esa-sceva/satcom-chunk-collection huggingface dataset:
# Employing Huber and TAP Losses to Improve Inter-SubNet in Speech Enhancement
## 3 Inter-SubNet with New Loss Function
### Huber Loss  
Huber loss [33], also termed smooth L1 loss, is a robust loss function used primarily in regression tasks, combining the properties of both L2 loss (mean squared error, MSE) and L1 loss (mean absolute error, MAE). It is expressed as follows:  
Figure 1: The flowchart of Inter-SubNet (using the MSE of clRM as the loss).  
Figure 2: The flowchart of the SubInter module in Inter-SubNet.  
\[\mathcal{L}_{Huber}(y,\hat{y})=\begin{cases}\frac{1}{2}(y-\tilde{y})^{2}&\text{ for }|y-\tilde{y}|\leq\Delta\\ \Delta\cdot\left(|y-\tilde{y}|-\frac{1}{2}\Delta\right)&\text{ otherwise}\end{cases}, \tag{7}\]  
where \(y\) and \(\tilde{y}\) are the estimated and ground-truth values, respectively, and \(\Delta\) is a threshold parameter that determines the transition point between the quadratic and linear loss. For errors smaller than \(\Delta\), Huber loss behaves like L2 loss, providing smoothness and differentiability, which is advantageous for optimization. For larger errors, it transitions to L1 loss, which is less sensitive to outliers.  
One of the primary advantages of Huber loss over L2 (MSE) loss is its robustness to outliers, while MSE can disproportionately penalize large errors due to its quadratic nature, leading to skewed results in the presence of outliers, Huber loss mitigates this effect by treating large errors linearly beyond the threshold \(\Delta\). As a result, Huber loss provides a balanced approach to error measurement, combining the strengths of both L1 and L2 losses while reducing the impact of outliers, making it a preferred choice in many regression tasks.
# FLATSIM: The ForM@Ter LArge-Scale Multi-Temporal Sentinel-1 InterferoMetry Service
## 5 Results and Products Qualification
### Discussion of Results in the Afar Area  
In Afar, beside the three quality indicator maps shown in Figure 6, the quality of the results can be discussed through a comparison of the velocity maps along overlap areas, and through the analysis of the various observed surface deformation signals and their temporal behavior. The accuracy of the signals detected in the final mean velocity maps can be tested by comparing the results in the overlap region between the two adjacent descending tracks D079 and D006 (Figure 17, right). Despite different incidence angles (\(\approx\)44\({}^{\circ}\) for track D079 versus \(\approx\)36\({}^{\circ}\) for track D006), the excellent agreement indicates the reliability of the resulting velocity maps across a broad range of spatial wavelengths. Furthermore, we can detect signals that arise from different geophysical processes involving magmatic or tectonic drivers (Figure 17, left). Relatively strong deformation rates due to magmatic deflation or inflation characterize different volcanoes (e.g., Nabro, dabbahu, Erta 'ale). Additionally, a tenuous extension signal is visible across the Asal rift, as previously shown thanks to Radarsat InSAR timeseries [10].  
Figure 16: Proxy for temporal coherence, merged for all tracks that have been processed in eastern Tibet. (**a**) Global map. (**b**) Zoom on the rectangle shown in (**a**). (**c**) Associated optical image. The red arrow points toward a large geometrical field of stabilized sand dunes. The blue arrow shows how dirt roads increase the coherence in desert sand dunes.  
The time-series quality is highlighted by extracting the displacement history for a number of selected pixels (Figure 18). Three geophysical phenomena, with contrasting temporal signatures, are shown here:  
* Following a major rifting episode that took place between 2005 and 2011 [55], the Dabbahu volcano (located North West of Manda Hararo in Figure 18) has experienced a steady re-inflation, inducing uplift at a rate of \(\approx\)3.5 cm/yr. The inflation is remarkably constant between 2014 and 2021, confirming the findings of [56];
* In contrast, the volcano Erta 'Ale has shown a transient deflation that started in January 2017 following a lava lake overflow event, that produced long-lived lava flows [57];
* Finally, a very small signal is detected in the Dergaha graben, coinciding with a cluster of seismicity detected in 2008 [58]. The time-series shows a clear step around March 2018, which corresponds well with the time of occurrence of the largest event of the seismic sequence.  
Note that no low-pass filtering nor model adjustment have been applied on the time series presented here. They can therefore be safely interpreted, and uncertainties on the geophysical interpretation can be assessed using the dispersion of the time series.  
Figure 17: Mean velocity map for track D079 and D006 showing a number of geophysical signals.
## 3 Results  
### Verification of Quaternion and Angle Reliability  
Table 3 shows the results of one experiment for the first type of verification test mentioned in Section 2.4. The mean absolute error of 30 samples is 1.195 degrees for this experiment. The angles of the motor forward and backward passing 180 (samples #10 and #20) are different, which may be caused by the system device itself (motor gear gap). A total of 31 experiments were conducted. Table 4 shows the mean absolute error of each experiment, and the average mean absolute error of 1.485 degrees was obtained. Therefore, this proves the quaternion values obtained from the nine-axis sensor are within an acceptable engineering criterion.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Degree**} & \multicolumn{3}{c}{**Degree**} \\ \hline \multirow{2}{*}{**Sample \#**} & \multirow{2}{*}{\(\theta_{motor}\)} & \multirow{2}{*}{\(\theta_{imu}\)} & **Absolute** & \multirow{2}{*}{**Sample \#**} & \multirow{2}{*}{\(\theta_{motor}\)} & \multirow{2}{*}{\(\theta_{imu}\)} & **Absolute** \\  & & & & & & & & \\ \hline
1 & 18 & 17.22 & 0.78 & 16 & 252 & 253.37 & 1.37 \\ \hline
2 & 36 & 35.61 & 0.39 & 17 & 234 & 235.8 & 1.8 \\ \hline
3 & 54 & 52.39 & 1.61 & 18 & 216 & 217.98 & 1.98 \\
# Experimental Study of a Signal Modulation Method to Improve eLORAN Data Channel Communications
## 4 Discussion  
The realization and application of eLORAN system data channels technology can expand the application range of eLORAN system and build a relatively perfect PNT system. However, the existing demodulation method used in the eLORAN system cannot effectively suppress noise and interference. Therefore, with the development of modern eLORAN systems, a more advanced eLORAN signal modulation processing capability is required.  
In order to adapt to the complex signal propagation environment in inland areas, increase the success rate of signal demodulation, and reduce the demodulation threshold of the receiver, this article proposed a new modulation method for eLORAN data channels, APM modulation, without changing the existing eLORAN system, eight pulses are added after the existing pulse set, and the presence or absence of pulses represents 1 and 0 in binary, similar to On-Off Key modulation. Therefore, the APM technology has a very low implementation complexity, and the performance of APM modulation with the prevailing 3S-PPM and ninth pulse modulations was compared. Theoretical analysis results show that the demodulation success rate of the APM signal modulation scheme is higher in the case of weak signals, that is, the signal field strength is small, and there is interference.
## 2 Materials and Methods
### Computational Complexity and Comparison with Other Methods  
The computational complexity of the proposed algorithm depends on the number of decisions an agent performs during each step, which is equal to the number of outputs of brain network \(\#C\), number of semantic classes (objects types) \(\#S\), input image resolution \(\#I\) and number of observations we acquired to generate CART \(\#obs\). The complexity of GradCAM depends on the resolution of image \(I\), \(\#I\), and equals \(O(\#I)\). Then, for each semantic class in \(S\), we count all pixels in the image segment that belong to class \(s\in S\). That operation has complexity \(O(\#S\cdot\#I)\). Suppose the proxy geometry has a size equal to \(9\), which is the highest possible value. Due to this, there are \(9\cdot\#S\) features. The computational complexity of creating CART is \(O(9\cdot\#S\cdot\#obs\cdot log_{2}(\#obs))\approx O(\#S\cdot\#obslog_{2}( \#obs))\) and we have to create a CART tree for each network output \(\#C\). Finally, the computational complexity of our approach is \(O(\#C\cdot(\#I+\#S\cdot\#I+\#S\cdot\#obs\cdot log_{2}(\#obs)))\approx O(\#C \cdot\#S\cdot(\#I+\#obs\cdot log_{2}(\#obs)))\).  
As can be seen, the computational complexity of our approach does not exceed the computational complexity of generating CART \(\#C\) times, provided that \(\#I\ll\#obs\); if \(\#obs\ll\#I\), it becomes polynomial. This complexity allows a relatively fast generation of approximation of explained models.  
Our approach utilizes decision tree formalism, which makes it similar to policy explanation approaches [38; 39; 40]. However, that method cannot be directly applied to visual data to produce explainable (understandable to the user) descriptions due to the high dimensionality of images. Similarly to visualization techniques, we also apply a GradCAM [43; 44]; however, the explanation is not only based on visual feedback but also generates rules in the form of CART that govern the behavior of an agent. Our solution also uses a knowledge distillation approach [46] in which a black-box brain provides training data (feature-based knowledge) for its easy-to-interpreted approximation. To summarize, our method incorporates several elements from already established approaches and creates a single, easy-to-interpreted tree-based solution, thanks to which it can be applied in the domain of explaining the action of an agent collecting visual data, which none of the above methods can achieve individually.
###### Abstract  
The impact of the Coriolis force on the long distance wake behind wind farms is investigated using Large Eddy Simulations (LES) combined with a Forced Boundary Layer (FBL) technique. When using the FBL technique any mean wind shear and turbulent fluctuations can be added with body forces. The wind shear can also include the mean wind veer due to the Coriolis force. The variation of the Coriolis force due to local deviations from the mean profile, e.g., from wakes, is not taken into account in the FBL. This can be corrected for with an extra source term in the equations, hereon defined as the Coriolis correction. For a row of 4 turbines it is shown that the inclusion of the wind veer turns the wake to the right, while including the Coriolis correction turns it to the left. When including both wind veer and Coriolis correction the impact of wind veer dominates. For an idealized farm to farm interaction case, two farms of \(4*4\) turbines with 6 km in between, it can be seen that when including wind veer and the Coriolis correction a approximately 3% increase in the relative production for a full wake direction can be seen and only a slightly smaller increase can be seen when including only wind veer. The results indicate that FBL can be used for studies of long distance wakes without including a Coriolis correction but efforts need to be taken to use a wind shear with a correct mean wind veer.  
long distance wake; farm to farm interaction; wind farm cluster; large Eddy simulations; LES; actuator disc; ACD; forced boundary layer; FBL; coriolis; wind veer +
Footnote †: journal: Applied Sciences
# PrivacyGLUE: A Benchmark Dataset for General Language Understanding in Privacy Policies
## 4 Experimental Setup
### Task-Specific Approaches  
Given the aforementioned models and tasks, we now describe our task-specific fine-tuning and evaluation approaches. Given an input sequence \(s=\{w_{1},w_{2},\ldots,w_{N}\}\) consisting of \(N\) sequential sub-word tokens, we feed \(s\) into a transformer encoder and obtain a contextual representation \(\{h_{0},h_{1},\ldots,h_{N}\}\) where \(h_{i}\in\mathbb{R}^{D}\) and \(D\) is the output dimensionality of the transformer encoder. Here, \(h_{0}\) refers to the contextual embedding for the starting token which is [CLS] for BERT-derived models and 
 for RoBERTa-derived models. For PolicyQA and PrivacyQA, the input sequence \(s\) is composed by concatenating the question and context/answer pairs, respectively. The concatenated sequences are separated by a separator token, which is [SEP] for BERT-derived models and 
 for RoBERTa-derived models.
# Visual-Based Localization Using Pictorial Planar Objects in Indoor Environment
## 1 Introduction  
Device localization is an important technology for future distributed indoor IoT applications with autonomous moving devices, such as industrial robot, intelligent hotel assistant, and smart office courier. Next generation smart services must be location-based and self-location-aware. For example, smart movers in large warehouses can help workers collect customer orders from different shelves into one shipment package. Mobile hospitality assistants can lead customers in different corners of big hotels or shopping centers to specific locations or give directions. Autonomous moving couriers can be used in offices to deliver documents and supplies without human contacts. Many distributed devices can be deployed together to provide a coordinated smart service network.  
Visual-based localization (VBL) [1, 2] is one of the promising self-localization technologies that have received a growing interest. VBL identifies a device's location in a target space by using its camera to see the device's surroundings, without the dependency on GPS which is designed for outdoor usage and also subject to signal bouncing and interference. Therefore, VBL is especially attractive for devices that must work indoor, underground, or in an enclosed space. Moreover, with the advancement and popularity of low-cost, low-energy, high-quality cameras, VBL has become very attractive and easy toadopt. Many indoor devices and smart equipments can be equipped with small cameras and instantly become location-aware by connecting them to servers for VBL processing.
## 2 Materials and Methods
### Methods
#### 2.3.4 Lag Analysis Method  
The optimal time lag of GPP with respect to climate factors is determined by calculating the maximum lag coefficient through partial correlation analysis. Partial correlation analysis is a method used to measure the degree of correlation between two random variables, while controlling for the influence of a set of control variables [42]. This type of analysis helps uncover spurious correlations and reveal hidden relationships. The past experience has shown that the time lag of vegetation response to climate at the monthly scale is typically less than a quarter of the full year [43; 44]. Therefore, this study selected a time-lag range of 0-3 months to investigate the response of GPP to climate factors, expressed as follows:  
\[\begin{array}{l}r_{1}(k_{1})=\max(r_{k}(x,y))\\ r_{2}(k_{2})=\min(r_{k}(x,y))\\ \left\{\begin{array}{l}R=r_{1},K=k_{1}|r_{1}|>|r_{2}|\\ R=null,K=null|r_{1}|=|r_{2}|\\ R=r_{2},K=k_{2}|r_{1}|<|r_{2}|\end{array}\right.\end{array} \tag{4}\]  
where \(r_{1}\) and \(r_{2}\) represent the maximum and minimum correlation coefficients of the GPP with climate variables, respectively; and \(R\) and \(K\) represent the optimal correlation coefficient and time lag of the GPP response to climate variables.
## 4 Conclusions  
Based on the results of an ab initio study, the following conclusions can be drawn. The proposed atomic configurations of van der Waals borophene/ReS\({}_{2}\) and borophene/ReSe\({}_{2}\) heterostructures are energetically stable and characterized by a metallic type of conductivity. The synergistic effect of the combination of a triangulated borophene monolayer and ReS\({}_{2}\)/ReSe\({}_{2}\) monolayers leads to an increased sensitivity of borophene/ReS\({}_{2}\) and borophene/ReSe\({}_{2}\) heterostructures to radiation in the UV range, which makes them potentially promising materials for the development of UV detectors in the near- and far-UV ranges. It is predicted that the efficiency of such detectors will be high, since both heterostructures are practically insensitive to other wavelengths. Based on the calculated photocurrent spectra in the wavelength range from 200 to 2000 nm, it was predicted that the borophene/ReS\({}_{2}\) and borophene/ReSe\({}_{2}\) heterostructures have a pronounced ability to generate a photocurrent in the visible wavelength range. It was shown that the integral value of the photocurrent in the visible radiation range of borophene/rhenium sulfide and borophene/rhenium selenide heterostructures is many times higher than the indices of monolayers of graphene and borophene \(\beta_{12}\), and correlates well with the data of full-scale experiments with other layered vertical 2D structures. At the same time, for the effective use of borophene/rhenium sulfide and borophene/rhenium selenide heterostructures as a material for creating solar cells, it is necessary to solve the problem of opening a band gap in their band structure. This task is the subject of further research.  
Conceptualization, M.M.S. and O.E.G.; methodology, M.M.S. and O.E.G.; funding acquisition, M.M.S.; investigation, O.E.G., D.A.K. and M.M.S.; writing--original draft preparation, D.A.K. and M.M.S.; writing--review and editing, O.E.G. All authors have read and agreed to the published version of the manuscript.  
The research was funded by Russian Science Foundation, grant number 21-72-00082.  
Not applicable.  
The authors declare no conflict of interest.
# Comparison of Nonlinear and Linear Controllers for Magnetic Levitation System
## 3 Control Design  
A nonlinear system control design frequently uses linearization techniques, which provide powerful tools to simplify analysis and design. The main idea behind linearization approaches is to apply appropriate transformation to the nonlinear system so that the simplified--linearized model is obtained, for which some of many existing control design methods can be used.  
In this section, two different approaches to a nonlinear system control design are presented. The first one is based on the classic linearization approach using Taylor series (Jacobian) around the appointed working point. To guarantee stability around the working point, the resulting linearized model is then represented as a polytopic uncertain system. For such system model, we recall a robust pole region approach introduced in [9] and briefly summarize our recent results for a discrete-time domain [18; 19]. Robust pole-placement control is designed directly in a discrete-time domain since control implementation uses digital controller. In the second part of this section a nonlinear continuous time control law motivated by [22] and based on feedback linearization is developed for magnetic levitation. Presented result further develops nonlinear control law from [22] and uses approximation of lumped exponential terms by a rational function to simplify the resulting control law. The main motivation is to compare these two approaches and assess the qualities of recently developed, relatively simple robust pole-placement control [19].  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameters** & **Values** & **Units** \\ \hline \(m\) & 0.016; 0.023; 0.039 & kg \\ \(g\) & 9.81 & m/s\({}^{2}\) \\ \(F_{emP1}\) & 0.017521 & H \\ \(F_{emP2}\) & 0.0058231 & m \\ \(f_{1P1}\) & 1.4142 \(\times\) 10\({}^{-4}\) & ms \\ \(f_{1P2}\) & 4.5626 \(\times\) 10\({}^{-3}\) & m \\ \(c_{1}\) & \(-\)0.4 & A \\ \(k_{1}\) & 4.4 & A \\ \(i_{MIN}\) & 0.03884 & A \\ \(i_{MAX}\) & 2.38 & A \\ \(u_{MIN}\) & 0.00498 & MU \\ \hline \hline \end{tabular}
\end{table}
Table 1: ML plant parameters used in nonlinear state model.
###### Abstract  
The traditional centralized optimization method encounters challenges in representing the interaction among multi-agents and cannot consider the interests of each agent. In traditional low-carbon scheduling, the fixed carbon quota trading price can easily cause arbitrage behavior of the trading subject, and the carbon reduction effect is poor. This paper proposes a two-layer dynamic community integrated energy system (CIES) low-carbon collaborative optimization operation method. Firstly, a multi-agent stage feedback carbon trading model is proposed, which calculates carbon trading costs in stages and introduces feedback factors to reduce carbon emissions indirectly. Secondly, a two-layer CIES low-carbon optimal scheduling model is constructed. The upper energy seller (ES) sets energy prices. The lower layer is the combined cooling, heating, and power (CCHP) system and load aggregator (LA), which is responsible for energy output and consumption. The energy supply and consumption are determined according to the ES energy price strategy, which reversely affects the energy quotation. Then, the non-dominated sorting genetic algorithm embedded with quadratic programming is utilized to solve the established scheduling model, which reduces the difficulty and improves the solving efficiency. Finally, the simulation results under the actual CIES example show that compared with the traditional centralized scheduling method, the total carbon emission of the proposed method is reduced by 16.34%, which can improve the income of each subject and make the energy supply lower carbon economy.  
integrated energy system; optimized operation; pricing strategy; low-carbon scheduling
###### Abstract  
In this paper, an optimized kinematic modeling method to accurately describe the actual structure of a mobile manipulator robot with a manipulator similar to the universal robot (UR5) is developed, and an improved self-collision detection technology realized for improving the description accuracy of each component and reducing the time required for approximating the whole robot is introduced. As the primary foundation for trajectory tracking and automatic navigation, the kinematic modeling technology of the mobile manipulator has been the subject of much interest and research for many years. However, the kinematic model established by various methods is different from the actual physical model due to the fact that researchers have mainly focused on the relationship between driving joints and the end positions while ignoring the physical structure. To improve the accuracy of the kinematic model, we present a kinematic modeling method with the addition of key points and coordinate systems to some components that failed to model the physical structure based on the classical method. Moreover, self-collision detection is also a primary problem for successfully completing the specified task of the mobile manipulator. In traditional self-collision detection technology, the description of each approximation is determined by the spatial transformation of each corresponding component in the mobile manipulator robot. Unlike the traditional technology, each approximation in the paper is directly established by the physical structure used in the kinematic modeling method, which significantly reduces the complicated analysis and shortens the required time. The numerical simulations prove that the kinematic model with the addition of key point technology is similar to the actual structure of mobile manipulator robots, and the self-collision detection technology proposed in the article effectively improves the performance of self-collision detection. Additionally, the experimental results prove that the kinematic modeling method and self-collision detection technology outlined in this paper can optimize the inverse kinematics solution.  
mobile manipulator robot; kinematic modeling; self-collision detection; actual physical structure