esa-sceva/satcom-chunk-collection huggingface dataset:
# External Slot Relationship Memory for Multi-Domain Dialogue State Tracking
## 3 Model
### Slot Contextual Information Extraction Layer  
In the context of a dialogue, each slot pays attention to different parts of the dialogue context. To predict the state of a specific slot, it is necessary to extract slot-specific feature information from the dialogue history. To achieve this, we employ a slot attention mechanism based on multi-head attention [30]. This mechanism allows the model to focus on relevant parts of the dialogue utterances that are informative for predicting a specific slot, thereby improving prediction accuracy. It enables the subsequent layers of the model to capture the semantic information and contextual information specific to each slot, thus capturing the interrelationships between slots effectively.  
\[Q_{t}^{S_{j}}=h_{S_{j}}W_{Q}+b_{Q} \tag{5}\]  
\[K_{t}^{S_{j}}=H_{t}W_{K}+b_{K} \tag{6}\]  
\[V_{t}^{S_{j}}=H_{t}W_{V}+b_{V} \tag{7}\]  
\[\alpha_{t}^{S_{j}}=Softmax(\frac{Q_{t}^{S_{j}}K_{t}^{S_{j}}}{\sqrt{d_{k}}})V_{t }^{S_{j}} \tag{8}\]  
\[C_{t}^{S_{j}}=W_{2}ReLU\left(W_{1}\left[h_{S_{j}},\alpha_{t}^{S_{j}}\right]+b_{ 1}\right)+b_{2} \tag{9}\]  
The parameters \(W_{Q}\), \(b_{Q}\), \(W_{K}\), \(b_{K}\), \(W_{V}\), and \(b_{V}\) are linear layer parameters used to map the queries, keys, and values, respectively. Here, \(d_{k}=d_{h}/N\), where \(d_{h}\) represents the hidden size of the model and N is the number of heads in the multi-head attention mechanism.
## 2 Methodology
### Bayesian Optimization Algorithm  
The Bayesian optimization (BO) algorithm is a probabilistic optimization method well-suited for global optimization problems, particularly those involving expensive functions without an analytical expression [15]. BO approximates the posterior distribution of an unknown objective function using prior knowledge and then selects the next hyperparameter combination to sample based on this distribution. This approach allows BO to effectively utilize information from previous searches, minimizing extensive and inefficient exploration of the hyperparameter space.  
BO's two key components are the probabilistic surrogate model and the acquisition function. The surrogate model approximates the objective function, with Gaussian processes being the most used. Gaussian processes assume that the function values at any finite set of points follow a multivariate normal distribution, defining the similarity between points through a kernel function to construct the covariance matrix. The acquisition function selects the next parameter point to evaluate. It balances exploration and exploitation based on the surrogate model's output, efficiently finding the global optimum. The synergy between the surrogate model and the acquisition function enables BO to excel in solving complex and expensive optimization problems.
# In Situ Experimental Study of the Friction of Sea Ice and Steel on Sea Ice
## 4 Discussion
### Surface Roughness  
The microcosmic surface characterization is a critical parameter in the contact mechanics. However, the surface of nature ice cover is always not flat and has bumps. Though the obvious bumps were cleaned in the current study, the inconspicuous bumps still had scales larger than the microcosmic surface roughness. The pure effect of the surface roughness could be covered by the bumps. From the view of ice navigation, the nature ice surface morphology is various, including bare ice, snow cover, ice ridge and melt pond, which surfaces are not even, and the scales of bumps are larger than the microscopic surface characterization. Moreover, the ice surface is known to be very reactive  
Figure 10: Variations of \(\mu_{\rm k}\) with velocity \(V\) for SS-ice, CS-ice, and ice-ice contacts. The data at a given velocity were the mean values over different test conditions, and the error bars represent the standard deviation.  
and its nature may change easily by contact with other materials [24]. Therefore, a detailed study on the effect of surface roughness on \(\mu_{\rm k}\) is beyond the scope of the current work, which can be investigated in laboratory using well-prepared ice samples. [PERSON] et al. [29] conducted the surface analysis on three kinds of concrete and four kinds of steel plates using a high-accuracy surface roughness meter and pulled them on ice samples. Their results showed that the friction coefficient had no apparent correlation with wavelength of irregularities, but increased with increasing mean wave height and mean steepness and with decreasing mean sharpness. [PERSON] and [PERSON] [25] conducted ice-ice friction tests at \(-10\)\({}^{\circ}\)C and \(10^{-4}\) m\(\cdot\)s\({}^{-1}\) using fresh ice of surface roughness \(R_{\rm a}\) between 0.1 and 12 \(\upmu\)m, and an increasing trend was detected as \(\mu_{\rm k}\propto R_{\rm a}^{\ 0.08}\) (\(R^{2}=0.98\)). [PERSON] et al. [23] measured the \(R_{\rm a}\) in directions parallel and perpendicular to sliding before and after ice-ice friction under 20 kPa at \(-3\)--\(10\)\({}^{\circ}\)C. After testing, the roughness increased by about two to four times when measured along the sliding direction, and the increased roughness was greater after tests at lower velocity than at higher velocity, which corresponds to the high friction coefficient at the lower speed.
# Fungal Pigments and Their Roles Associated with Human Health
## 3 Biosynthesis of Fungal Pigments
### Terpenoid Synthetic Pathways  
Biochemically, the carotenoids are terpenoids [15]. Accordingly, the synthesis of carotenoids utilizes the terpenoid synthetic pathways deriving from the condensation of C\({}_{5}\) isoprene units [108]. The common terpenoid precursor is isopentenyl pyrophosphate (IPP), which is synthesized either via the mevalonate pathway (generated from hydroxymethylglutaryl coenzyme A, HMG-CoA), or via the non-mevalonate pathway (generated from the condensation of pyruvate and glyceraldehyde 3-phosphate [109]. So far, available research has demonstrated that fungal IPP is produced via the mevalonate pathway, whereas in bacteria and photosynthetic species IPP is produced via the non-mevalonate pathway. The early biosynthetic steps involve the sequential additions of IPP (isoprene, C\({}_{5}\)) units to yield geranyl pyrophosphate (GPP, C\({}_{10}\)), famesyl pyrophosphate (FPP, C\({}_{15}\)), and geranylgeranyl pyrophosphate (GGPP, C\({}_{20}\)) [108,109]. The initial compound possessing the typical aliphatic carotenoid-like structure is 15-cis-phytoene (a colorless molecule), consisting of a symmetrical polyene chain generated via the condensation of two GGPP units catalyzed by phytoene synthase (Figure 8). The light-absorbing characteristics of carotenoids are attributed to the presence of a chromophore, which is comprised of an array of conjugated double bonds via the catalysis of desaturases. Different subsequent chemical modifications, usually the introduction of a cyclic end group (with \(\beta\) and \(\varepsilon\) rings being the most frequently introduced) into at least one of the ends of the molecule, and/or oxidative reactions (for instance, hydroxylation, carboxylation, epoxidation, esterification, etc.), may contribute to the huge arsenal of carotenoids [108].
## 3 System Framework and Algorithm Procedure  
In this section, we present the algorithm procedure, frame format, and some implementation details. Fundamentally, the DMAC algorithm partitions the channel time into a series of superframe periods. Additionally, each superframe consists of a CP and a TP. The nodes successfully contending channel during CP can send the data packet continuously and with collision-free according to the order in contention queue (CQ) during TP. I-DMAC1 is designed to provide different channel contention probability for nodes to achieve the time fairness in multi-rate scenarios, and I-DMAC2 enable the nodes to transmit several packets in one transmission opportunity without collision occurring.
## 2 Materials and Methods
### Cropland Suitability Classification According to FAO Standards  
Cropland suitability classification was conducted in accordance with FAO standards using an unsupervised K-means classification applied to the most accurate annual predictions of the LAI and FAPAR across all agricultural land within the study area. This analysis involved 14 input layers, comprising one LAI and one FAPAR layer per year over a span of seven years, categorizing the data into five distinct classes. The classification process ranked these classes based on the cumulative means of LAI and FAPAR values in the seven-year study period. Based on the mean class values resulting from K-means classification, these classes were translated into FAO suitability categories, where the class with the highest cumulative values of all classes resulting from K-means classification was designated as highly suitable (S1), followed by moderately suitable (S2), marginally suitable (S3), currently not suitable (N1), and permanently not suitable (N2) based on descending mean values [5]. Therefore, the S1 class designating highest cropland suitability had the highest sum of the mean class LAI and FAPAR values from K-means classification, while the N2 class of the lowest cropland suitability had the lowest sum of mean values. The outcome of this classification provided detailed cropland suitability maps specifically for soybean cultivation, which included the entire agricultural land in the study area.
## 2 Materials and Methods
### Agricultural Operations and Remote Sensing Data
The final dataset comprised Sentinel-1 variables (VH, CR, and \(\Delta\)VH = VH\({}_{\text{t}}-\text{VH}_{\text{t-1}}\)) and the NDVI from Sentinel-2.
## 3 Categorization of Visual Inspection (Tasks)
### Overview on How to Solve Automated Visual Inspection with Deep-Learning Models
#### 3.3.3 Visual Inspection via Localization  
As previously mentioned, localization is used to detect where an object of interest is located. Table 4 summarizes which VI use cases are addressed with localization and the appropriate models. In a total of 50 publications, localization was employed for AVL Contrary to classification approaches, crack detection is the most addressed VI use case, with a total of 26 publications investigating it. The most utilized approach for crack detection is the CNN, which was applied in eight publications. Furthermore, in three other publications, extended CNN architectures were used. [PERSON] et al. [120] introduced a CNN with an attention mechanism, and [PERSON] et al. [121] used a CNN with an encoder-decoder architecture. [PERSON] et al. [122] combined a CNN with a long short-term memory cell (LSTM) to process the images recurrently for crack detection. Among custom CNN approaches, six authors used UNet to detect cracks, mostly in public constructions. Damage detection via localization occurred 16 times and was addressed with at least twelve different DL-based models. Three authors decided to approach it with DL-based models of the Transformer family. For example, [PERSON] et al. [123] utilized a Swin-Transformer to  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**VI Use Case** & **Model** & **Count** & **References** \\ \hline \multirow{6}{*}{Crack Detection} & AlexNet & 1 & [90] \\  & CNN & 1 & [86] \\  & EfficientNet & 1 & [91] \\  & ResNet & 1 & [92] \\  & VGG & 2 & [87, 88] \\ \hline \multirow{6}{*}{Damage Detection} & AlexNet & 1 & [93] \\  & CNN & 6 & [66, 94, 95, 96, 97, 98] \\  & CNN LSTM & 1 & [99] \\  & EfficientNet & 2 & [100, 101] \\  & Ensemble & 1 & [56] \\  & GAN & 2 & [102, 103] \\  & MLP & 1 & [85] \\  & MobileNet & 1 & [104] \\  & ResNet & 4 & [105, 106, 107, 108] \\  & VGG & 2 & [60, 109] \\ \hline \multirow{2}{*}{Completeness Check} & ResNet & 1 & [51] \\  & SSD & 1 & [89] \\ \hline \multirow{6}{*}{Quality Inspection} & CNN & 3 & [110, 111, 112] \\  & DenseNet & 1 & [113] \\ \cline{1-1}  & ResNet & 2 & [55, 114] \\ \cline{1-1}  & VGG & 1 & [115] \\ \hline \multirow{6}{*}{Other} & CNN & 1 & [65] \\ \cline{1-1}  & EfficientNet & 1 & [116] \\ \cline{1-1}  & MLP & 1 & [117] \\ \cline{1-1}  & MobileNet & 1 & [118] \\ \cline{1-1}  & ResNet & 1 & [54] \\ \cline{1-1}  & VGG & 1 & [119] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Overview of VI use cases and models that solve these use cases via multi-class classification.  
localize damages on rail surfaces. Completeness checks can be executed with YOLO and/or regional convolutional neural networks (RCNNs). Furthermore, YOLO can be used for vibration estimation, as shown by [PERSON] et al. [124]. [PERSON] et al. [125] proposed a Faster RCNN to localize abnormalities on potato plants.
## 4 Experimental Evaluations and Results
### Remote Electrical Antenna Tilt Environment  
Remote Electrical Antenna Tilt is a joint-optimization problem in which the agent controls the tilt of the antenna to optimize several key performance indicators (KPI). The antenna performance is evaluated mainly in three different aspects, namely the quality, capacity, and coverage of the traffic delivered by each antenna. The quality KPI is measured by the Signal-to-Interference and Noise Ratio (SINR), where the user's equipment (UE) signal strength is compared to noise and interference, and capacity KPI measures the throughput (Tput) delivered from the antenna to the UE, while the coverage is indicated by the Reference Signals Received Power (RSRP) showing the received power level of a reference signal. The challenge of this setup lies in the opposing KPIs that are used. When an antenna is fully tilted down, the signal beam covers a narrow spot, delivering high capacities due to fewer UEs being served, and high-quality signal due to less interference from the opposing antenna. However, when the antenna is fully tilted up, it will cover a wider area, but it will deliver low capacities to the UEs and low quality due to interference from other antennas. Therefore, it is important to understand the factors of the performing agent when controlling the tilt of the antenna.  
In this work, the inputs (state variables) of the RL agent consist of the tilt position, RSRP, SINR, and throughput of the antenna. DeepliftSHAP [4] is applied to analyze the contribution of these input features to the prediction generated by the RL agent. The reward for the agent is constructed with factors similar to the input feature, which are SINR (\(R_{SINR}\)), RSRP (\(R_{RSRP}\)), and throughput (\(R_{Throughput}\)) metrics of the network. The reward calculation is shown in Equation (11) where the RSRP has twice the weight of other KPIs. This reward component has higher priority because the other two components have opposing properties, as mentioned above. We implement our drDQN model with three branches (one for each reward component) without shared layer, and each of them has two hidden layers where each layer has 32 nodes and rectified linear unit (ReLU) activation function.  
\[R_{total}=R_{SINR}+R_{Throughput}+2\times R_{RSRP} \tag{11}\]  
Once the RL training converged, we ran an evaluation where five episodes with 100 time steps in total, resulting in 2100 data points to collect the observations as the baseline for the Deeplift-SHAP explainer. Each datum is then fed to the trained agent to calculate the Q-values, select the optimal action, and generate a local explanation. The global explanation is calculated by taking the absolute mean value of all local explanations.
# An Arch-Bridge Topology-Based Expressway Network Structure and Automatic Generation
## 3 Methodology
### Automatic Generation of Expressway Network Topology  
#### 3.2.1 Topology Set Generation and Analysis  
Given the ETC driving trajectory set \(\boldsymbol{eTrAJ}=\left\{\boldsymbol{eTr^{1},eTr^{2},\ldots,eTr^{m}}\right\}\), the set of candidate topologies formed by adjacent trajectory points of all driving trajectories is called the candidate topology set \(\boldsymbol{cE}=\left\{\boldsymbol{ce_{1},ce_{2},\ldots,ce_{n}}\right\}\), \(\boldsymbol{ce_{ij}}=<g_{i},g_{j}>\ \in\ \boldsymbol{cE}\), \(g_{i}\in G\), \(g_{j}\in G\).  
However, there is a large amount of \"dirty\" data in the ETC data, which causes many incorrect topologies in the candidate edge set \(\boldsymbol{cE}\) and severely interferes with the reconstruction of the expressway network. There are mainly four aspects:  
1. Generally, data is repeatedly uploaded during transmission or copied repeatedly during storage, which easily leads to an increase in data size and causes serious interference to the automatic generation of ETC topology. In addition, when traffic congestion, breakdowns, and other issues occur within the ETC gantry antenna coverage area, the vehicle-mounted OBU will continue to communicate with the ETC antenna, resulting in duplicate data collection. As shown in Figure 4a, abnormal topology forms a loop, referred to as a loop topology.
2. Due to equipment failures, severe weather conditions, etc., the vehicle-mounted device and the ETC antenna may not communicate or fail to communicate successfully, resulting in data loss. At the same time, packet loss may occur during data transmission, causing data loss. When \(n\geq 3\), there may be consecutive data loss, referred to as a missing topology, as shown in Figure 4b.
3. Due to wireless interference, the vehicle-mounted OBU driving on the road upline (downline) successfully communicates with the ETC antenna deployed on the road downline (upline), and the resulting topology is referred to as opposite topology record data that do not conform to the rules of expressway driving, as shown in Figure 4c.
4. Special vehicles such as police cars and emergency vehicles inevitably perform reverse driving when executing emergency or special tasks, resulting in and referred to as a reverse topology, as shown in Figure 4d.  
Figure 3: An example of road trajectory.
###### Abstract  
Due to their high transmission ratio, high load carrying capacity and small size, planetary gears are widely used in the transmission systems of wind turbines. The planetary gearbox is the core of the transmission system of a wind turbine, but because of its special structure and complex internal and external excitation, the vibration signal spectrum shows strong nonlinearity, asymmetry and time variation, which brings great trouble to planetary gear fault diagnosis. The traditional time-frequency analysis technology is insufficient in the condition monitoring and fault diagnosis of wind turbines. For this reason, we propose a new method of planetary gearbox fault diagnosis based on Compressive sensing, Two-dimensional variational mode decomposition (2D-VMD) and full-vector spectrum technology. Firstly, the nonlinear reconstruction and noise reduction of the signal is carried out by using compressed sensing, and then the signal with multiple degrees of freedom is adaptively decomposed into multiple sets of characteristic scale components by using 2D-VMD. Then, Renyi entropy is used as the optimization index of 2D-VMD analysis performance to extract the effective target intrinsic mode function (IMF) component, reconstruct the dynamics signal in the planetary gearbox, and improve the signal-to-noise ratio. Then, using the full-vector spectrum technique, the homologous information collected by numerous sensors is data layer fused in the spatial domain and the time domain to increase the comprehensiveness and certainty of the fault information. Finally, the Teager-Kaiser energy operator is used to demodulate the potential low-frequency dynamics frequency characteristics from the high-frequency domain and detect the fault characteristic frequency. Furthermore, the correctness and validity of the method are verified by the fault test signal of the planetary gearbox.  
planetary gearbox; fault detection; multi-sensor information fusion; 2D-VMD; compressed sensing +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Selection of Optimal Wavelengths and Multispectral Model Development
Among all of the selected wavelengths, the wavelengths of 1109 nm, 1127 nm, 1203 nm, 1207 nm, 1249 nm, 1252 nm, 1256 nm, 1368 nm, 1464 nm, and 1606 nm were selected more than once by UVE or SPA for individual peanut or walnut contamination, or a combination of contaminated samples by peanut or walnut. A wavelength of 1127 nm was attributed to O-H stretch from carboxylacids [34], a wavelength of 1200 nm was attributed to the C-H stretch (methylene and methyl) second overtone of lipids, starches, and/or proteins [29], 1210 nm was related to the second overtone of a C-H stretch of lipid [35], and 1465 nm was associated with the first overtone of the O-H stretch of water. These wavelengths were respectively close to the selected wavelengths of 1203 nm, 1207 nm, and 1464 nm, which indicated that the feature wavelengths contained the information for the main chemical composition (protein, starch, lipid, moisture) of peanut, walnut, and flours.
# A Heterogeneous Multi-Satellite Dynamic Mission Planning Method Based on Metaheuristic Algorithms
## 2 Model Structure
### PSO Algorithm  
PSO is an intelligent optimization method derived from the foraging behavior of swarms and flocks in nature, such as bees and birds, as illustrated in Figure 3. Its advantages lie in its simplicity and practicality, and it has been developed to a relatively mature state. The PSO algorithm first defines the solution space using dimensions to represent the range of solutions, and it defines a fitness function to indicate the quality of the solutions. Then, it randomly initializes the positions and velocities of each particle. Next, the algorithm systematically moves each particle within the solution space at a certain velocity, dynamically updating the particle's velocity based on its own experience and the experience of the entire swarm, directing it towards the best-known particle position. In PSO, each particle represents a potential solution to the problem we are addressing, and the objective is to move these potential solutions towards the optimal solution. This process is repeated iteratively.  
In PSO, the position and velocity of the particles are updated as shown in the following equations:  
\[\overset{\rightarrow}{v_{t}}^{(k+1)}=w\cdot\overset{\rightarrow}{v_{t}}^{(k )}+c_{1}r_{1}\left(\overset{\rightarrow}{p_{t}}^{(k)}-\overset{\rightarrow}{x _{t}}^{(k)}\right)+c_{2}r_{2}\left(\overset{\rightarrow}{g}^{(k)}-\overset{ \rightarrow}{x_{t}}^{(k)}\right) \tag{1}\]  
\[\overset{\rightarrow}{x_{t}}^{(k+1)}=\overset{\rightarrow}{x_{t}}^{(k)}+ \overset{\rightarrow}{v_{t}}^{(k+1)}\Delta t \tag{2}\]  
Among these, the subscript \(i\) and the superscript \(k\) denote the \(i\)-th particle in the \(k\)-th iteration. \(\overset{\rightarrow}{v_{t}}^{(k)}\) represents the velocity vector of the \(i\)-th particle at iteration \(k\), and \(w\) is the inertia weight factor during the iteration process. \(\overset{\rightarrow}{p_{t}}^{(k)}\) and \(\overset{\rightarrow}{g}^{(k)}\) represent the individual best and global best positions, respectively. \(c_{1}c_{2}\) are weighting coefficients, commonly referred to as learning factors, which indicate the degree to which a particle is influenced by the experience of the entire swarm and its own experience. Their primary function is to guide the particle towards other particles with better positions and to improve itself. Properly setting the learning factors can, to some extent, reduce and prevent optimization from  
Figure 2: Satellites’ multiple coverage.  
getting trapped in local optima, thereby enhancing the overall accuracy of the algorithm. \(r_{1}r_{2}\) are random variables in the range \((0,1)\), which add diversity to the entire swarm.