical cases of three-phase systems.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Coefficients** & **Training** & **Coefficients** & **Training** \\  & **Intervals** & & **Intervals** \\ \hline A\({}_{1}\), B\({}_{1}\) & \([-1,1]\) & A\({}_{11}\), B\({}_{11}\) & \([-0.1,0.1]\) \\ A\({}_{3}\), B\({}_{3}\) & \([-0.6,0.6]\) & A\({}_{33}\), B\({}_{13}\) & \([-0.1,0.1]\) \\ A\({}_{5}\), B\({}_{5}\) & \([-0.2,0.2]\
###### Abstract  
Home area energy networks will be an essential part of the future Energy Internet in terms of energy saving, demand-side management and stability improvement of the distribution network, while an energy router will be the perfect choice to serve as an intelligent and multi-functional energy interface between the home area energy network and power grid. This paper elaborates on the design, analysis and implementation of coordinated control of the low-voltage energy router-based smart home energy management system (HEMS). The main contribution of this paper is to develop a novel solution to make the energy router technically feasible and practical for the HEMS to make full use of the renewable energy sources (RESs), while maintaining \"operational friendly and beneficial\" to the power grid. The behaviors of the energy router-based HEMS in correlation with the power grid are investigated, then the coordinated control scheme composed of a reference voltage and current compensation strategy and a fuzzy logic control-based power management strategy is developed. The system model is built on the MATLAB/Simulink platform, simulation results have demonstrated that the presented control scheme is a strong performer in making full use of the RES generations for the HEMS while maintaining the operational stability of the whole system, as well as in collaboration with the power grid to suppress the impact of RES output fluctuations and load consumption variations.  
Energy Internet; home energy management system; prosumer; energy router; fuzzy logic control
# An Integrated Adaptive Kalman Filter for High-Speed UAVs
## 3 Verification and Analysis
#### 3.1.1 Trajectory Simulation and Error Model  
The ideal flight simulation data was generated by the trajectory generator, which corresponded to the high maneuvering condition (acceleration of 4-100 g; angular velocity of 100-1000\({}^{\circ}\)/h). The whole process includes take-off at an acceleration of 50 m/s\({}^{2}\), climb at a pitch angular velocity of 35\({}^{\circ}\)/s, turning at a yaw angular velocity of 15\({}^{\circ}\)/s, descend at a pitch angular velocity of 30\({}^{\circ}\)/s, level flight, etc. The navigation frame is chosen as the E-N-U geography frame. The initial position is selected as 120\({}^{\circ}\) E, 30\({}^{\circ}\) N, and at an altitude of 1000 m. The simulation process lasts 450 s, and the configuration of sensors and initial errors information are showed in Table 1.  
\(b_{\alpha}=0.9\), \(\theta=0.01\), \(\mathrm{R_{max}}=diag(1,1,2,20/\mathrm{R_{e}},20/\mathrm{R_{e}},50)^{2}\), \(\rho=0.95\), \(\alpha=diag[1.3,1.3,\cdots 1.3]\). Here, \(R_{e}\) represents semimajor axis of ellipsoid.