esa-sceva/satcom-chunk-collection huggingface dataset:
# Traffic Monitoring System Based on Deep Learning and Seismometer Data
## 2 Methods  
Neural networks, the main backbone for machine learning, operate in a way that is analogous to biological processes in that the connectivity pattern between neurons resembles the organization of the animal visual cortex [32]. Neural networks use little preprocessing compared to other classification algorithms. This means that the network learns its optimal processing filters, which are manually prepared in traditional algorithms. This independence from prior knowledge and human effort in feature design is a major advantage. Consequently, neural networks can efficiently find relationships between a set of input raw data (in this case, seismic waveforms) and the desired output value (vehicle class probabilities).  
Neural networks consist of three main components: neurons, weights, and bias. In a feedforward process, the neurons are determined by the values of the previous input and the weights variable that connect previous inputs to the neuron as shown in Figure 1. Bias is an independent variable that acts as a refresher that perturbs the function by adding a constant. The output Y of all neurons can be calculated as follows:  
\[Y=f\Big{[}\sum_{1}^{n}(X\times W)+b\Big{]} \tag{1}\]  
where \(n\) is the number of neurons in the previous layer, \(X\) is the value that the neuron holds, \(W\) is the weight that connects \(Y\) with \(X\), and \(b\) is the bias. The nonlinearity activation function \(f\) can be changed depending on the application of the neural network. To ensure a fair comparison of the three neural network models we evaluated in this study, we adopted the rectified linear unit (ReLU) [33] as an activation function after all layers. The ReLU equation returns all negative values to zero and keeps positive values:  
\[f(Y)=max(0,\ Y) \tag{2}\]  
Figure 1: A simple neural network illustrating Equation (1). Inputs (\(x\)) multiplied by weights (\(w\)) are summed in the dense layer, adding bias (\(b\)), then the activation function (\(f\)) is applied to get the output.  
Neurons are usually stacked in groups called hidden layers. The simplest neural network contains a single hidden layer and an output layer with a single neuron. In this study, we used three different models with complex architectures designed to classify data in the time domain. Each candidate architecture had its weights and bias optimized in a training process via back-propagation. In all three models, the output of the last layer was subjected to the _SoftMax_ function [34] to normalize the probabilities by the following:  
\[S(N)=\frac{e^{N}}{\sum_{n}e^{N_{n}}}, \tag{3}\]  
where \(N\) is the value of the output layer and \(n\) is the number of neurons in the output layer. Table 1 lists the specifications of the three models.
## 2 Materials and Methods
### Unsupervised Classification Methods  
We used unsupervised machine learning classification algorithms, which included hierarchical clustering (HCL) with the unweighted pair group method with arithmetic mean (UPGMA) and k-means clustering. Both methodologies were applied to the set of DEGs, in order to unravel expression patterns, as well as common expression profiles across all UBC samples. The k-means algorithm was applied with 100 iterations and the optimal cluster number for the \(k\)-means algorithm was estimated using the Calinski-Harabasz criterion. We complementarily used the Davies-Bouldin algorithm for detecting the optimal number of clusters [26].
# Inference Acceleration with Adaptive Distributed DNN Partition over Dynamic Video Stream
## 3 Preliminary
### Hierarchical Prediction Model
#### 3.1.1 Estimation on Inference Delay  
In the process of collaborative device inference, the model partitioning configuration relies on the accurate estimation of the model inference time. Methods exist to execute DNN models online and determine the inference time; however, due to the dynamic nature of idle computing resources, it is time-consuming and inaccurate to execute each layer and count the inference time on end-devices and edge servers. To solve this problem, we used a regression model that considered the allocatable computing power of devices with DNN parameters to estimate the inference time of each layer of the DNN. The DNN parameters included the data dimension and arithmetic type of each layer. According to [PERSON] [24], the inference time of the DNN model mainly comes from floating-point operations (FLOPs) and data access. The FLOPs are mainly present in the convolutional layer, fully connected layer, normalization layer, etc. They are related to the input size, the number of neurons, and step length. The data access process is prevalent in each layer of the DNN and is related to the data size. The DRAM Traffic (DT) is the size of bytes required to access the storage unit during the model computation, which reflects the model's demand for storage unit bandwidth. In access-intensive operators such as ReLU, Concat, etc., access traffic significantly impacts the model inference time.  
Specifically, we defined the theoretical computational peak of device \(D_{i}\) as \(F_{i}\) and the maximum memory bandwidth as \(MMB_{i}\). The height, width, and number of channels of the input and output data were defined as \(H_{in},W_{in},C_{in},H_{out},W_{out},C_{out}\), respectively. For convolutional layers, the FLOPs are also related to the step size \(s\) and the size \(K\). For computationally intensive operators, the computation can be expressed uniformly as \(FLOPs=H_{out}\cdot W_{out}\cdot C_{in}\cdot K^{2}\cdot C_{out}\). For the access-intensive operator, the computation is negligible, and the DT can be expressed as  
\[DT=\alpha\cdot H_{in}\cdot W_{in}\cdot C_{in}+\beta\cdot H_{out}\cdot W_{out} \cdot C_{out}+\gamma\cdot C_{in}\cdot C_{out}\cdot K^{2}. \tag{1}\]  
The values of \(\alpha\), \(\beta\), and \(\gamma\) represent the number of times each operator needs to access the input, output, and operator data (such as convolution kernel) during the calculation process. The details are shown in Table 1.  
We modeled the regression for the computation time of each layer in DNN with \(F_{i}\), \(MMB_{i}\), \(FLOPs\), and \(DT\) as input parameters. The inference time can be expressed as  
\[T_{infer}=\text{Func}(F_{i},MMB_{i},FLOPs,DT). \tag{2}\]  
Figure 2: Model hierarchical inference time.  
We tested the model inference latencies for all layers on the ResNet family of feature extractors, including inputs of different lengths in the W dimension. Figure 3 shows that the actual processing time of our proposed regression model is similar to the predicted processing time.
## 4 Testing and Comparison of Error Detection Capability  
### Attacker Model  
Since the emergence of fault injection, various attack methods have been created to inject fault into semiconductors [41], which makes it hard to define the fault model. Referring to the relevant literature, this paper puts forward the following common hypotheses on the capability of attackers: (1) Attackers are incapable of directly invading, modifying, or rebuilding the circuit structure, using powerful fault injection techniques like a focused ion beam [42]. It is a most powerful assumption of the capability of attacker. If attackers are capable of rebuilding the circuit, then our secure architecture is not able to resist this attack. However, the attack is high-cost and is hard to carry out in practice. It needs  
Figure 4: The logic diagram of \(\text{ACP}_{\text{K}}\).  
Figure 5: The logic diagram of \(\text{CPP}_{\text{K}}\).  
very expensive consumables and a strong technical background [24]. (2) Attackers are incapable of tampering with the clock signal [10]. Although tampering with the clock signal is a viable option for an attacker, it is a common assumption for a secure architecture designer because the designer is usually focused on protecting the data path of the chip but not the clock. (3) Attackers are capable of injecting a fault into either MFM or EDM, but not both at the same time. However, the probability that a fault is injected into MFM and EDM at the same time and escape from a comparison result is very small in practice. (4) Attackers are incapable of controlling the error pattern in the MFM output. It is also a strong assumption of the capability of an attacker, that if an attacker is capable of injecting a fault in MFM and causes an error pattern that our architecture cannot detect, our secure scheme will not work.
# Transpiler-Based Architecture Design Model for Back-End Layers in Software Development
## 3 Transpiler-Based Design Model for Back-End Layers
### Detailed Design
#### 3.4.7 Business Layer  
This layer is dedicated to the implementation of solution-specific methods by the developers. This is the only layer in which developers have direct involvement and can incorporate their source code. The following components are involved in this layer:  
* Business Logic This layer serves as the core of the architecture, and is where software developers can customize and integrate the necessary algorithms for the operation of an enterprise platform or information system. It is responsible for processing and determining business rules and conditions based on business objects and the data access layer. The implementation of business logic is achieved through methods that are specifically marked to be exposed to the front-end. The development team should primarily focus on this layer; as the other layers are automatically generated, this will lead to reduced development time.  
Figure 8: Database engine support.  
Figure 9 depicts the sequence for exposing a method. The sequence begins with a single transpilable business logic method during the coding stage, which is then transformed into multiple logic method libraries, each in a different programming language. The post-build utilities and preparation processes ready these artifacts for native exposure as well as to facilitate automatic method exposure in various service exposure types, such as SOAP XML web services, API Rest, TCP, or even cloud-based exposures. Ultimately, based on exposure compatibility and technology considerations, the service endpoints can be deployed across different application servers, allowing the developed business logic methods to be invoked as HTTP-based methods. The exposures, programming languages, and application servers shown in the diagram are representative examples to aid comprehension; additional languages, exposure types, or servers can be incorporated without limitations depending on the scope of nest implementation.
* Business Objects Business logic methods use input and output parameters. The parameters can be directly taken from the reference to auto-generate objects in the business data access layer. However, in several scenarios it is necessary to generate custom objects that are designed to be a composition of other objects or to extend existing object. These types of custom objects serve to process information in business logic or as a way to transport complex input or output parameters. The development team carries out the component.
* Business Services This layer is responsible for automatically generating the method exposure marked in the business logic that is to be included in the nest implementations. It acts as the central point where calls are received, and supports routing and request management throughout their lifecycle. This creates a single connection point for the nest implementation.
# Research and Application of a Multi-Agent-Based Intelligent Mine Gas State Decision-Making System
## 3 Hybrid Multi-Agent Orchestration Algorithm
### Problem Definition  
In the Intelligent Mine Gas State Decision-Making System, multiple agents need to collaborate to complete tasks such as data collection, analysis, judgment, and report generation. Each agent possesses various capabilities, such as sensor data collection, alarm assessment, and report generation, as well as different resources. The goal of the hybrid multi-agent orchestration algorithm is to maximize task execution efficiency and resource utilization while satisfying task requirements and resource constraints.  
Assume a network consisting of \(N\) agents, where each agent possesses unique capabilities and resources. A workflow comprising \(K\) tasks needs to be executed, with each  
Figure 1: Comparison of centralized, decentralized, and hybrid systems.  
task requiring specific types and quantities of resources. Moreover, each task is associated with a metric for token consumption and system stability. The objective is to determine a task allocation scheme that optimizes the overall quality in terms of execution time, token consumption, and system stability. Therefore, the objective function can be formulated as follows:  
\[\begin{split} Minimize\ Z=\alpha\cdot\sum_{i=1}^{N}\sum_{j=1}^{K}t_{ ij}\cdot X_{ij}+\beta\cdot\sum_{i=1}^{N}\sum_{j=1}^{K}C_{ij}\cdot X_{ij}- \gamma\cdot\\ \sum_{i=1}^{N}\sum_{j=1}^{K}P_{ij}\cdot X_{ij},\end{split} \tag{1}\]  
where:  
\(N\) denotes the number of agents, and \(K\) denotes the number of tasks;  
\(t_{ij}\) represents the time needed for Agent \(i\) to perform task \(j\); \(X_{ij}\) indicates the degree of contribution of Agent \(i\) to task \(j\); \(C_{ij}\) denotes the Token consumption of Agent \(i\) for task \(j\); \(P_{ij}\) signifies the system stability of Agent \(i\) during the execution of task \(j\).  
\(\alpha\), \(\beta\), and \(\gamma\) are weight parameters that balance the importance of execution time, token consumption, and the accuracy of the generated results, respectively.  
The constraints are as follows:  
Task Allocation Constraint: Each task must have at least one agent assigned to it.  
\[\sum_{i=1}^{N}X_{ij}\geq 1\quad\forall_{j}\in\{1,2,\ldots,K\}, \tag{2}\]  
Agent Resource Limitation: The total resources allocated by each agent across all tasks must not exceed the resources they possess.  
\[\sum_{i=1}^{N}R_{ij}\cdot X_{ij}\leq A_{i}\quad\forall_{i}\in\{1,2,\ldots,N\}, \tag{3}\]  
Here, \(A_{i}\) represents the capabilities and resources of Agent \(i\), and \(R_{ij}\) denotes the types and quantities of resources required by Agent \(i\) to execute task \(j\).  
Non-Negativity Constraint: The contribution level of an agent to a task must be non-negative.  
\[X_{ij}\geq 0\quad\forall_{i}\in\{1,2,\ldots,N\},\ \forall_{j}\in\{1,2,\ldots,K\}, \tag{4}\]
## 3 Results
### Resource Efficiency Metrics
#### 3.2.2 Microcontroller Memory Usage  
In Table 3, SRAM/SDRAM memory usage is evaluated exclusively for LiteRT DNN models deployed on the STM32F7 microcontroller, as the Raspberry Pi 4 utilizes only a negligible portion of its ample 8 GB SDRAM capacity. The non-quantized LiteRT DNN model occupies 235 kB of SRAM (45% of the 512 kB available) and 203 kB of SDRAM (2.5% of the 16 MB available) on the STM32F7 microcontroller. In comparison, the quantized IntQ and Full-IntQ LiteRT models require almost the same amount of SRAM--214 kB (41% of 512 kB)--but considerably less SDRAM, consuming only 55 kB (0.66% of 16 MB) due to the use of int8 data.
# Integrated Optical Phased Arrays for Beam Forming and Steering
## 7 Performances
### Power Consumption
This power was one-order smaller than existing solid-state systems at such ranges [144].
# Application of Compressive Sensing in the Presence of Noise for Transient Photometric Events
## 3 Noise in Compressive Sensing Measurements
### Measurement Noise  
Measurement noise is associated with the data acquisition process at the detector front end. For measurement noise, we model photocurrent shot noise using Poisson distribution. This modeling can be generalized to any type of noise, which requires applying Poisson noise to the data measurements.
## 4 Numerical Results and Discussions
### Network Performance in Changing of Required SINR and Required Harvesting Energy Values
Moreover, we utilize relative error, which is a measure of the uncertainty of measurement to evaluate the variations between the results provided by the deep learning-based approaches and the optimization algorithm. For example, in Figure 7a at required SINR is 5 dB, relative errors of FFNN, NARX, LRN, and LSTM compared to optimization algorithm are 0.2%, 0.58%, 2.1%, and 0.69%, respectively. In Figure 7b at required SINR is 5 dB, relative errors of FFNN, NARX, LRN, and LSTM compared to optimization algorithm are 5.6%, 5%, 6.1%, and 0.35%, respectively. Simulation results in Figure 7a,b also show that LSTM provides the best performance in term of transmit power. Figure 9 shows the sum of transmit powers while changing the required harvested energy in the training and testing stages. In Figure 9, we can see that the change of required harvesting energy threshold of receiver will affect the minimum transmit power of transmitter. The higher the required harvesting energy, the more power the transmitter needs to transmit, which makes the receivers avoid to interrupt the communication due to the lack of power. In Figure 9a, the deep learning-based approaches can capture responses similar to the response of the optimization algorithm in the training stage with relatively large samples. However, with a smaller number of samples in the testing stage and the relatively large fluctuation of transmit powers in the testing data, the deep learning-based approaches cannot effectively capture the response of optimization algorithm as shown in Figure 9b. We also calculate relative errors of FFNN, NARX, LRN, and LSTM compared to optimization algorithm in Figure 9a at the required harvested energy \(e_{k}=-14\) dBm, which are 0.05%, 0.3%, 1.83%, and 0.06%, respectively. In this case, LSTM also provides low relative error in most of deep learning-based approaches. Figure 10 shows the average PS ratios according to the required harvested energy in the training and testing stages. Overall, the schemes mostly have a downward trend in the PS ratio as the required harvested energy increases. That is, the PS ratio should be decreased to guarantee that the energy harvested by EH is utilized more efficiently. In Figure 10a, FFNN, LRN, and NARX cannot approximate the optimization algorithm accurately. Therefore, these deep learning-based approaches cannot capture a response like the response of the optimization algorithm in testing stage, as shown in Figure 10b. Although LSTM cannot capture a response as good as the response of the optimization algorithm in Figure 9b, it gives the best performance in Figure 10b, where it can capture a response similar to the response of the optimization algorithm.
## 3 Challenges in China's Weather Routing Services
### Limitations in Accurate Prediction of Ship Speed Loss  
Ship speed loss prediction is a critical technical challenge in weather routing services, serving as the foundation for vessel position estimation and optimized weather routing. While this is a global challenge, its impact in China is shaped by the specific models and data available domestically. Ship speed reduction primarily occurs under two scenarios: (1) voluntary speed reduction by captains to ensure safety during vessel or cargo emergencies, and (2) involuntary speed loss caused by additional resistance from hydrometeorological factors (e.g., wind, waves, currents), potentially leading to operational stagnation. Current methodologies for ship speed loss modeling are categorized into three approaches: theoretical models [32], empirical formulas [44], and machine learning (ML)-based frameworks [45]. Researchers are refining prediction accuracy and applicability through advancements in empirical formula optimization, computational fluid dynamics (CFD), and hybrid data model-driven strategies. The integration of artificial intelligence (AI) has enabled ML models to capture complex nonlinear relationships and high-dimensional data.  
However, even for vessels of the same class, prediction accuracy varies significantly due to operational heterogeneity, including differences in vessel age, cargo load, and hull fouling levels. While vessel-specific models can enhance accuracy, acquiring and processing high-quality datasets remains a major bottleneck for most ships. Furthermore, due to the scarcity of navigation data under severe sea conditions, model performance degrades markedly in extreme weather scenarios. These limitations directly impact the reliability of Estimated Time of Arrival (ETA) calculations and the effectiveness of route optimization provided by Chinese services.
# Synergistic Potential of Optical and Radar Remote Sensing for Snow Cover Monitoring
## 3 Data and Methods
### Methods
#### 3.1.2 Analysis of the Recent Impacts of Climate Change on Snow Cover  
To assess the temporal changes in SCA, we set the time span at a 5-year period (lustrum) due to the relatively short time coverage of MODIS records. This allowed us to determine the magnitude of the trend (slope), based on the average PSCPI, across the entire domain of each mountain range for four lustra (2001-2005, 2006-2010, 2011-2015, and 2016-2020) at a monthly scale. As suggested in previous studies, summer SCA time series may not be suitable for trend analysis [56], so we restricted our analysis to the snow season months (November to May).  
In this study, both parametric and non-parametric methods were employed to detect the magnitude of SCA trend. A linear regression analysis was used to estimate the trend's rate when the SCA time series were normally distributed; otherwise, we performed a non-parametric Sen's slope estimator [57]. Rates are shown as% per lustrum (%/lustrum). To assess normality, we decided on the Anderson-Darling test [58; 59] with a significance level of 5%. When \(p<0.05\), the null hypothesis is rejected, indicating that the SCA time series does not follow a normal distribution.
Furthermore, studies on crop FPAR retrieval mainly focused on crops such as wheat, maize and soybean [26; 27], but limited studies were performed on canola. Monitoring canola FPAR dynamics could effectively diagnose growth status and stage nodes, providing key variables for crop productivity models. Due to differences in the canopy structure between different crops, existing FPAR estimation models developed for other crops need further evaluation and validation when applied to canola [28; 29]. Therefore, to identify the robust hybrid estimation methods for improving the canola FPAR retrieval accuracy, the PROSAIL model, which is a classical model mechanism of radiative transfer for vegetation canopy, was employed and recompiled to simulate FPAR variations. A total of 29 VIs and two machine learning algorithms were devoted to retrieving the FPAR. The model-estimated and in situ measured FPAR from a two-yearfield experiment were used to evaluate the inversion performance. The main objectives of this study were to (1) reveal the capability of different VIs in interpreting FPAR variation, (2) analyze the sensitivity of VIs to changes in leaf and canopy parameters, and (3) identify the robust hybrid estimation methods that consider inversion accuracy in different canola growth stages.