esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results and Analysis
### Flat Walking Task  
In order to prove that the algorithm framework proposed in this work can complete the autonomous motion learning task of a quadruped robot, the robot was first made to carry out motion learning on a flat ground.  
Figure 7 shows the reward value obtained by the quadruped robot trained in a virtual environment on flat ground. It can be seen clearly from the picture that the reward value increases rapidly from 0 to 1500 steps, meaning that the robot can quickly learn the movement, from the initial fall to the complete walk, which signifies that the robot can quickly learn the motor skills by using our proposed algorithm. By 9000 steps, the reward function value has exceeded 600, and the robot has learned good motion.  
Figure 8 shows the position of the center of gravity when the robot moves on flat ground. The X direction represents the yaw direction, the Y direction represents the forward direction and the Z direction represents the direction of the body shaking up and down during the movement of the robot. It can be seen from the figure that the robot's position in the Y direction is basically a straight line, which indicates that the robot presents uniform motion in the forward direction, especially during the period from 4.5 s to 9 s. From the Z direction, it can be seen that the robot would shake up and down in the process of lifting its legs and landing. Although there is a small amount of yaw in the X direction during movement, the maximum yaw is less than 0.12 m, and the robot is also corrected in real-time during the movement. Therefore, on the whole, the motion state of the robot is good, which proves that the algorithm proposed in this paper can achieve good movement of the robot.  
Figure 9 shows the changes in the robot's body posture in the process of movement. The solid line represents the learning result using the reward function \(r\) in this paper, while the dashed line represents the learning result using only \(r_{x}\) and \(r_{y}\) as \(r_{xy}\). It can be seen from the figure that the yaw angle of the robot can be greatly reduced after learning with the reward function \(r\) compared with that of the reward function \(r_{xy}\). This is because the addition of the reward function \(r_{o}\) is equivalent to the constraint on the robot's attitude, and the pitch angle and roll angle are not large, so the constraint effect is not very obvious.  
It can be seen from Figures 8 and 9 that the learning algorithm proposed in this paper can enable the robot to better complete the flat motion and basically achieve uniform straight motion on flat ground, and the pitch angle, yaw angle and roll angle all vary within a small range, with the maximum of 0.09, 0.10 and 0.09 radians, respectively.  
Figure 8: Curves of the robot’s center of gravity position during walking on flat ground.  
Figure 7: Reward.
# Lp-Norm Inversion of Gravity Data Using Adaptive Differential Evolution
###### Abstract
[34] proposed a modified Boltzmann Annealing Differential Evolution (BADE) algorithm, which uses an annealing strategy to avoid the local minima and solve the inversion problem in the directional resistivity logging-while-drilling (DRLWD) measurements; the results also show robustness and immunity to the non-uniqueness inversion problem of their method. [PERSON] et al. [17; 35] applied DE to both inverting model parameters from residual gravity anomalies and amplitude of the 2D analytic signal of magnetic anomalies. Their works show the applicability and effectiveness of this algorithm on both synthetic and field anomalies, but they mainly focus on the basic parameters of the field source. [PERSON] et al. [36] achieved a DE inversion for total field magnetic anomalies caused by vertical-sided prismatic bodies, and they mainly focus on geometric structure inversion. Recently, [PERSON] et al. [37] reconstructed magnetic susceptibility by using an improved adaptive DE. According to the inverted results, their inversion methods can help maintain the sharp boundary of magnetic sources. Apart from the above-mentioned studies, DE has few applications in physical property inversion, although this method has become popular for geophysical inversion. The main reason is that in DE iteration, the smooth constraint is difficult to apply directly to physical property parameters. Here, we present a DE algorithm incorporated with the \(lp\) regularization technique to resolve the inversion problem by determining the 2-D distribution of the subsurface density distribution in the mineral exploration. Our gravity inversion method is tested with synthetic examples, and then applied to an iron ore deposit. Finally, we briefly discuss the application of \(lp\) norm gravity inversion using DE.
## 3 Shortest Queue Length-Cluster-Based Routing Protocol
### Topology Information Maintenance Mechanism  
Despite the routing decision process mainly happening in cluster heads, the source node also needs to know the cluster where the destination node is located to establish a path from the source node's cluster to the destination node's cluster. Therefore, each cluster head needs to maintain the global cluster structure information of the network. However, due to the mobility of the satellite self-organizing network, the cluster structure may change over time. Therefore, it is necessary for the cluster heads to periodically flood their connection table which contains the topology information of the cluster such as members and neighbors. The period is consistent with the cluster maintenance cycle.  
The details of the topology information maintenance mechanism are as follows:  
1. After the periodic cluster maintenance is completed, if the local cluster structure has been changed, the cluster head updates the connection table and sends it to neighboring cluster heads;
2. After receiving the broadcast connection table, the neighboring cluster head first checks if it is a new connection table. If it is, create a new connection table locally and update the local connection table list. If not, proceed to the next step;
3. Determine whether the connection table is the latest table of the corresponding cluster. If it is, update the local connection table list. Otherwise, discard this table;
4. Repeat the above three steps in a cycle until the connection table floods over the entire network.
# Adaptive Multi-Channel Residual Shrinkage Networks for the Diagnosis of Multi-Fault Gearbox
## 2 Basic Theory
### Convolutional Neural Networks (CNN)  
CNN has two prominent features, i.e., sparse connectivity and weight sharing. The two most important elements of CNN are the convolutional layer and pooling layer, while the fully connected layer will be classified.  
(1) Convolutional Layer: There are several convolutional kernels in the convolutional layer, and the raw data are convolved to obtain data features by convolutional operations. The formula is described as follows:  
\[S_{j}^{l}=f(\sum_{i\in N}S_{i}^{l-1}*\omega_{ij}^{l}+b_{j}^{l}) \tag{1}\]  
where \(S_{j}^{l}\) is the output feature of the \(j\)th convolutional kernel in layer \(l,f(\cdot)\) is the activation function, \(S_{i}^{l-1}\) is the output feature of the \(i\)th convolutional kernel in layer \(l-1\), \(\omega_{ij}^{l}\) is the weights, \(b_{j}^{l}\) is the bias, and \(N\) is the target convolution range.  
(2) Pooling Layer: In pooling operations, the space size of the data body is reduced by using maximum pooling. Thus, the overfitting phenomenon of the network can be effectively controlled. The formula is described as follows:  
\[x_{il}=Max(x_{i(l-1)}),i\leq i^{\prime}\leq i+m \tag{2}\]  
where are all eigenvalues in the sampling range of layer \(l-1\), m is the width of a pooling window, \(x_{il}\) is the corresponding maximum pooled sampling value, and \(Max\) (\(\cdot\)) is the maximum value function.
###### Abstract  
Depending on the vineyard location, cluster thinning (CT) may represent an effective tool to obtain the desired grape composition and wine quality. The effect of 20% cluster thinning on Ribolla Gialla (_Vitis vinifera_ L.) sparkling wine aroma, lipid compounds, and aromatic amino acid (AAA) metabolites composition was studied for three consecutive seasons in two vineyards located in the Friuli Venezia Giulia region, Italy. In the examined sparkling wines, the vintage meteorological conditions exhibited significant influences on the metabolic profile of the samples. Data were normalized by season, and the impact of the CT treatment was evaluated for each vineyard site separately. Crop removal showed a limited positive impact on aroma compounds in sparkling wines from vineyards located in the valley. Concerning the AAA compounds, their concentration was higher in the vineyard at the foot of the hills. Cluster thinning resulted in a drop in concentration, reducing the risk of atypical aging. Despite minor differences according to targeted metabolome profiling, the sensory analysis confirmed the effects of the CT treatment in the valley floor vineyard. Reducing crop in this site, where the yield was higher, promoted a moderate improvement of Ribolla Gialla sparkling wine. In contrast, for wine produced in the vineyard at the foot of the hills, the sensory analysis indicated a preference for wines from the unthinned control samples. Overall, the study indicates that cluster thinning is a viticultural technique that could potentially improve the quality of Ribolla Gialla sparkling wines, but only in situations of excessive grape production.  
Ribolla Gialla; sparkling wine; cluster thinning; vineyard site; volatile organic compounds; lipids; aromatic amino acids; sensory analysis +
Footnote †: journal: Metabolites
# An Ultra-Wideband Frequency Selective Rasorber with Low Infrared Emissivity
## 2 Analysis, Design and Experimental Verification
### Design of the FSR  
According to the equivalent circuit analysis in Section 2.1, in order to create a transmission window with minimal insertion loss at a frequency higher than the absorption band, the equivalent circuit form of RS must include a parallel LC circuit. At the resonant frequency of the parallel LC circuit, the equivalent surface impedance of the impedance surface layer will approach infinity, ensuring low insertion loss for transmitted electromagnetic waves. For FSR's bandpass FSS layer, it is necessary not only to be in a \"bandpass\" state within the wave-passing band of the RS layer but also to exhibit near-total reflection characteristics within its designed absorption band. Thus, the design of FSR should encompass both in-band transparency and out-of-band absorption characteristics. The two key challenges are as follows: first, designing wideband low insertion loss within the transmission band; secondly, achieving impedance matching across a wide absorption band. Therefore, FSR's basic design concept is as follows: RS and FSS are designed for widebandtransmission, respectively; subsequently cascading them and adjusting resistance values and interlayer spacing enables the realization of an FSR design featuring both broad wave absorption and transmission capabilities.  
The conclusion in Section 2.1 indicates that the wideband transmission of RS is a prerequisite for achieving the overall FSR transmittance, thus necessitating the initial design of RS.
# In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification
## 4 Experimental Settings
### Models and Implementation
#### 4.2.2 Appearance Feature Extraction  
We evaluate the following three backbone architectures for appearance feature extraction: OSNet [16], MGH [7], and PiT [10]--each selected for its distinctive strengths. For example, OSNet is known for its lightweight design, MGH utilizes hypergraphs to capture complex relationships, and PiT employs a transformer-based approach to leverage global context. Detailed specifics for each architecture are provided in Section 3.2.  
* **OSNet Implementation.** OSNet is pretrained on ImageNet, with input frames resized to \(256\times 128\) pixels. Training extends over 60 epochs, with a batch size of 6 and using the Adam optimizer and cross-entropy loss. The learning rate is set at \(10^{-3.5}\).
* **MGH Implementation.** In the MGH architecture, images are resized to \(256\times 128\) pixels. Training batches consist of 8 random sub-sequences from 8 individuals. We use a hypergraph with \(L=2\) layers and \(K=3\) neighbors; spatial partitions of 1, 2, 4, and 8; and temporal thresholds set to 1, 3, and 5. The Adam optimizer uses a weight decay of \(5\times 10^{-4}\), and the learning rate, starting from \(10^{-3.5}\), reduces tenfold every 100 epochs up to 300 epochs. Graph-level features are concatenated after training, using cosine similarity as the matching metric [48].
* **PiT Implementation.** The PiT model, based on the pre-trained ViT-B16 transformer [22], processes images resized to \(256\times 128\) pixels. The batch size and \(m\) parameter are set to 16 and 11, respectively. The convolution layer, with a kernel size of 16 and stride of 12, results in feature embeddings with dimensions of \(21\times 10\times 768\). Trade-off parameters \(\lambda_{1}\) and \(\lambda_{2}\) are fine-tuned to 1.0 and 1.5, respectively. Division parameters are set to \(D_{v}=2\), \(D_{h}=3\), and \(D_{p}=6\) (see [10] for more details). Training uses Stochastic Gradient Descent (SGD) with momentum, starting from a learning rate of \(10^{-2}\) for 120 epochs, which is adjusted using cosine annealing. The convolution and transformer layers are initially frozen for the first five epochs to optimize the refinement of the classifier.
# Atomic Layer Deposition (ALD) of Metal Gates for CMOS
## 2 ALD of the Metal Gates  
Atomic layer deposition is a derivative of chemical vapor deposition (CVD), using the same reactants but different reactant introduction mode, to have a thickness control of mono-atomic layer level [60]. In CVD, the reactants, say AC and BD, are introduced together simultaneously and continuously into reaction chamber and the chemical reactions takes place in gas phase, or on the top surface of the substrate after absorption, to form a deposit AB. For ALD, the reactants are introduced separately and sequentially, in different pulse steps separated by purge step. A typical ALD cycle is composed of four sequential process steps: pulse of AC, purge, pulse of BD, purge. If the pulse steps are long enough, AC or BD could react with the pre-deposited B or A at all possible sites on the exposed surface of the substrate and finish a surface saturation reaction. After the saturation, no more sites are available, and the reaction will be self-limited. The introduction of the reactants will stop, and the residual gas purged by the inert gas. An ideal ALD cycle deposits only one monolayer of the atom. The merit of ALD is that its outstanding thickness uniformity and isotropic characteristic, since the surface reactions during the two pulse steps are self-limited.  
Similar to CVD, most of ALD is thermal ALD, or it takes place at high temperature. This limits it from the applications which cannot stand the deposition temperature. To lower the deposition temperature, plasma is used to enhance the ALD reaction. The choice between the thermal ALD and the plasma-enhanced ALD (PEALD) should depend on the requirement of the application. For the deposition of high-\(\kappa\) and metal gate, PECVD is not a favorable technique, because the plasma might induce surface damage of the delicate gate area [61; 62]. If a thermal ALD with acceptable deposition temperature is available, PEALD will be given up. In the following paragraphs, the ALD of p-metal gate and n-metal gate is discussed.
## 4 Experiments
### Parameter Settings  
The parameters include (1) the number of clusters, (2) the transmission rate \(\gamma\), and (3) the evaporation rate \(\lambda\).  
The transmission rate \(\gamma\) controls the pheromones exchanging speed between a user and an item, and has a value of \(\gamma\in[0,1]\). When \(\gamma=0\), no pheromone exchange occurs when a rating is given by a user to an item. A greater \(\gamma\) indicates faster exchanges of pheromones. \(\gamma=1\) indicates a full exchange of pheromones between the user and the item for each rating. For simplicity, in our experiments, \(\gamma\) is set to 1.  
The evaporation speed of the pheromone of the user or the item is affected by the evaporation rate \(\lambda\in[0,1]\). A greater \(\lambda\) indicates a slower evaporation of pheromones, which means weaker time effects on the user's preference towards an item. As the influence of pheromone evaporation in real ant systems is unclear, the \(\lambda\) is empirically set according to [24]. The optimal value is set at 0.2, as illustrated in Figure 1, where the value along the x-axis represents the evaporation rate, and the value along the vertical axis represents the mean Root Mean Square Error (RMSE) over 10 runs. The results indicate a high sensitivity regarding the evaporation rate.  
The optimal cluster number found is 20. The number of clusters is determined with the elbow method. A quick running of the Yellowbrick [25] implementation of the elbow method suggests that the number of clusters at 20 gives a balance between more variation and over-fitting.  
Figure 1: Empirical evaluation of Root Mean Square Error (RMSE) over different value of evaporation rate \(\lambda\).
## 3 Results
### NPP Sensitivity in Response to Climate Change and Elevation  
We conducted a partial correlation analysis of NPP\({}_{\text{slope}}\) with Temp\({}_{\text{slope}}\), Pre\({}_{\text{slope}}\), and the DEM (Table 2). The results showed that Temp\({}_{\text{slope}}\), Pre\({}_{\text{slope}}\), and the DEM all significantly affected NPP\({}_{\text{slope}}\). Among them, NPP\({}_{\text{slope}}\) was significantly positively correlated with  
Figure 7: R\({}^{2}\) of the explanatory capacity for NPP of temperature, precipitation, and their interactions; NPP per 100 m interval of elevation **(a)** for the all the alpine grassland, **(b)** for all the alpine meadow, and **(c)** for all the alpine steppe. Note: Each point indicates the mean value of R\({}^{2}\) over a 100 m interval; The solid line indicates that the linear fit reaches the significance level, while the dashed line indicates that the linear fit does not reach the significance level; Shadows represent confidence bands with 95% confidence.  
\(\text{Temp}_{\text{slope}}\) and \(\text{Pre}_{\text{slope}}\), while it was significantly negatively correlated with the DEM. The partial correlation coefficients between \(\text{NPP}_{\text{slope}}\) and \(\text{Temp}_{\text{slope}}\) and between \(\text{NPP}_{\text{slope}}\) and \(\text{Pre}_{\text{slope}}\) were greater in alpine steppe than in alpine meadow, while the partial correlation coefficient between \(\text{NPP}_{\text{slope}}\) and the DEM was greater in alpine meadow.  
To verify the effects of elevation, temperature change, and precipitation change on the magnitude of NPP change, the SEM was used to explore the operation of these three factors on the magnitude of NPP change (Figure 8). The DEM ranked first in terms of influence (standard coefficient \(=-0.44\), \(p<0.01\)), as evidenced by a significant decrease in \(\text{NPP}_{\text{slope}}\) due to elevation gain. \(\text{Temp}_{\text{slope}}\) ranked second (standard coefficient \(=0.15\), \(p<0.01\)), as a greater trend of temperature enhanced the \(\text{NPP}_{\text{slope}}\). Third, a greater trend of precipitation improved the \(\text{NPP}_{\text{slope}}\) (standard coefficient \(=0.11\), \(p<0.01\)). The model indicated a smaller variability of NPP at higher elevations than at lower elevations for the same extent of temperature and precipitation change, implying that increasing elevation suppressed the sensitivity of NPP to temperature and precipitation, and that this suppression was stronger in alpine meadows than in alpine steppes.
## 3 Reduced-order model
### Basis functions for in-plane displacement  
Basis functions for the in-plane displacement can be determined by solving the following linear eigenvalue problem associated with equation (10b), see for example [41],  
\[\kappa^{2}\bar{u}_{i}+(1-\
u)\frac{\partial^{2}\bar{u}_{i}}{\partial x_{k} \partial x_{k}}+(1+\
u)\frac{\partial}{\partial x_{i}}\left(\frac{\partial \bar{u}_{k}}{\partial x_{k}}\right)=0,\quad i=1,2, \tag{16}\]  
where \(\kappa\) is the wave number. Following [41], we decompose the in-plane displacement as \(u_{i}=u_{i}^{\mathrm{n}}+u_{i}^{\mathrm{t}}\), with \(i=1,2\), where \(u_{i}^{\mathrm{n}}\) and \(u_{i}^{\mathrm{t}}\) are displacements associated, respectively, with the longitudinal and the transverse waves, and satisfy  
\[\frac{\partial\bar{u}_{1}^{\mathrm{n}}}{\partial x_{2}}-\frac{ \partial\bar{u}_{2}^{\mathrm{n}}}{\partial x_{1}}=0, \tag{17a}\] \[\frac{\partial\bar{u}_{1}^{\mathrm{t}}}{\partial x_{1}}+\frac{ \partial\bar{u}_{2}^{\mathrm{t}}}{\partial x_{2}}=0. \tag{17b}\]  
Therefore, equation (16) is equivalent to the following pair of partial differential equations [41]  
\[\frac{\partial^{2}\bar{u}_{i}^{\mathrm{n}}}{\partial x_{k}\partial x _{k}}+\eta_{\mathrm{n}}^{2}\bar{u}_{i}^{\mathrm{n}}=0,\quad i=1,2, \tag{18a}\] \[\frac{\partial^{2}\bar{u}_{i}^{\mathrm{t}}}{\partial x_{k} \partial x_{k}}+\eta_{\mathrm{t}}^{2}\bar{u}_{i}^{\mathrm{t}}=0,\quad i=1,2, \tag{18b}\]  
where \(\eta_{\mathrm{n}}=\kappa/\sqrt{2}\) and \(\eta_{\mathrm{t}}=\kappa/\sqrt{1-\
u}\) are wave numbers of the longitudinal and the transverse waves, respectively.  
By integrating over the domain \(\Omega\) and by applying [PERSON]'s formulas to transform surface integrals into line integrals, equation set (17) implies the following set of boundary conditions for the displacements associated with the longitudinal and the transverse waves  
\[u_{1}^{\mathrm{n}}\left(x_{1},0\right)=0,\quad u_{2}^{\mathrm{n}} \left(1,x_{2}\right)=0,\quad u_{1}^{\mathrm{n}}\left(x_{1},\varphi\right)=0, \quad u_{2}^{\mathrm{n}}\left(0,x_{2}\right)=0, \tag{19a}\] \[u_{2}^{\mathrm{t}}\left(x_{1},0\right)=0,\quad u_{1}^{\mathrm{t} }\left(1,x_{2}\right)=0,\quad u_{2}^{\mathrm{t}}\left(x_{1},\varphi\right)=0, \quad u_{1}^{\mathrm{t}}\left(0,x_{2}\right)=0. \tag{19b}\]  
Additional boundary conditions are provided by equation (13b) through  
\[u_{2}^{\mathrm{n}}\left(x_{1},0\right)=0,\quad u_{1}^{\mathrm{n} }\left(1,x_{2}\right)=0,\quad u_{2}^{\mathrm{n}}\left(x_{1},\varphi\right)=0, \quad u_{1}^{\mathrm{n}}\left(0,x_{2}\right)=0, \tag{20a}\] \[u_{1}^{\mathrm{t}}\left(x_{1},0\right)=0,\quad u_{2}^{\mathrm{t} }\left(1,x_{2}\right)=0,\quad u_{1}^{\mathrm{t}}\left(x_{1},\varphi\right)=0, \quad u_{2}^{\mathrm{t}}\left(0,x_{2}\right)=0. \tag{20b}\]  
Therefore, basis functions for the in-plane displacement are given by  
\[\bar{u}_{(p)i}=A_{mn}\sin\left(m\pi x_{1}\right)\sin\left(\frac{n\pi x_{2}}{ \varphi}\right),\qquad m=1,\ldots,\bar{p},\quad n=1,\ldots,\bar{q}, \tag{21}\]  
where \(A_{mn}\) are constants used for normalization, and \(p=(m-1)\bar{q}+n\) for \(m=1,\ldots,\bar{p}\) and \(n=1,\ldots,\bar{q}\).