esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Implementation
### Libp2p Host
#### 4.5.3 Publish/Subscribe Mechanism  
Pub/Sub [40] is a communications model that allows peers to exchange messages about topics of interest. The subscription to a topic means that the subscribed nodes will receive the messages related to it. Peers can send messages to topics, and each message is distributed to all peers that have subscribed to that topic. Before a peer can subscribe to a subject, it must first locate and connect with other peers via the network. Pub/Sub does not have a built-in method for discovering peers.  
FloodSub [41] was utilized for the development presented in this article. It is a basic implementation of the Pub/Sub paradigm for P2P networks that is intended for usage in decentralized contexts. In FloodSub, all nodes in the network serve as both publishers and subscribers, which means that any node may post messages and subscribe to topics.  
When a node subscribes to a topic, it indicates such a subscription to its neighboring nodes. When a node publishes a message on a subject, all of its neighbors obtain it. These neighbors, in turn, spread the word to their neighbors, and so on. This procedure continues until the word has spread across the whole network.  
FloodSub has the benefit of not having a central server, making it ideal for P2P networks in which all nodes are equal. However, this strategy has drawbacks since it might flood the network and consume excessive bandwidth, reducing the scalability of the protocol.
# A Review of Fabrication Technologies for Carbon Electrode-Based Micro-Supercapacitors
## 4 Micro-Supercapacitor Electrolytes
### Organic Electrolytes  
Organic electrolytes have a wider potential stability window (2-3 V) in comparison to aqueous electrolytes (around 1V). They are formed by dissolving organic salts in organic solvents. The main disadvantages of organic electrolytes include their inferior power performance, their toxicity, and flammability, which limit their application for micro-supercapacitors [117]. Moreover, they are sensitive to moisture, and as a consequence, supercapacitors based on organic electrolytes require ultra-dry storage conditions and a complex assembly process, which increases the fabrication cost.  
A non-aqueous propylene carbonate electrolyte solution with 0.5 M LiClO\({}_{4}\) was tested with 2 wt% of added organic salt for a supercapacitor with electrodeposited polyaniline, resulting in a specific capacitance of 259 F/g and an 80% capacity retention after 1000 cycles [118]. A micro-supercapacitor with a sulfone-based electrolyte, propylene carbonate/sulfolane/LiClO\({}_{4}\)/polymethyl methacrylate, with mixed manganese/vanadium oxide grown onto multi-wall carbon nanotube electrode, had an operating voltage of up to 2 V and an areal capacitance of 11.8 mF/cm\({}^{2}\)[119].
# A Co-Design-Based Reliable Low-Latency and Energy-Efficient Transmission Protocol for UWSNs
## 5 Design of Data Transmission Protocol
### Holding Time Calculation  
The dynamic routing strategy in CDTP provides a selection strategy that contains several neighbors of a node. In our network, the number of hops from the source node to the sink is fixed, and each node in the selection strategy can reach a lower layer. Therefore, for a sender, no matter who the next-hop is, the number of hops to the sink can be guaranteed to be the same. In order to reduce the holding time of nodes and further reduce the end-to-end delay in CDTP, forwarders prefer to elect the node which can forward a packet to its next-hop earliest. In other words, the receiver with the minimal distance from the sender should forward a packet first. The selected receiver under this strategy tries to prevent other neighboring nodes from forwarding the same packet to reduce energy consumption. That is, among the chosen nodes (under selection strategy \(S_{n}\)), the one that is closer to node \(n\) has a higher forwarding priority. Selected forwarders and node \(n\) know the distance information between each other. Then node \(n\) sorts all forwarders based on their distance to itself.  
In Figure 5, \(\{A,B,E\}\) is the optimal selection strategy depending on algorithm 1. According to their distance from node \(n\), the forwarding priority is \(E>A>B\). The holding time that is calculated by these nodes will increase with increasing distance from node \(n\). Node \(E\) will reply CTS immediately after receiving an RTS. Therefore, the holding time of node \(E\) is 0. When nodes \(A\) and \(B\) receive the RTS, node \(E\) should have already received it. If node \(E\) has successfully received an RTS and forwarded CTS, nodes \(A\) and \(B\) should wait until detecting CTS from \(E\) to avoid multiple flying CTS packets. The holding times of \(A\) and \(B\) depend on the distance between \(E\) and themselves. If \(A\) and \(B\) cannot detect the CTS from \(E\), node \(E\) will be considered as a failure, and they will reply CTS on their own. Let \(t_{EA}\) and \(t_{EB}\) express the transmission time between \(E\), \(A\) and \(E\), \(B\). The holding times of \(A\) and \(B\) will be \(t_{EA}+\xi\) and \(t_{EB}+\xi\), respectively, where \(\xi\) is a short protection time. The protection time is used to prevent changes in the distance between nodes due to node movement.  
In short, the node closest to the sender will reply immediately, and the holding time for other nodes is the transmission time between the closest node and themselves plus a short protection time.
## 3 Results and Discussions
### Impacts of Hydraulic Retention Time (HRT) on the ROUR  
HRT also showed significant impacts on the ROUR (Figure 5), which declined with the increasing HRT, from 51.0 \(\pm\) 5.1% to 41.0 \(\pm\) 2.7% (Zn\({}^{2+}\)), 58.0 \(\pm\) 10.7% to 22.0 \(\pm\)5.0% (Ni\({}^{2+}\)) and 32.0 \(\pm\) 5.8% to 16.0 \(\pm\) 2.8% (phenol), respectively. The reciprocal of distance between ROUR and 1.0 (the ROUR value in the absence of toxic substances) was positively correlated with HRT, as expressed in Equation (5). We speculate that the remaining activated sludge activities decreased according to the integral of toxin exposure and linked to the function of HRT, as defined in Equation (6), where ROUR\({}_{t}\) represents the ROUR at time = \(t\).  
\[1-\text{ROUR}\propto[\text{HRT}]^{-1} \tag{5}\]  
\[\text{ROUR}=1-\int_{0}^{HRT}\text{ROUR}_{t}\text{d}t \tag{6}\]  
HRT represents the duration of direct interaction between the activated sludge and toxic substances [48]. As the persistent toxicity causes gradually inactivation of proteins in the active cells [70], the ROUR is dependent on the exposure time or HRT. Previous studies also reported that the increasing HRT led to the decrease of MLSS, EPS concentration, sludge viscosity, and the ratio of food to microorganisms [71]. Therefore, the inhibition of microbial activities in the activated sludge increased with higher HRT.
# Energy Aware Cluster-Based Routing in Flying Ad-Hoc Networks
## 3 EALC Methodology
### K-MeansSortedFitness  
K-Means Sorted Fitness (a variant of K-Means Density) performs the actual clustering and elects CHs. K-Means Density is used along with K-means for initial centroids selection. It computes \"k\" number of optimal centroids location based on the degree of the neighborhood of data points and  
Figure 3: Scenarios where the static weight assignment is incorrect.  
provides them as an input parameter for K-means. These pre-computed centroids allow the K-means to converge very quickly towards an optimal solution.  
In K-Means Density, we have to provide a fixed value for \"k\", whereas in FANETs, we cannot specify the \"k\" value, because the nodes change their position very frequently and due to this change, the network topology also changes. Each network topology requires a different number of clusters based on nodes position and transmission range. In K-Means Sorted Fitness, we consider the transmission range of CHs in order to determine the size and number of clusters.  
K-Means Sorted Fitness takes the fitness value of nodes as input and provides CHs and their associated member as output. To reduce the computational overhead of the clustering function, EALC keeps it simple as much as possible. This function takes decisions based on the fitness value. More accurate the fitness value, more optimal CHs will be elected. Figure 4 and Algorithm 2 illustrates the clustering process. First of all, the fitness value is sorted in decreasing order. A node with the highest fitness will be elected as a CH. All other nodes that are within the transmission range of the selected CH, will become its CMs. The elected and its associated member nodes will then be removed from the remaining nodes. This process of electing CHs is repeated until no remaining node is left behind. At this point in time, all nodes have adopted their role, either as a CH or a cluster member.  
```
1. Input f_val for all \(n\in N\)
2. Output: CHs and their affiliated CMs
3. START
4. Remaining nodes \(\leftarrow\) All nodes
5. WHILE (Remaining nodes!= 0)
6. Sort (f_val) \(CH_{i}\leftarrow\) node ID having highestf_val
7. Member \(\leftarrow\) All nodes in the transmission range of CH\({}_{i}\)
8. Remaining nodes \(\leftarrow\) Remaining nodes--Member
9. END WHILE
10. Returns CHs
11. END
```  
**Algorithm 2** K-Means Sorted Fitness  
Figure 4: Flow chart for K-Means Sorted Fitness for clustering the network.
## 4 Simulation and Results  
### BPMR Channel  
In the system, signal \(a[j,k]\in\{-1,1\}\) is the data stored in the BPMR channel by modulating the user data \(u[k]\in\{0,1\}\). Therefore, the output signal of the channel is calculated as follows [30,31]:  
\[y[j,k]=a[j,k]*c[j,k]+w[j,k], \tag{18}\]  
where \(j\) and \(k\) are the position indices of the magnetic island in the down- and cross-track directions, respectively; \(*\) is the convolution operation; \(a[j,k]\) is the 2D discrete input data,  
Figure 8: Testing model for evaluating the BER performance of the system.  
Figure 7: Training model for calculating the coefficients of the equalizer and target.  
\(c[j,k]\) is 2D channel response, and \(w[j,k]\) is electronic noise modeled as AWGN with zero mean and variance \(\sigma^{2}\).  
\(c[j,k]\) is given by  
\[c[j,k]=P\left(jT_{x},kT_{z}\pm\Delta_{off}\right), \tag{19}\]  
which is a sampling of the following 2D Gaussian pulse response, which is used to describe the 2D island response [20]:  
\[P(x,z)=A\exp\left(-\frac{1}{2q^{2}}\left[\left(\frac{x+\Delta_{x}}{PW_{x}} \right)^{2}+\left(\frac{z+\Delta_{z}}{PW_{z}}\right)^{2}\right]\right), \tag{20}\]  
where \(x\) and \(z\) are time indices in the down- and cross-track directions, respectively; \(A\) is the peak of the 2D Gaussian function; \(\Delta_{x}\) and \(\Delta_{z}\) show the location fluctuation effect; \(q\) is 1/2.3548, and it is the relationship between the standard deviation of a Gaussian function and \(PW_{50}\), which is the pulse width at half of the peak amplitude; and \(PW_{x}\) and \(PW_{z}\) indicate the \(PW_{50}\) component of each direction. Additionally, in this study, we define the TMR for the BPMR system as  
\[\text{TMR}(\%)=\frac{\Delta_{off}}{T_{z}}. \tag{21}\]
## 3 Results
### The Effects of GSL and GPPmax on GPP  
The annual GPP is significantly correlated with the GSL (R = 0.69; \(p<0.05\)) and the GPPmax (R = 0.81; \(p<0.05\)) in China's deciduous forest ecosystems (Figure 4a,d). The annual GPPmax is more strongly correlated with the GPP than the GSL, indicating that the GPPmax might play a more important role in GPP change. For different forest types, the correlation between the GPP and GPPmax in planted forests (R = 0.86; \(p<0.05\); Figure 4e) was higher than that in natural forests during 2001-2020 (R = 0.79; \(p<0.05\); Figure 4f). However, the correlation between the GPP and GSL was similar between natural forests (R = 0.64; \(p<0.05\); Figure 4b) and planted forests (R = 0.65; \(p<0.05\); Figure 4c).  
The contributions of the GSL and GPPmax to the trend in GPP in natural and planted forests are shown in Figure 5. The change in the GSL was determined via the leaf unfolding and leaf falling date. In natural forests, the leaf unfolding date advanced considerably  
Figure 4: The relationships between the annual gross primary productivity (Black dots: GPP) and the growing season length (Green dots: GSL) and maximum gross primary productivity (Orange dots: GPPmax) in China’s deciduous forest ecosystems (**a,d**), and across natural forests (**b,e**), and planted forests (**c,f**), during 2001–2020.  
by \(-0.20\) days/yr, and the leaf falling date was significantly delayed by 0.14 days/yr, extending the GSL during 2001-2020 (Figure 5a). Over the same time period, the leaf unfolding date of planted forests advanced significantly by \(-0.24\) days/yr, and the leaf falling date was delayed significantly by 0.12 days/yr, resulting in an extension of the GSL in the period 2001-2020 (Figure 5b). The differences in the GPPmax were larger than in the GSL between natural and planted forests (Figure 5a,b). Thus, the trend in the GPP was associated with the long-term trend in the GPPmax during 2001-2020.
## 5 Conclusions  
In this paper, we proposed an efficient model to recognize customer sentiment in customer service conversations. The model consists of a conversation text encoder, context semantic encoder, affective interaction encoder, and sentiment classifier. Firstly, the conversation text encoder uses ERNIE pre-trained model to generate the utterance representations. Then, the context semantic encoder uses multiple GRU neural networks and attention mechanisms to encode different contexts to obtain more comprehensive context semantic features. Meanwhile, the affective interaction encoder constructs a directed graph to depict  
Figure 4: The effect of window size on the macro-F1 value.  
Figure 5: The impact of different fusion methods on F1 value.  
the affective interaction relationship between speakers and uses GCN to encode its affective interaction features. Finally, after fusing the encoding of different features, the sentiment classifier outputs the predicted sentiment label. To verify the performance of our model, we select eight baseline models for the same task. Experimental results on the JDDC dataset show that the performance of our model is better than other baseline models in terms of recognizing customer sentiment.  
Although our model captures different speakers' contextual information and inter-speaker affective interaction information that can improve the accuracy of sentiment recognition, our study has certain limitations. The proposed model does not take conversation topics or multimodal data into account. Therefore, the following two research directions merit future exploration: (1) consider more factors that affect the accuracy of sentiment recognition in conversations, such as conversational topics, and (2) incorporate multimodal information into our work.  
Conceptualization, [PERSON], H.L. and J.Z.; methodology, H.L. and J.Z.; software, J.M.; validation, [PERSON], H.L. and J.Z.; formal analysis, H.L. and J.Z.; investigation, H.L. and J.Z.; resources, J.Z.; data curation, H.L. and J.M.; writing--original draft preparation, H.L.; writing--review and editing, Z.H., H.L., J.M. and J.Z.; visualization, J.Z.; supervision, Z.H.; project administration, J.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Cross-border E-commerce Teacher Competence Improvement Project of Guangxi Minzu Normal University \"Research on User Behavior of Cross-border E-commerce Platform under the Background of Artificial Intelligence\" (KJDSKYZD20203); Guangxi University Young and Middle-aged Teachers Basic Ability Improvement Project \"Probability Numbers and its Application in Uncertain Decision Theory\" (2022 KY0764).  
Not applicable.  
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.  
The author is thankful to China Three Gorges University for providing resources for this research.  
The authors declare no conflict of interest.
## 2 Related Work  
### 5G RAT Positioning  
In the field of 5G NR positioning technology [13], significant progress has been made in research and standardization in recent years. As a new generation of mobile communication technology, the development of 5G NR's positioning technology is crucial to meet the demand for accurate indoor and outdoor positioning. [PERSON] et al. [14] provided an in-depth analysis of how 5G-NR can be used for positioning with non-synchronous base stations through the use of PRS to achieve positioning with asynchronous base stations, analyzed in detail the different distribution schemes of the PRS used for positioning in 5G NR systems, and explored the impact of the PRS distribution on the positioning performance with the aim of identifying the optimal PRS distribution scheme to improve the positioning accuracy. In 3 GPP Rel-16 and Rel-17, 5G NR localization techniques have been further enhanced and refined, and [PERSON] et al. [15] provided a comprehensive overview of 3 GPP's work on localization in 5G NR, focusing on the architectural and physical layer aspects, as well as the multiple localization solutions specified in Rel-16, including Uplink/Downlink-Time Difference of Arrival (UL/DL-TDOA), UL-Angle of Arrival (UL-AoA), DL-Angle of Departure (DL-AoD), and Multi-Round Trip Time (RTT) advanced positioning methods, with the ultimate goal of achieving positioning accuracy of less than 3 m. [PERSON] et al. [16] summarized the latest progress of 3 GPP on 5G NR positioning, including the standardized techniques in Release 16 and Release 17, and discusses the research on expanding and improving positioning in Release 18, as well as the future challenges for LOS/NLOS (Non-Line of Sight) identification, the utilization of AoA (Angle of Arrival), and the mitigation of UE/gNB RF (GNodeB Radio Frequency) timing errors. In addition, for algorithms based on single-site 3D localization, [PERSON] et al. [10] explored the feasibility of single-site 3D UE localization using uplink physical layer channel measurement SRSs in 5G NR, and proposed subspace-based joint angular-temporal estimation and statistically based expectation maximization (EM) algorithms. [PERSON] et al. [17] proposed a new algorithm in the integration of Machine Learning (ML) algorithms in 5G radio access networks, discussed the implementation challenges of the solution and the need for architectural changes, and evaluated the performance of the ML-assisted localization approach using deep-learning-assisted UE localization as an example use case.
# Rough-Terrain Path Planning Based on Deep Reinforcement Learning
## 3 Path-Planning Algorithm Design
### Principle of the DQN Algorithm  
The deep reinforcement learning DQN algorithm combines reinforcement learning and neural networks. The DQN [31] is based on the traditional Q-learning algorithm. The goal of Q-learning is to learn an action-value function that assigns a value to each state-action pair. The goal of the DQN algorithm is to approximate the optimal action-value function (Q*(s, \(a\))) using deep neural networks:  
\[Q(s,a,\omega)\approx r_{t}+\gamma\underset{a_{t+1}}{\text{max}}Q*(s_{t+1},a_{ t+1},\omega) \tag{14}\]  
where \(\omega\) represents the neural network weight parameter of the Deep Q-Network. Its loss function is the mean-square deviation of the output value from the target value:  
\[L(\omega)=E[(r_{t}+\gamma\underset{a_{t+1}}{\text{max}}Q*(s_{t+1},a_{t+1}, \omega)-Q(s,a,\omega))^{2}] \tag{15}\]  
The DQN uses Stochastic Gradient Descent (SGD) to update the parameters to achieve the end-to-end optimization objective. The typical DQN uses two networks with the same structure but different parameter update frequencies, an online network for current value estimation and decision making, and a target value network to stabilize the learning process. The parameters of the target network are copied from the current-value network after several rounds of training and are used to compute the target Q-value.
# Time-Dependent Probabilistic Approach of Failure Mode and Effect Analysis
## 2 Time-Dependent FMEA Model (TD-FMEA Model)  
### Modeling Procedure  
The risk evaluation scheme of TD-FMEA proposed in this study is shown in Figure 2. First, probability density functions for occurrence time of failure \(h_{T}(t)\), occurrence time of failure-cause \(h\tau_{T}(t)\), and detection time \(g_{S|T=t}(s)\) are defined. Next, the loss function \(L(s,t)\) is defined. For evaluating the expected loss, it must be set parameter values \(\lambda\), \(\varepsilon\), \(\mu\), \(\alpha\), \(\beta\), and \(\theta\). By integrating the loss function, we deduce the expected loss \(E[L]\). Finally, by multiplying the occurrence rate of failure-cause \(\varepsilon\) by \(E[L]\), we get the expected loss per unit time (_RPM_) for a focused failure-cause.
# Hybrid Local and Global Deep-Learning Architecture for Salient-Object Detection
## 3 Proposed Approach
### Refinement Module  
The refinement module refines the overall saliency map. This module is different from BRB because the latter is used to refine the feature maps and preserve the rich spatial information present in initial layers, while the refinement module refines the predicted saliency map to enhance model accuracy. Predicted saliency map \(S_{p}\) is input for this refinement module. Typically, a refinement module is built as a residual block that purifies the input saliency map through learning residuals between saliency map and its ground truth.  
A refinement module is embedded to improve both regional and boundary limitations in coarse saliency maps. Coarse saliency maps refer to boundary blurriness and the uneven prediction of probabilities in regions. The refinement module consists of an input layer followed by an encoder, a decoder, a link stage between encoder and decoder networks, and an output layer. Both encoder and decoder networks comprise four stages. Every stage consists of one convolutional layer with 64 filters of 3 \(\times\) 3 size. After each convolutional layer, batch normalisation and RELU function are  
Figure 4: Output of various components of proposed approach: (**a**) input image, (**b**) local feature map, (**c**) global feature map, (**d**) predicted saliency map, and (**e**) refined saliency map.  
performed. The link stage also contains a convolutional layer with the same filter and size, after which normalisation and RELU are performed.  
To downsample in an encoder, max pooling is used; for upsampling, bilinear interpolation is used in the decoder. The resulting map is final saliency map \(S^{M}\). Final saliency map \(S_{M}\) is produced by the refinement of the predicted saliency map. The soft-max function computes the probability that a pixel p in a feature map belongs to a salient object:  
\[S^{M}(p)=P(G^{T}(p)=l)=\frac{e^{w_{l}^{L}F^{L}(p)+v_{l}^{L}+w_{l}^{G}F^{G}+v_{l }^{G}}}{\sum_{l^{\prime}e(0,l)}e^{w_{l^{\prime}}^{L}F^{L}(p)+v_{l^{\prime}}^{L }+w_{l^{\prime}}^{G}F^{G}+v_{l^{\prime}}^{G}}} \tag{4}\]  
where ( \(W^{L}\), \(v^{L}\) ) and ( \(W^{G}\), \(V^{G}\) ) are linear operators, and \(G^{T}\) is the ground truth. The loss function is the combination of cross-entropy loss and boundary loss. Figure 4e shows a refined saliency map. The results of the proposed approach on low-contrast images are compiled in Figure 5. The steps involved in encoding and decoding are shown in Figure 6.  
Figure 5: Salient-object detection results of proposed approach on low-contrast images: (**Column 1**) input; (**Column 2**) ground truth; (**Column 3**) proposed-approach results.