esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Digital Modulation Classification Based on Curriculum Learning
## 2 Signal Model and Relative Theories
### Curriculum Learning  
As known, overfitting occurs easily in the application of a neural network, and curriculum learning provides the possibility to solve this problem. Curriculum learning is inspired by the learning principles behind the cognitive processes of human and animal, which usually begin with learning the easy contents and then gradually consider the more complex parts. According to this learning principle, curriculum learning can assign priority to samples of the training set, such as \(\mathrm{D}=\{(x_{1},y_{1}),\cdots(x_{i},y_{i}),\cdots(x_{n},y_{n})\}\), by associating the learning model parameter \(\mathrm{w}\) and the weight of sample in training set \(\mathrm{v}\) as follows [20]:  
\[\min_{\mathrm{w}\in\mathbb{R}^{d},\mathrm{v}\in[0,1]^{n}}\mathbb{F}(\mathrm{w },\mathrm{v})=\frac{1}{n}\sum_{i=1}^{n}\mathrm{v}_{i}L\big{(}y_{i},f(x_{i}, \mathrm{w})\big{)}+G(\mathrm{v};\lambda)+\theta\parallel\mathrm{w}\parallel _{2}^{2}, \tag{4}\]  
where \(\mathrm{x}_{i}\) is the \(i\)th training sample, \(\mathrm{y}_{i}\) is the corresponding label, \(f(\mathrm{x}_{i},\mathrm{w})\) is the discriminative function of a neural network called StudentNet, \(L\big{(}y_{i},f(\mathrm{x}_{i},\mathrm{w})\big{)}\) is the loss function of StudentNet, \(G(\mathrm{v};\lambda)\) represents a curriculum and \(\lambda\) is a variable parameter to tune the learning pace. Although the alternating minimization algorithm is usually employed to minimize Equation (4), it is too complex and requires too much calculation resources. Herein, we employ the scholastic gradient partial descent (SPADE) algorithm [21] based on another neural network named MentorNet to minimize the association of the parameter \(\mathrm{w}\) of StudentNet and the weights \(\mathrm{v}\) of random mini-batch samples, so that the bad local minima can be avoided and the better generalization results can be gained.
## 4 Methodology
### Cooperative Resource Allocation Method Based on Nash Bargaining Game and Lagrangian Optimization  
In satellite networks, due to the interdependence between nodes (satellites, ground stations, and user terminals) and the dynamic changes in the network environment, efficiently allocating limited resources (such as bandwidth and power) to optimize overall system performance becomes a critical issue. Traditional resource allocation methods often assume that nodes make independent decisions, but this non-cooperative strategy tends to lead to suboptimal global outcomes, resembling the \"prisoner's dilemma\" in game theory. To overcome this problem, we propose a cooperative resource allocation model based on game theory, aiming to optimize resource configuration in cognitive satellite networks, particularly under resource constraints and interference limitations.
## 3 Our Proposed Method
### Synthesis of Gene Expression  
The given input data is an observed, discretized gene expression matrix of \(K\times N\) size. Let \(\textbf{v}(t)=[v_{1}(t),v_{2}(t),...,v_{N}(t)]\) be a network state at time step \(t\) represented by a vector of all gene values. Then, the input data are the collection of \(\textbf{v}(t)\) for all observed  
Figure 1: The framework of our method. The observed expression data create the input–target matrices, which represent a pair of consecutive expression vectors to train an autoencoder. The trained autoencoder autoregressively synthesizes expression data. The combined data of the observed and synthesized expressions are used as input for the inference algorithms.  
time steps (\(t=1,2,\ \ldots,K\)). The observed gene expression matrix is transformed into two \((K-1)\times N\) matrices called the input and target matrices for training the autoencoder. The first and second matrices are the collections of \(\mathbf{v}(t)\) ranging from \(t=1\) to \(t=K-1\) and from \(t=2\) to \(t=K\), respectively. This representation can delineate the correlation of gene expression values between two consecutive time steps, enabling the autoencoder to effectively identify the underlying dynamics. After the training process is finished, the autoencoder model can be used to generate unobserved discrete gene expression data after the last observation time step \(K\). At the first iteration of the generation process, \(\mathbf{v}(K)\), the network state at the last observation time step is given as an input to the model, and the model produces an output \(\mathbf{v}^{\prime}(K+1)\), which is used as an input for the output \(\mathbf{v}^{\prime}(K+2)\) and so on. This process is repeated for a desired time step. The structure of the autoencoder is presented in Table 1. We chose the set of parameters that provides the best results, as presented in Table 2. The model structure and hyperparameters were selected through extensive empirical tuning considering the specific characteristics of biological gene expression data and the demands of network reconstruction tasks. A symmetric three-layer architecture was chosen to provide sufficient capacity for capturing the complex nonlinear relationships commonly observed in gene regulatory networks while avoiding overfitting typically observed in small biological datasets. ReLU activation functions were used in the hidden layers due to their ability to efficiently learn sparse representations. This aligns with the biological assumption that only a subset of genes is co-regulated at a time. In contrast, alternatives like sigmoid and tanh introduced saturation effects and slower convergence. Linear output activation preserves the full dynamic range of gene expression values (between 0 and 2). The learning rate and training epochs were dynamically adjusted to accommodate datasets of varying size and complexity, ensuring stable convergence without sacrificing generalization. These design choices were made to optimize the model's ability to generate biologically plausible data and support the accurate inference of regulatory interactions.
# Improving Passive Time Reversal Underwater Acoustic Communications Using Subarray Processing
###### Abstract  
Multichannel receivers are usually employed in high-rate underwater acoustic communication to achieve spatial diversity. In the context of multichannel underwater acoustic communications, passive time reversal (TR) combined with a single-channel adaptive decision feedback equalizer (TR-DFE) is a low-complexity solution to achieve both spatial and temporal focusing. In this paper, we present a novel receiver structure to combine passive time reversal with a low-order multichannel adaptive decision feedback equalizer (TR-MC-DFE) to improve the performance of the conventional TR-DFE. First, the proposed method divides the whole received array into several subarrays. Second, we conduct passive time reversal processing in each subarray. Third, the multiple subarray outputs are equalized with a low-order multichannel DFE. We also investigated different channel estimation methods, including least squares (LS), orthogonal matching pursuit (OMP), and improved proportionate normalized least mean squares (IPNLMS). The bit error rate (BER) and output signal-to-noise ratio (SNR) performances of the receiver algorithms are evaluated using simulation and real data collected in a lake experiment. The source-receiver range is 7.4 km, and the data rate with quadrature phase shift keying (QPSK) signal is 8 kbits/s. The uncoded BER of the single input multiple output (SIMO) systems varies between \(1\times 10^{-1}\) and \(2\times 10^{-2}\) for the conventional TR-DFE, and between \(1\times 10^{-2}\) and \(1\times 10^{-3}\) for the proposed TR-MC-DFE when eight hydrophones are utilized. Compared to conventional TR-DFE, the average output SNR of the experimental data is enhanced by 3 dB.  
underwater acoustic communications; passive time reversal; adaptive multichannel equalization; channel estimation +
Footnote †: journal: Sensors
## 2 Materials and Methods
### Liquid Chromatography-Mass Spectrometry Analysis (LC/MS)  
The standard compounds of caffe acid (CA) and rosmarinic acid (RA) were purchased from Tokyo Chemical Industry Co., Ltd. (Tokyo, Japan) and Sigma-Aldrich Co. LLC (St. Louis, MO, USA), respectively. Samples and standards were dissolved in methanol. Ultra-performance liquid chromatography (UPLC) was performed on an ACQUITY UPLC system (Waters, MA, USA) with an ACQUITY binary solvent manager pump (ACQ-BSM), an ACQUITY PDA detector (ACQ-PDA), and a Waters micromass ZQ spectrometer (Waters) coupled with an electrospray ionization (ESI) interface and an ion trap mass analyzer. Samples and standards were analyzed under the following conditions: column, ACQUITY UPLC BEH Shield RP18 (2.1 \(\times\) 100 mm, 1.7 \(\upmu\)m; USA); mobile phase, distilled  
Figure 1: Effect of TQ extract on the nasal symptom score of ovalbumin (OVA)-induced AR mouse. (a) Schematic diagram of the experiment. The number of mice rubbing motions (b) and nose sneezing (c) were measured for a 10-minute period. Normal group: Blank; OVA-induced AR group: AR; 10 mg/kg TQ 50% EtOH extract treated AR group: 50E 10; 100 mg/kg TQ 50% EtOH extract treated AR group: 50E 100. Values are presented as mean \(\pm\) SEM (\(n\) = 6 in each group). # \(p\) < 0.05 compared with Blank group; * \(p\) < 0.05 compared with AR group.  
water (solvent system A) and acetonitrile (CH\({}_{3}\)CN, solvent system B) in a gradient mode (B 3% from 0 to 10 min, B 30% from 10 to 15 min, B 100% from 15 min to 18 min, B 3% from 18 to 20 min); sample injection volume, 5 \(\mu\)L; flow rate, 0.3 mL/min; column temperature, 40 \({}^{\circ}\)C, UV wavelength, 325 nm. Conditions for MS analysis in the negative ion mode were as follows: capillary voltages, 3.30 kV; cone voltage, 50 V; extractor voltage, 3 V; RF lens voltage, 0.2 V; source temperature 120 \({}^{\circ}\)C, desolvation temperature 300 \({}^{\circ}\)C, gas flow desolvation 600 L/h, gas flow cone 30 L/h.
## 6 Performance Evaluations
### Simulation Results
#### a.1.1 Average end-to-end delay evaluation under the first scenario  
This set of simulation experiments examines end-to-end delay at varying traffic rates and sink node numbers for the first scenario.  
First simulation experiment analyses end-to-end delay with average traffic rate. This experiment varied traffic from three to 11 packets per second using three sink nodes. Traffic volume affects end-to-end delay (Figure 16). The proposed routing method has the lowest end-to-end delay compared to SMRP and EERP because it picks the forwarding node that can transmit data packets within their deadlines based on relaying speed. It also picks forwarding nodes with a more stable connection to prevent packet loss and resend, reducing delivery delay.  
In the case of SMRP and EERP routing algorithms, packets cannot bypass the lossy links, which results in an increase in the end-to-end latency due to the retransmission of lost packets. In addition, they do not support real-time communications, leading to late delivery of data.  
The second experiment examines end-to-end delay with varied numbers of sink nodes. To test this variation, the number of sink nodes in the network varies from one to three, while traffic is fixed at three packets per second. Figure 17 depicts end-to-end delay variation for the first case with varying sink nodes. End-to-end delay reduces as sink nodes increase, as indicated in the figure. The delivery delay reduces as the average distance to sink nodes decreases. The proposed algorithm has the lowest end-to-end delay. The algorithm picks relaying nodes depending on speed, enhancing real-time data delivery. Improves packet delivery across unstable networks, reducing packet loss and retransmission delays.  
Figure 15: Influence of increasing the number of sink nodes on network lifetime under the second scenario.  
Figure 16: Influence of increasing average traffic rate on average end-to-end delay for the first scenario.  
Figure 17: Influence of increasing number of sink nodes on average end-to-end delay for the first scenario.  
SMRP and EERP do not include packet loss due to wireless connections' dynamic nature. This increases packet loss, which delays delivery due to retransmission. They do not improve real-time data transmission.
# Performance Analysis of Time Synchronization Protocols in Wireless Sensor Networks
## 3 Time Synchronization in WSNs
### Gradient Time Synchronization Protocol  
GTSP [11] is designed to optimize the synchronization error between neighboring nodes and is a completely distributed protocol that is based on the average consensus algorithm. Each node synchronizes with its neighboring nodes and no special reference node exists. Unfortunately, this property prevents GTSP from synchronizing to an external time source (e.g., UTC time) [16]. Similar to FTSP, GTSP uses a MAC layer timestamp technique and one-way message dissemination. GTSP avoids single points of failure and can dynamically adapt to topology changes. The researchers that proposed GTSP reported that the average synchronization error between neighboring nodes (4.0 us) is slightly smaller than that of FTSP (5.3 us), while the network synchronization error is higher.
# An Integrated Decision Support System for Low-Disturbance Surface Mining
## 2 Methodology
### Integrated Methodological Approach  
In this study, the F-AHP, grey clustering method, and F-TOPSIS method are combined. The detailed steps of the synthesized methodology are shown in Figure 3. This step also reflects in detail the logical relationship between the model and the data, and the key data calculated through the model will be shown in the Appendix A of this paper.  
Step 1: Setting up the evaluation standard  
A total of 4 primary and 13 secondary indicators were analyzed in terms of both realism and desirability. Expert consultation was used to conduct the evaluation.  
Step 2: Determining weights of indicators  
F-AHP was used to determine the relative weights of the indicators through a two-by-two comparison matrix. The two-by-two comparison matrix for each indicator is shown in Table 1 (in Appendix A).  
Step 3: Deciding grey levels  
The indicators were categorized into five LD levels: very low, low, medium, high, and very high. Using a 5-point scale, experts assessed each indicator based on aspects of reality and desirability.  
Step 4: Defining grey winterization weight functions  
The center-point triangular winterization weight functions (CTWWF) were used to calculate the grey clustering coefficients [23]. The maximum value of the weighted grey clustering coefficients was selected as the clustering result.  
Step 5: Judging the LD levels of indicators  
The maximum value of an element in the grey clustering coefficients is selected as the clustering result.  
Step 6: Sort the paths using the F-TOPSIS method.  
The F-TOPSIS method was used to calculate the comparative advantages of the four possible paths and to analyze the ranking of the optimal paths involving the four leading indicators.  
Details of the procedures for F-AHP, grey clustering method, and F-TOPSIS are given below.
# RTK with the Assistance of an IMU-Based Pedestrian Navigation Algorithm for Smartphones
## 4 Conclusions and Future Work  
In this paper, we tested the performances of single-frequency RTK and dual-frequency RTK on the Xiaomi Mi 8 and compared the performance of RTK based on the Xiaomi Mi 8 with that based on the NovAtel receiver. Both single-frequency RTK and dual-frequency RTK could provide fixed solutions in static mode. The introduction of the dual-frequency RTK reduced the convergence time and improved the fixed rate in static mode. When it comes to dynamic mode, dual-frequency RTK with GPS and Galileo measurements suffered from the least biases and fluctuations. The introduction of Galileo satellites helped provide more continuous positioning results because of the increasing number of visible satellites. The fixed rate of dual-frequency RTK based on the Xiaomi Mi 8 in dynamic mode did not rise significantly compared with single-frequency RTK based on the Xiaomi Mi 8.  
We also provided a method to assist RTK with an IMU-based pedestrian navigation algorithm for smartphones. We combined RTK with the Madgwick algorithm and the ZUPT aiding INS to provide more continuous positioning results. The experiment showed that RTK with the assistance of IMU could provide reliable positioning results during GNSS outage, which was much more reliable and adaptable to complex environments than RTK without the assistance of an IMU-based pedestrian navigation algorithm. The experiments verified the feasibility of providing continuous positioning results in both GNSS-allowed and GNSS-challenged environments with a Xiaomi Mi 8.  
Assisting RTK with an IMU-based pedestrian navigation algorithm is still in the proof-of-concept stage. Experiments verified the feasibility of our method. However, further work is needed to assess our method's performance more accurately. In the future, we will conduct experiments to test the method's accuracy and stability in different environments. Besides, we will obtain the GNSS measurements and measurements of IMU with our app and develop a GNSS/INS with a loosely-coupled or tightly-coupled integration system based on smartphones to raise the anti-jamming capability of RTK. Considering tying smartphones to the pedestrian's foot is inconvenient and not practical for pedestrians, we will also assist RTK with other algorithms during GNSS outage so that continuous positioning results can be provided with a handheld smartphone.  
Conceptualization, Z.N. and P.N.; data curation, Z.N., L.T., and J.S.; formal analysis, Z.N., L.T., and J.S.; software, Z.N.; writing, original draft, Z.N.; writing, review and editing, Z.N. and B.Z. This research was funded by the National Key Research and Development Project of China Grant Number 2016 YFB0502100. The authors declare no conflict of interest.
# Optimal Data Collection Time in LoRa Networks--A Time-Slotted Approach
## 1 Introduction
Both schedule computation and dissemination are two very challenging problems because (a) the schedule has to be as efficient as possible in terms of data collection time, (b) as compact as possible in terms of size, and (c) to be transmitted to the nodes as quickly as possible over low-rate and duty cycle-restricted wireless links. In our previous work [11], we investigated the gains of using two centralised scheduling algorithms when the number of nodes and their transmission settings (e.g., reliable SFs) are known. The proposed algorithms assign SFs and allocate time slots for transmissions, aiming at generating collision-free schedules with the minimum possible data collection time. The missing parts of this work were that (a) no theoretical background of the Aloha-based data collection methods was provided. (b) The proposed heuristics did not provide any performance guarantees while there was no comparison with the optimal solution even for small-scale scenarios, (c) we neglected the assessment of the transmission of the schedule to nodes which constitutes one of the major problems in LoRa networks scheduling, and finally, (d) the solutions were not experimentally validated. Hence, in this paper, we extend our previous work to add the following contributions:  
1. We describe the weaknesses of an Aloha transmission policy for efficient bulk data collection and we formulate the problem of finding the minimum transmission rate to achieve a certain success probability of packet delivery for each node. This transmission rate provides the lower bound of the bulk data collection time in Aloha approaches. 2. We formulate and solve the optimal time-slotted scheduling problem that minimises the data collection time in LoRa-based networks. We implement a prototype of the proposed approach in a proof-of-concept to assess the practicality and performance of the schedule dissemination time and the ability of the nodes to compute the schedule with their own resources. The rest of the paper is organised as follows: Section 2 surveys recent related works. Section 3 introduces the bulk data collection problem and describes the limitations of the Aloha MAC in this context. In Section 4, we present preliminary information regarding time-slotted communications and in Section 5 we formulate the optimal scheduling optimisation problem. Section 6 presents theoretical and simulation results, while Section 7 describes how a proof of concept was realised along with experimental results from the testbed. Finally, Section 8 concludes the paper and presents ideas for future directions.
## 4 Receiver-Triggered Handshake Protocol
### Receiver-Triggered Handshake Protocol  
The receiver-triggered handshake protocol reflects the technical elements (1) to (6) discussed in Section 4.1. Each carrier terminal has its own carrier ID. Three types of bundle are exchanged: search message bundle (Sm), response message bundle (Rm), which contains safety confirmation information, and acknowledgement message bundle (Am). Only carrier terminals send Sm. A carrier terminal sends Sm every T seconds. A victim terminal that has received an Sm sends back an Rm to the sender. The following information is added to the header of an Sm or an Rm.  
Sm: _stime_ and _buf_.  
Rm: _carrierID_, _sendtime_, and _gps_.  
_Stime_ contains the timestamp of the time when the carrier terminal concerned was connected to a shelter terminal the last time. _Buf_ contains information about the current buffer usage. _Sendtime_ contains the time when the victim terminal concerned sent Rm. _Gps_ contains information about the victim terminal's current location.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Roles of nodes**} & **Number of copies made by a source terminal** & **Number of copies made by a relay node** & **Number of hops before a message reaches the destination** & **Method of deleting copies** & **Trigger for communication** \\ \hline Proposed protocol & Differentiated & Limited & Unlimited & Unlimited & Acknowledge bundle & Receiver \\ _Epidemic_ & Not & Unlimited & Unlimited & Unlimited & TTL & Sender \\ _Routing_ & differentiated & & Limited & Limited & TTL & Sender \\ _Spray and_ & Not & Limited & Limited & Limited & TTL & Sender \\ _Wait_ & differentiated & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Technical differences between the proposed protocol and the existing routing methods.