esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Preprocessing Pipeline  
The Empatica E4 wristband collects the various physiological biosignals at different frequencies. Table 3 provides the frequency for each biosignal. GSR or electrodermal activity (EDA) is a physiological measurement of electricity flowing through the skin in which the amount of sweating can change the skin electrical activity. GSR can be divided into skin conductance level (SCL), representing the slow change background of the GSR, and skin conductance response (SCR), representing the time-varying peaks in response to a stimulus. It is reported in the literature that the peak of the SCR in the GSR signal occurs in reaction to a single stimulus, and appears between 1.5 and 6.5 s after the stimulus [25]; however, the individual's physiological response and the nature of the stimulus can cause deviations from this range.  
Data segmentation is the process of splitting signals into smaller data segments or windows for use in training models. The selection of an appropriate signal window is crucial, as it can affect the accuracy and interpretability of the analysis [26]. We segmented the time series biosignals into windows of 10 s with two-second time steps, which resulted in 39,021 samples for APS and 5967 samples for NS. The mean of GSR, HR derived directly from the BVP using the Empatica E4 algorithm, ST, and three-axis ACC were calculated over each window. The means of the signals for each time window were then used as features for training the XGBoost model. The label of each window was determined by taking the mode of the labels within the window. The dataset was split into training, validation, and testing sets with the following ratios: 80% for training, 10% for validation, and 10% for testing. We employed the stratified shuffle split, a combination of stratified k-fold and shuffle splitting, to maintain the same class distribution in the training, validation, and testing datasets as in the original dataset, thereby eliminating the risk of biased evaluation results due to class imbalance [27]. To ensure that all signals had the same scale, the training, validation, and testing data were standardized by subtracting the mean and dividing by the standard deviation of the training data. Figure 1 shows the overview of the machine learning pipeline, including data collection, data preprocessing, XGBoost model training, and model interpretation using SHAP.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Biosignals** & **Frequency** \\ \hline BVP & 64 Hz \\ ACC & 32 Hz \\ GSR & 4 Hz \\ ST & 4 Hz \\ HR & 1 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 3: E4 wristband physiological signal frequency.
## 6 Evaluation and Experimental Results
### End-to-End Transmission Delay Reduction
#### 6.3.5 Overall E2E IoT Handshake Time Reduction  
For calculating the total E2E handshake time of one handshake packet, we use the following formula:  
\[\text{E2E transmission delay}=\text{T}_{\text{r}}+\text{T}_{\text{p}}+\text{T}_{ \text{q}}+\text{T}_{\text{ps}},\]  
where:  
* Tr is the transmission delay;
* Pr is the propagation delay;
* Tq is the queuing delay;
* Tps is the processing delay.  
Table 11 summarizes the total transmission delays of the handshake obtained during the experiments.  
For a further evaluation of the transmission delay performance during the HIP DEX handshake in the protocol stack, we compare the obtained results, between an IPv4 IoT over HTTP domain and a 6 LoWPAN over CoAP architecture. We observed, on one side, that the reduction of the overall HIP DEX handshake E2E delay is improved when it is executed in a constrained environment where the protocol stack implements protocols standardized especially for constrained and lossy networks. As we implemented 6 LoWPAN, CoAP, and RPL protocols on our network experiment, we deduce that the E2E HIP DEX handshake is minimized by about 32.75% in the WSN based-6 LoWPAN-IoT architecture (before compression) and about 41.37% (after compression), in comparison with WSN based-IPv4-IoT architecture (Results from [9]). On another side, the compression of HIP DEX handshake packets ameliorates the overall E2E delay of the handshake by 12.82% (Results from [9]). Table 12 presents a comparison about the overall E2E LC-DEX's handshake delays with previous works.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Architecture** & **Overall E2E HIP DEX Handshake’s Delay (s)** \\ \hline WSN based-IPv4-IoT & 0.57 \\ WSN based-6 LoWPAN-IoT & 0.39 \\
**(before compression)** & 0.34 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Overall E2E delays of handshake packets.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**HIP Solution** & **Handshake Latency Time (s)** & **Percentage Gain** \\ \hline P-HIP & 1.076 & 70\% \\ [PERSON] et al. [9] & 0.34 & 6\% \\
**(after compression)** & 0.32 & - \\ \hline \hline \end{tabular}
\end{table}
Table 12: Overall E2E delays of handshake packets
# Production Improvement Rate with Time Series Data on Standard Time at Manufacturing Sites
## 2 Related Work
### Average
#### 2.4.3 Harmonic Mean  
The harmonic mean is determined by taking the reciprocal of each data value, which yields the arithmetic mean, and then taking the reciprocal once more. This technique is particularly useful for calculating the average speed over an entire segment based on data for average segment speeds.  
\[H=\frac{1}{\frac{1}{n}\cdot\sum\frac{1}{x_{i}}}=\frac{1}{n}\cdot\frac{1}{(\frac {1}{x_{1}}+\ldots+\frac{1}{x_{n}})} \tag{6}\]  
The following is a simple example of the calculation of each average. Generally, the arithmetic average represents the absolute values of the data, while the geometric average is based on ratios. The arithmetic average is valued for its simplicity and the direct influence of each data point on the average. However, it may not be a suitable indicator for datasets with exceptionally large values. For instance, consider the numbers 2, 4, 3, 6, 5, 9, 8, 13, and 85. The arithmetic average of these numbers is 15, primarily influenced by the presence of the value 85. In such cases, 15 does not adequately represent the dataset. Conversely, the geometric average for these numbers is 7.3, providing a relative concentration index that makes more sense [37].  
In specific scenarios involving rates and ratios, the harmonic mean offers the appropriate average. For example, if a vehicle travels a distance \"d\" outbound at speed \"x\" (e.g., 60 km/h) and returns the same distance at speed \"y\" (e.g., 20 km/h), the vehicle's average speed is the harmonic mean of \"x\" and \"y\" (30 km/h), not the arithmetic mean (40 km/h). This demonstrates that the harmonic mean is the correct measure in cases where rates or ratios are involved. In this example, the total travel time is the same as if the vehicle had covered the entire distance at the average speed.
## 3 Elicitors and Their Classifications
### Mode of Action  
Initially, elicitor was a term used only for molecules that were capable of producing phytoalexins. However, substances capable of triggering any form of defense in plants are now also referred to as elicitors [5]. Recent understanding of the plant immune system shows that the first step of the innate response of plants to elicitors is the perception of the stimulus by pattern recognition receptors (PRRs) located in plants (plasma membranes or within cells) [82; 86]. These pattern recognition receptors (PRRs) detect pathogen-associated molecular patterns (PAMPs) and activate PAMP-triggered immunity (PTI), a local defense mechanism that halts the colonization of pathogens by the induction of mitogen-activated protein kinases (MAPK) [80], the production of reactive oxygen species (ROS), reactive nitrogen species (RNS), ion fluxes, among other defense responses [87; 88].  
In the next phase, signaling molecules such as salicylic acid, jasmonic acid, and ethylene (SA, JA, and ET) trigger reactions to protect the plant from subsequent attacks [5; 14; 80; 82; 83; 89]. Effector-triggered immunity (ETI), unlike PTI, deals with modified pathogens (effector molecules) that act as potential pathogen indicators [82; 83; 89]. Once resistance proteins identify these indicators (effectors), ETI will activate and induce immune responses. The induction of ETI results in the death of tissues in the infected region, preventing the spread of infection to other parts of the plant, a phenomenon known as the hypersensitive response (HR) [90]. As a result, the plant acquires increased resistance to subsequent infections through the signal transduction pathways [82; 90].  
SAR is one of the defense pathways of plants against infectious attacks, and this response is activated and expressed throughout the plant tissues on account of a previous pathogen attack [82; 83; 90]. The induction of SAR occurs through the buildup of salicylic  
Figure 1: Classifications of elicitors.  
acid (SA), a phytohormone that uses the protein Non-Expressor of Pathogenesis-Related Genes 1 (NPR1) to stimulate the expression of pathogenesis-related (PR) genes [83; 84]. Pathogenesis-related (PR) genes are a group of plant defense genes responsible for the accumulation of phytoalexins, the production of pathogenesis-related proteins, cell wall reinforcement, and others [5]. Figure 2 shows the general response mechanisms of plants to elicitors.  
Figure 2: General mechanisms of plant responses to elicitors.
# Optimal Configuration and Path Planning for UAV Swarms Using a Novel Localization Approach
## 1 Introduction
Therefore, the real-time path planning of UAVs is converted to nonlinear optimization with constraints. The interior penalty function method is adopted to convert the nonlinear optimization to simple unconstrained optimization so as to get the flight path of each UAV for the next time. The rest of the paper is structured as follows. Section 2 introduces the TDOA measurement model and distance-dependent noise model. In Section 3, the optimal configuration is analyzed in both static and movable emitter scenarios based on the CRLB. Section 4 presents the optimal UAV path optimization method. Simulations and conclusions are given in Sections 5 and 6, respectively.
# A New Intra-Cluster Scheduling Scheme for Real-Time Flows in Wireless Sensor Networks
## 4 Conclusions  
In this paper, a new intra-cluster scheduling scheme for real-time flow was proposed. In the proposed scheduling algorithm, the IntraSend scheduling utilizes both the IntraSend and IntraRecv time slots if the cluster has outgoing flows. Similarly, if a cluster has only incoming flows, the IntraRecv scheduling algorithm utilizes both the IntraRecv and IntraRecv time slots. In simulation results, we first analyzed the impact of various parameters on our proposed scheme. Then, we compared our scheme to the existing algorithm, and showed that our scheme has more flows delivered within the deadline than the existing solutions. The simulation results showed that the performance of  
Figure 10: IntraRecv time slot utilization.  
Figure 9: IntraSend slot utilization.  
the proposed scheme had a higher acceptance rate than the previous scheme. Further enhancements for our technique may certainly be to enhance our scheduling scheme by delivering more real-time flows within the deadline. In this work, we focused our attention only on an intra-cluster scheduling scheme to schedule the real-time flow within the deadline, by considering interference within the cluster and among the clusters. The proposed scheme can be further enhanced by reducing the number of slots of inter-cluster real-time flow between the cluster head. Importantly, the cluster head signal is too strong to interfere with other cluster headers. Therefore, more time slots are required for the headers nodes to schedule their data between cluster-headers. Thus, to reduce the inter-cluster time slots, it would be efficient for flows in the same cluster that send their message to nodes in other similar clusters to apply some aggregation function and send them in one slot, instead of sending in multiple slots. A second improvement to the scheme could be to apply a compositional real-time scheduling into the proposed frame works. The last improvement to the scheme would be to extend the proposed model to a more realistic model; a routing protocol and distributed approach will be considered for the proposed scheme.  
[PERSON] and [PERSON] conceived of the presented idea and developed the theory. [PERSON], [PERSON] and [PERSON] did simulations and verified the results. [PERSON] wrote the paper and [PERSON] supervised this work. All authors discussed the results and contributed to the final manuscript. All authors have read and agreed to the published version of the manuscript. This research is funded by Portuguese funds by FCT - Fundacao para a Ciencia e a Tecnologia, I.P., under the project UIDB/05105/2020 and Research Fund R19046 of Zayed University, Abu Dhabi, UAE. The authors declare that they have no conflict of interest.
# Authorship Detection on Classical Chinese Text Using Deep Learning
###### Abstract  
Authorship detection has played an important role in social information science. In this study, we propose a support vector machine (SVM)-based authorship detection model for classical Chinese texts. Term frequency-inverse document frequency (TF-IDF) feature extraction technique is combined with the SVM-based method. The linguistic features used in this model are based on TF-IDF calculations of different function words, including literary Chinese words, end-function words, vernacular function words, and transitional function words. Furthermore, a bidirectional long short-term memory (BiLSTM)-based authorship model is introduced to detect authorship in classical Chinese texts. The BiLSTM model incorporates an attention mechanism to better capture the meaning and weight of the words. We conduct a comparative analysis between the SVM-based and BiLSTM-based models in the context of authorship detection in Chinese classical literature. The applicability of the two authorship detection models for classical Chinese texts is examined. Results indicate varying authorship between different sections of the texts, with the SVM model outperforming the BiLSTM model. Notably, these classification outcomes are consistent with findings from prior studies in classical Chinese literary analysis. The proposed SVM-based authorship detection model is especially suited for automatic literary analysis, which underscores its potential for broader literary studies.  
Keywords:authorship detection; support vector machine; TF-IDF; bidirectional long short-term memory; attention mechanism Article
# Renyi Entropy-Based Adaptive Integration Method for 5G-Based Passive Radar Drone Detection
## 3 5G NR Signal Characteristics
When there are no active users (downloading no data), there is almost no signal transmission. As more data traffic is generated by the users, the signal becomes more filled in both the time and frequency domains. This dependency is crucial from the perspective of passive radar effectiveness. Theoretically, both downlink and uplink transmission could be utilized for sensing [34]. Furthermore, particular signals could be considered for matched filter detections, e.g., SSB [35]. In this paper, the authors focus on passive radar based on downlink data transmission because of the relatively large bandwidth possible to be used and because of the known location of the transmitter. The 5G NR standard covers two Frequency Ranges (FR): FR1--below 6 GHz, and FR2--mmWaves. The latter (not deployed yet) promises to unlock even larger bandwidth (up to 2 GHz), which leads to the possibility of even more precise targets' localization, imaging, or recognition using micro-Doppler features.
## 2 Related Works
The number of clusters in each unit is obtained by balancing the total energy consumption of nodes in different units. In addition, by minimizing the total energy consumption of the network, the optimal unit size can be acquired while the optimal number of units is determined. Though the network lifetime is greatly extended by adopting these classical schemes, the bottleneck exists as sensor nodes are energy-limited and can not be recharged in traditional WSNs. Recently, with the development of energy harvesting techniques, this problem can be effectively mitigated or eradicated. In [29], [PERSON] _et al._ present a clustering protocol for prolonging the lifetime of the WSNs with energy-harvesting sensors. A mixed network model is adopted in which both normal sensor nodes and the sensors with energy harvesting abilities are introduced. In addition, the energy-harvesting sensors act as dedicated relay nodes to forward data for CHs. Extensive simulations validate that the proposed protocol can efficiently achieve optimal or suboptimal solutions. An Adaptive Energy Harvesting Aware Clustering (AEHAC) protocol is proposed in [25] for EH-WSNs. Node energy state is taken into CH election mechanism. Then, a sensor node with higher energy harvesting rate or more residual energy has bigger probability to be the CH. Simulation results show that AEHAC maintains available nodes about 15% and network throughput 19% more than LEACH. In [30], a protocol named Energy Potential LEACH (EP-LEACH) is proposed to extend the traditional protocol LEACH to be suitable for EH-WSNs. An Energy Potential Function is utilized to measure sensor nodes' energy harvesting capability. Based on this function, the probability to be CH for each node is designed. Then the nodes with extremely low energy potential will never be chosen as CHs, and nodes with energy potential above a certain threshold will be chosen as CHs with nearly equal probability. Compared with LEACH, EP-LEACH can not only extend network lifetime, but also improve the network throughput in EH-WSNs. In addition, in [20], a distributed Energy Neutral Clustering (ENC) protocol is proposed for EH-WSNs, with the goals of achieving perpetual network operation and maximizing network information gathering. A CH group (CHG) mechanism is adopted to allow that several CHs in each cluster are selected to share the heavy traffic load. This CHG mechanism can prevent the excessive energy consumption of sensors which in turn holds the network-wide energy neutral state, and then the perpetual network operation is achieved. Furthermore,an extension to ENC is proposed to maximize the amount of information gathering, and the optimal number of clusters is mathematically derived using convex optimization. Simulation results show that ENC can provide perpetual network operation and maximize network information gathering. However, the multi-hop inter-cluster communication structure is not considered in this protocol.
## 3 Problem Description and Formulation  
### Problem Description  
This paper aims to design a three-echelon distribution network with \"transfer shortcuts\" by making allocation decisions between different facilities. For the standard form of multi-echelon SCND, interested readers can refer to the studies by [20; 34; 47]. In their research, materials or commodities are processed through a range of echelons and only one node in each echelon can be chosen for a process chain (as shown in Figure 3a); the decisions typically are associated with the facility locations and the allocation between different echelons. In this paper, we consider a three-echelon city logistic distribution network, as shown in Figure 3b, with facilities at fixed locations, i.e., warehouses, DCs, and delivery terminals/stations. Commodities are stored in warehouses (origins) and transported to stations (destinations) via DCs. We need to decide on two pairs of assignment relationship (i.e., warehouses-to-DCs and DCs-to-delivery stations) in the city logistics distribution network, which will greatly affect the overall transportation costs, since we further consider transferring options in the same echelon (DCs) in our problem, which could lead to very difficult assignment relationship (warehouses-to-DCs and DCs-to-delivery stations), and is not covered in the standard multi-echelon SCND reformulation.  
Without loss of generality, the following assumptions are adopted in the underlining mathematical model:  
1. A single sourcing/single path strategy [17] assumes that each warehouse can only be assigned to a single DC and one station can only be covered by one DC.
2. Each delivery station has deterministic demand following a known pattern [6; 17; 49].
3. Transportation cost is expressed as a piecewise linear function of material flow [50] and it is proportional to the volume of freight flows (i.e., transportation costs equal to the product of distance and freight flows in our problem).  
Then, we can state the overall problem as follows: The major input data include the number of warehouses, DCs, stations, locations of facilities, and the connections between DCs. Moreover, other additional given parameters include distances between different facilities, the capacities of DCs, and the demand. The decision variables are related to the assignment of warehouses to DCs and the assignment of DCs to stations. The goal is to minimize the total costs subject to the demand satisfactory constraints under available resources. Table 3 lists the notations used throughout this paper, and the definitions of the decision variables are provided in Table 4.  
Figure 3: Different types of distribution networks.
# Periocular Recognition in the Wild: Implementation of RGB-OCLBCP Dual-Stream CNN
## 5 Experiments
### Experimental Results
#### 5.2.1 Performance Analysis on Proposed Network  
This section analyses the robustness and performance of our network and other networks using Ethnic-ocular database, which reports the experimental results in Table 2.  
Table 2 shows the proposed network achieved the highest Rank-1 and Rank-5 recognition accuracies with 85.03 \(\pm\) 1.88% and 94.23 \(\pm\) 1.26%, respectively. As compared to CNN, this network using the RGB image only achieved the Rank-1 and Rank-5 accuracies of 80.79 \(\pm\) 1.43% and 90.42 \(\pm\) 1.29%, respectively. In addition, CNN using the OCLBCP can only achieved 66.65 \(\pm\) 2.22% and 89.73 \(\pm\) 1.91% for Rank-1 and Rank-5 accuracies, respectively. These results indicate that our network provides more complementary information than CNN. This leads to the proposed late-fusion layers that significantly correlate the RGB image and OCLBCP for achieving better recognition performance.  
Furthermore, we also evaluated the dual-stream CNN without using shared weights. However, this network only achieved 82.09 \(\pm\) 1.59% and 92.11 \(\pm\) 1.32% at Rank-1 and Rank-5 accuracies (see Table 2), respectively. The experimental results prove that the proposed network performed  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Networks**} & \multicolumn{2}{c}{**Accuracy (\%)**} & \multirow{2}{*}{_t.w._2} & \multirow{2}{*}{_flops_3  } \\ \cline{2-3}  & _Rank-1_ & & & \\ \hline CNN 1 with RGB image & 80.79 \(\pm\) 1.43 & 90.42 \(\pm\) 1.29 & 131.1 M & 2.22 GFLOPS \\ CNN with OCLBCP & 66.65 \(\pm\) 2.22 & 89.73 \(\pm\) 1.91 & 131.1 M & 2.22 GFLOPS \\ Dual-stream CNN (using unshared weights) & 82.09 \(\pm\) 1.59 & 92.11 \(\pm\) 1.32 & 250.8 M & 1.90 GFLOPS \\
**Proposed network** & **85.03 \(\pm\) 1.88** & **94.23 \(\pm\) 1.26** & **126.1 M** & **0.90 GFLOPS** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance analysis on the proposed network and other networks with Rank-1 and Rank-5 recognition accuracies. The highest accuracy is highlighted in bold.  
well with at least 2.9% improvement as compared to dual-stream CNN without using shared weight. As can be observed, the shared _conv_ layers and the fusion layers were utilised in the network to aggregate the RGB image and OCLBCP. Thus, the proposed network successfully transformed new knowledge representations to perform better recognition in the wild.  
In Table 2, we also notice the space complexity (total weight number) and time complexity (flops) of the proposed network are significantly smaller than its single network and dual-stream unshared weights networks counterparts while still outperforming them.