esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Areas and Survey Conditions  
Six assorted snow-covered areas were mapped to produce SDEMs during late-winter conditions in April 2015. The surveyed areas differed in terms of snow surface pattern type, light conditions, and underlying topography complexity. The areas were resurveyed in July 2015 during summer conditions to provide TDEMs. The geographical location of the sites is shown in Figure 1, with conditions at each site summarized in Table 1. Snow surface type, luminance conditions, and topography complexity were qualitatively defined during all surveys (examples shown in Figure 2a-e).
# UPGCN: User Perception-Guided Graph Convolutional Network for Multimodal Recommendation
## 4 Experiments
### Hyperparameter Sensitivity Study  
#### 4.7.1 Effects of the Fusion Weight \(\lambda_{\text{reg}}\)  
In our multimodal enhanced fusion method, the first enhancement utilizes dimensionally transformed modal features and item modal features that contain user modal preference information as guiding signals to extract the parts of item features that users particularly focus on. Subsequently, the enhanced item representations across different modalities are fused. Before the second fusion, the modal features undergo self-enhancement to highlight the single-modal features, thus distinguishing the similarities between different modalities and removing noise. Then, using Equation (18), the two enhanced results, \(\overline{e}_{i}\) and \(\overline{h}_{i}\), are fused to fully leverage the complementarity of these two outcomes. Theoretically, this linear combination helps the model make full use of both types of information, thereby improving recommendation performance. As shown in Figure 3, in two datasets, by observing changes in Recall@20 and NDCG@20, when \(\lambda_{i}=0.45\), both Recall@20 and NDCG@20 reached their maximum values. This suggests that in the Baby and Sports datasets, a moderate fusion weight can find the optimal balance between the two types of information, thereby maximizing recommendation performance. However, when \(\lambda_{i}=0.35\) or \(\lambda_{i}=0.55\), both Recall@20 and NDCG@20 slightly decreased, indicating that excessively high or low weights lead to reduced recommendation performance, indirectly suggesting that the two types of extracted information positively contribute to improving recommendation performance.
# Binaural Rendering with Measured Room Responses: First-Order Ambisonic Microphone vs. Dummy Head
## 2 Experiment I: Dynamic Rendering of Multiple-Orientation Binaural Signals
### Listening Experiment: Implementation and Settings  
The real-time implementation of the LISHPh, as well as the broadband linear interpolation, was done in _pure data_ ([[https://puredata.info/](https://puredata.info/)]([https://puredata.info/](https://puredata.info/))), an open source real-time audio software. Appendix A describes the example implementation provided online ([[https://phaidra.kug.ac.at/o:97087](https://phaidra.kug.ac.at/o:97087)]([https://phaidra.kug.ac.at/o:97087](https://phaidra.kug.ac.at/o:97087))).  
Block processingIn the short-time block processing with block size \(N\) and hop size \(L=N/2\) of the high-frequency part, a sine half-wave window is applied at both the analysis and synthesis stage to reduce cyclic artifacts at the block boundaries. As found in [20] and as the optimum for broadband musical sounds, it is crucial to keep the block size low to avoid temporal artifacts and to obtain low latency. Thus, we suggest setting \(N=128\) at a sampling rate of 44.1 kHz. For high-frequency phase selection, we used an update rate of 200 Hz; see the block labeled as _Selector_ in the diagram of Figure 2a.  
CrossoverWe estimate the spectral ripple of two interfering signals with phase offsets by \(\frac{1}{2}[e^{\frac{1}{2}}+e^{-i\frac{\phi}{2}}]=\cos\frac{\phi}{2}\). To keep the spectral ripple below 3 dB, we require a phase difference \(\phi<\frac{\pi}{2}\), or \(\phi<\frac{\pi}{4}\) to keep it below 0.7 dB, and hence spectrally inaudible [26]. With Equation (2) or a rule of thumb \(f_{max}\approx 2\,\mathrm{kHz}\frac{5\sqrt{3}\,\odot^{\phi}}{\Delta\varphi}\), the phase difference is \(\phi=\frac{\pi\,f}{f_{max}}\) in our case. Spectrally, good results are achieved with a crossover frequency \(f_{c}\approx\frac{f_{max}}{4}\). However, setting it too low, e.g., \(f_{c}<1.5\)kHz, impedes interaural phase cues in a relevant frequency range. Accordingly, the choice  
\[f_{c}(\Delta\phi) =2^{\mu}\,f_{max}(\Delta\phi),\] (4) with \[u =\begin{cases}-2,\text{ if }\Delta\varphi<30^{\circ}\\ -1,\text{ if }\Delta\varphi\geq 30^{\circ}\end{cases}\]  
offers a reasonable trade-off, cf. [20]. For \(\Delta\phi=\{8,15,30,60\}^{\circ}\), we obtain the crossovers at \(f_{c}=[4,2,2,1]\) kHz. The crossover is implemented by 4 th-order Linkwitz-Riley filters because of their in-phase sub bands [27, 28].  
For playback, we used AKG K702 headphones equipped with the IEM headtracker [8] and the experiment was conducted in a quiet office room.
## 4 Numerical Examples and Illustrations
### Bifurcation and Stability Analysis
#### 4.2.1 In-Plane Excitations
From a non-linear dynamics perspective, it can be observed that periodic motions bifurcate into chaotic ones as the temperature increases from \(-40\)\({}^{\circ}\)C to \(40\)\({}^{\circ}\)C, emphasizing the significant impact of temperature on the system's non-linear behavior. Dynamic chaos and oscillations are an essential undesired non-linear phenomena in cable structures, and they may induce undesired destructions of the discomfort of the non-linear system. Here, the temperature changes become the key factor, and the chaos may be excited in different temperature conditions. Figure 7 illustrates that the non-linear system exhibits chaotic motions in both warming and cooling conditions, with similarities observed in their phase plane diagrams and chaotic attractors. However, a significant difference is observed in the response amplitudes, which are reduced in the warming condition. These observations highlight the sensitivity of chaotic motions to temperature changes from a non-linear dynamics perspective.
# A Completion Method for Missing Concrete Dam Deformation Monitoring Data Pieces
## 2 Method
### Completion Strategy for a Single Missing Value
#### 2.1.2 Single-Value Missing Data Completion Based on NLM (Non-local Means) Method
Secondly, the deformation value in the deformation time series of point B and the interpolation point in the sequence of point A at the same time can be referred to as the hypothesis interpolation point, and the weight of the deformation value of this hypothesis interpolation point at other points in the sequence of point B may be calculated. In this paper, the Square of Euclidean Distance (SED) is used to measure the similarity of deformation values at different times. The formula for calculating the square of Euclidean distance is:  
\[d_{ij}(SED)=\left(\delta_{i}-\delta_{j}\right)^{2} \tag{19}\]  
where \(\delta_{i}\) and \(\delta_{j}\) represent the deformation values corresponding to the measuring points at time \(i\) and \(j\). In general, the smaller the difference is between the deformation values at different moments \(\delta_{t_{1}}\) and \(\delta_{t_{j}}\), the more similar the deformation is at the two moments, and the largerthe weight value is given in the calculation. The weight is calculated by the following formula:  
\[w(i,j)=\exp\left(-\frac{d_{ij}(SED)}{h^{2}}\right) \tag{20}\]  
where \(h\) is the parameter that controls the increased or decreased speed of the exponential function and determines the weight. Finally, calculate the weight of each reference point relative to the assumed interpolation point based on the complete deformation sequence of point B, and assign the deformation value to the measuring point A at the corresponding time. Then, the value of the interpolation point can be calculated by a weighted average. The formula is:  
\[\delta_{i}=\frac{\sum\limits_{j\in I}w(i,j)\delta_{j}}{\sum\limits_{j\in I}w( i,j)} \tag{21}\]  
where \(I\) represents the set of moments of the selected entire time series. Assume the deformation time series of measuring points A and B are shown in Figure 1, where the sequence of measuring point B is complete, and there is a missing spot in the sequence of measuring point A. The dots in the figure represent the corresponding deformation values at different times, and the square point represents the missing value in the sequence of measuring points A, namely the interpolation point to be solved. Point 1 in Figure 1 is a hypothetical interpolation point. Considering the similarity between other points and point 1 in the sequence, the deformation values of points 2, 3, and 4 are the same as that of point 1. According to the definition of Euclidean distance square, the weight of points 2, 3, and 4 is 1, and the closer the value is to the value of point 1, the larger the weight value is assigned. Through traversing the whole time series, the weight value of all points can be obtained. The weight value of each point in the B sequence is assigned to the corresponding point (points at the same time) in the A sequence, and the value of the interpolation points can be obtained after weighted averaging.
# Optimal Coordination of PV Active Power Curtailment and EVs Charging among Aggregators
## 3 Centralised Optimisation by the DSO for Multiple Aggregators
### Aggregators Constraints  
Following the above linear objective function, constraint (12) ensures that the absolute value of the net power of all aggregators should not exceed the rated capacity of the distribution transformer \(j\) in kW, i.e., \(\textit{\textit{\textit{\textit{\textit{\textit{\textit{\textit{\textit{\textit{ \textit{\textit{\textit{\textit{\textit{\textit{\textit{\textit{\textit{\textit{\textit{ \textit{    }}}}}}}}}}}}}}}}\cdot\cos(\varphi)\). Nevertheless, the distribution transformer could be subjected to overload conditions in case of a considerable charging demand or reverse power flow derived from the EVs and PVs of each aggregator. Similarly, constraint (13) imposes the absolute value of the maximum power flow through the main cable of the feeder managed by the \(k\)th aggregator, which can also be subjected to temporary overloads. In order to handle these situations, those constraints are relaxed by introducing the penalty deviation variable \(OL_{k,t}\), as mentioned before. By adding these two nonlinear constraints (because of the absolute value function), the linear programming (LP) problem becomes a mixed-integer linear programming (MILP) model after linearising them. Equation (14) constrains that the optimal charging power for the \(k\)th aggregator at every time step should not be larger than the maximum power allowed on the feeder's main cable, i.e., the minimum value between the upper power boundary of the \(k\)th aggregator \((P_{k,t}^{upper})\), and the difference between the rated capacity of the main cable \((P_{\textit{Line}_{k}}^{rated}=\textit{\textit{\textit{\textit{\textit{ \textit{\textit{\textit{\textit{\textit{\textit{\textit{\textit{\textit{\textit{ \textit{\textit{\textit{\textit{ \textit{      \textit{      }}}}}}}}}}}}}} \cos(\varphi))\) and the forecasted power demand \((P_{\textit{Line}_{k,t}}^{fc})\) on such a feeder. Notice that this limit of power can eventually be violated by a massive charging demand for a given period because of use of \(OL_{k,t}\). Constraint (15) imposes that the aggregated rate of charge \((\Delta_{k,t}^{P})\) of the aggregator \(k\) should not display significant variations in power over consecutive time steps in order to limit the thermal stress in the main cable of its feeder. As the current charging level is compared to the one at the previous time step, this constraint is defined for all intervals where \(P_{k,t}^{upper}\) is different from zero. Constraint (16) ensures that the optimal charging power of each aggregator satisfies their forecasted cumulative energy boundaries. Note that the complexity of this optimisation problem is mainly given by the number of aggregators and the length of the evaluation period \((N_{\textit{slots}})\), which makes the proposed problem easily solved. After solving this convex optimisation model, the DSO sends the optimal charging profile to each aggregator for further intelligent dispatching.
## 2 Materials and Methods
### Jointly Fine-Tuning of Two DNNs
#### 2.5.1 By Amplified Features (JFN-A)  
In this section, we propose a jointly fine-tuned model that is based on the amplification of the features that are resulted from the element-wise summation of the last hidden layers of the two DNNs with two different feature sets. The input features of the first DNN are the facial features which were extracted from pre-trained model on face recognition, while the input features of the second DNN are the superpixels and their relations, which were extracted from a pre-trained model for depth estimation.  
As shown in Figure 3, the proposed JFN-A consists of three parts. The first and second parts are two supervised DNNs, and the third part is a supervised neural network that jointly fine-tunes the first and second parts. The first and second parts have two hidden layers and one output layer. The third part has one input layer, one hidden layer, and one output layer. There are eight labels in the output layer. The input of the third part is the element-wise summation of the last hidden layers of the first and second parts.  
The learning process of the JFN-A is explained as follows:  
1. Three loss functions are used to train the three networks. All three loss functions are the softmax cross entropy function, as in (11). \[L_{i}=-\sum_{j=1}^{c}y_{j}\log\left(\overline{y}_{i,j}\right)\] (11) \(L_{i}\) is the loss function of the network \(i\), \(y_{j}\) is the \(j\)th value of the label, and \(\overline{y}_{i,j}\) is the \(j\)th output value of the network \(i\).
2. The first and second parts of the model are trained by using first batch of their corresponding features. Then, the outputs of the last hidden layer of both networks are element-wise summed to form the input of the third part, as in Equation (12). \[x_{3,j}=\ Relu\left(l_{1,j}\right)+\ Relu\left(l_{2,j}\right)\] (12) \(x_{3,j}\) is the input of the third part, \(l_{1,j}\) and \(l_{2,j}\) are the outputs of the last hidden layer of the first and second parts, and \(Relu\) is the rectified activation function.
3. Feedforwarding, error calculation, and backpropagation are performed on the third part.
4. The steps from 1 to 3 are repeated for the rest of the training batches.
5. After the training is complete, the softmax output of the third part, \(\overline{s}\), is obtained as the final decision, as in Equation (13). \[\overline{s}=\arg max_{j}\ \overline{y}_{3,j}\] (13)
# Equivalent Dynamic Analysis of a Cable-Driven Snake Arm Maintainer
## 1 Introduction
[PERSON] [26] used the direct method and the superpositioning method to analyze the mode and dynamics of SAM to determine its vibration shape and natural frequency. Meanwhile, [PERSON] et al. [27] adopted the Gibbs-Appell method to obtain the dynamic equations of the in-plane motion and simplify the dynamics of SAM. [PERSON] et al. [28] used a multiobjective particle swarm optimization method to simultaneously optimize the energy and control accuracy indicators in the SAM trajectory-tracking process. [PERSON] et al. [29] proposed a dynamic control strategy using multilevel mapping for motion analysis and compensation. They calculated the feedforward torque of the SAM motor using recursive dynamics and the \"rope pull-motor torque\" relationship. [PERSON] et al. [30] investigated the effect of different parameters on the vibration of a dynamic model by controlling for variables. [PERSON] et al. [31] proposed a virtual force feedback algorithm to improve SAMs' perception and assistance capabilities in remote operations. [PERSON] et al. [32] applied the perturbation method to a dynamic model of SAM, derived a vibration control  
Figure 1: SAM operation process. (**a**) SAM installs the end of the CMOR into the vacuum chamber; (**b**) maintenance inspection of the divertor. equation, and studied the natural frequency value of the cable when it was elastic. Due to the strong coupling between the joints, the end effectors, and the drive motors of the SAM, it can prove difficult to obtain the attendant mapping relationships directly. As such, it is difficult to establish an accurate dynamic model to calculate the driving force of each drive cable in real time, especially when the arm length and degree of freedom increase. In this paper, an underactuated SAM applied to the narrow vacuum chamber of the CFETR is proposed. The kinematics equation of the SAM is subsequently established using the Denavit-Hartenberg (D-H) method, and the static cable driving force is calculated. For the strong coupling characteristics of SAM dynamics, an equivalent dynamics modelling method is proposed. The forcibly equivalent method is adopted for the transformation of joint torque and cable traction force. Then, the equivalent dynamic model can be obtained based on traditional series robot dynamics modelling methods (Lagrangian method, etc.). Finally, we establish a SAM dynamics controller and prototypes and verify the effectiveness of equivalent dynamics modelling through simulation and experiments. The remainder of the paper is organized as follows. In Section 2, we introduce the underactuated SAM platform. In Section 3, we outline the equivalent dynamic analysis method. In Section 4, we introduce an equivalent dynamics controller. In Section 5, we present the related simulation and experiments of the single-joint group and a SAM with an arm length of 1500 mm. In the last section, we summarize the full text and provide several conclusions.
## 3 Simulation Results
### Air-Incident Transmission Pattern  
Subsequently, the transmission energy was analyzed. Figure 9a shows the percentage of the air-incident energy, which was transmitted into the water across the sea surface. Figure 9b-d show the horizontal, 45\({}^{\circ}\), and circular polarization, respectively. The wind-blown rough sea surface had less influence on the transmission light. The transmitted lights were concentrated on the specular-refracted quad near \(\theta=32^{\circ}\). The energy decreased sharply to zero when it was transmitted away from the specular-refracted quad. Figure 9b shows that the sea surface had less of an effect on depolarization for transmission light compared with reflected light. The 45\({}^{\circ}\) polarization shown in Figure 9c arose because of transmission light titled by wave facets. As shown in Figure 9d, the circular polarization was nearly zero.  
Figure 7: Reflection and transmission of ocean surface for the water-incident case and lidar Stokes status of \(S_{0}=[1,1,0,0]^{\prime}\): (**a**) I of reflected light, (**b**) Q of reflected light, (**c**) U of reflected light, (**d**) DoP of reflected light, (**e**) I of transmission light, (**f**) Q of transmission light, (**g**) U of transmission light, and (**h**) DoP of transmission light. I, Q, and U are Stokes vector parameters.  
Figure 8: Reflected energy pattern for polarized air-incident lidar at 45\({}^{\circ}\) incident angle from the zenith with 10 m/s wind speed. The viewing direction is looking downward, facing the specular reflection direction. (**a**) I of reflectance; (**b**) \(Q\)/\(I\), horizontal polarization (\(>\)0) and vertical polarization (\(<\)0); (**c**) \(U\)/\(I\), \(+\)45\({}^{\circ}\) polarization (\(>\)0) and \(-\)45\({}^{\circ}\) polarization (\(<\)0); (**d**) circular polarization.  
Figure 9: Transmitted energy pattern for polarized air-incident lidar corresponding to Figure 8. The viewing direction is looking upward, facing the specular refraction direction. (**a**) I of reflectance; (**b**) \(Q\)/\(I\), horizontal polarization (\(>\)0) and vertical polarization (\(<\)0); (**c**) \(U\)/\(I\), \(+\)45\({}^{\circ}\) polarization (\(>\)0) and \(-\)45\({}^{\circ}\) polarization (\(<\)0); (**d**) circular polarization.
# Study on Permeability Stress-Sensitivity in
## 2 Experiments
### Experimental Set-Up and Scheme  
The high-temperature and high-pressure stress-sensitive test system [27] was used to measure the permeability of rock sample by the steady state method, and the structure of the device is shown in Figure 2. The device consists of four parts: hole pressure system, core holder, confining pressure system and back pressure system. The maximum confining pressure that can be set for the device is 90 MPa, the maximum pore pressure is 60 MPa and the maximum temperature is 125 \({}^{\circ}\)C, meeting the experimental requirements. Nitrogen is  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Depth** & **Weight** & **Length** & **Diameter** & **Porosity** & **Permeability** \\  & **(cm)** & **(g)** & **(cm)** & **(cm)** & **(\%)** & **(mD)** \\ \hline A1 & 6786.29 & 64.79 & 5.042 & 2.500 & 1.468 & 0.047 \\ A2 & 6703.50 & 62.48 & 5.045 & 2.500 & 4.575 & 0.028 \\ A3 & 6707.43 & 64.24 & 5.048 & 2.500 & — & 0.036 \\ A4 & 6706.61 & 62.29 & 5.045 & 2.504 & 4.606 & 0.033 \\ A5 & 7001.91 & 56.71 & 4.433 & 2.500 & 1.594 & 0.023 \\ B1 & 6778.62 & 64.38 & 5.063 & 2.502 & 2.558 & 0.095 \\ B2 & 6783.92 & 66.10 & 5.063 & 2.516 & — & 3.454 \\ B3 & 6706.61 & 62.41 & 5.054 & 2.499 & — & 0.622 \\ B4 & 6878.80 & 62.49 & 5.055 & 2.500 & 4.198 & 0.087 \\ B5 & 7363.07 & 64.21 & 5.030 & 2.500 & — & 0.113 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameter values of rock samples.  
Figure 1: The rock samples drilled from the target formation.  
stable, less prone to chemical reaction, safe and controllable, therefore it has been selected as the experimental fluid.  
The stress-sensitivity experiments of tight sandstone reservoirs mainly simulate the influence of effective stress changes on rock permeability [28]. The steady-state method was used for the determination of core permeability; the effective stress is increased successively by 5, 10, 15, 25, 35, 45, 55 and 65 MPa. The steady-state time of each test point was two hours, the data were recorded after state stabilized, the test was repeated five times and the average value was taken as the permeability of the test point.