esa-sceva/satcom-chunk-collection huggingface dataset:
# Instantaneous Networking Service Availability for Disaster Recovery
## 7 Discussion  
### State Machine Diagram in Networking Service Recovery  
The state transition diagrams of each LI-Cloud server and each user in a disaster area are illustrated in Figures 8 and 9, respectively. The set of states at an LI-Cloud server includes the state of no networking service, the state of local networking service launching, the state of local service area extension, and the state of global network service launching. The set of states at a user includes the state of no networking service, the state of local networking service, the state of connection to extended service area, and the state of global networking service available.  
Figure 6: I-NSA for users in the scenario of sequential deployment of networking services with (uniform distribution of restoration. Ta, Tb, and Tc are set to 100, 500, and 900, and \(T_{AG}\), \(T_{BG}\), and \(T_{CG}\) are set to 1100, 1500, and 1900, respectively).  
Figure 7: I-NSA for users in the scenario of sequential deployment of networking services with (Possion distribution of restoration. Ta, Tb, and Tc are set to 100, 500, and 900, and \(T_{AG}\), \(T_{BG}\), and \(T_{CG}\) are set to 1100, 1500, and 1900, respectively).  
For an LI-Cloud server, the state of no networking service refers to the state in which the server has not started its networking services yet. The state of local networking service launching is the state in which the LI-Cloud start to provide local networking services to users. The state of local service area extension refers to the state in which the LI-Cloud is connected to other LI-Clouds and enables the users in the LI-Cloud to communicate with users at other local clouds. The state of global network service launching refers to the state in which the LI-Cloud is connected to the Internet. For a user, the state of no networking service refers to the state in which the user has no networking services available. The state of local networking service is the state in which the user obtains the local networking services from its LI-Cloud server. The state of connection to an extended service area refers to the state in which the users are able to communicate with users at other local clouds. The state of global network service available refers to the state in which the user is able to connect to the Internet.  
The conditions of state transition of an LI-Cloud server are described in Table 3, in which the number of transitions corresponds to the Figure 8. Moreover, the state transition conditions of a user are illustrated in Table 4, in which the number of transitions corresponds to the Figure 9.  
Figure 8: State machine of an LI-Cloud server.  
Figure 9: State machine of a user.
## 4 Experiments and Results  
This section discusses the experiments conducted to evaluate the performance of the modules, namely (1) Trajectory Generation, (2) Congestion Prediction, and (3) Congestion Visualization, implemented in the crowd monitoring framework. In this section, we describe the datasets used in this study the evaluation metrics employed to measure the performance of the modules, the implementation details, and the results of the experiments.
# Explaining Deep Learning Models for Tabular Data Using Layer-Wise Relevance Propagation
## 4 Results
### Validation of the Subset of LRP Highlighted Features Importance  
It is important to verify that the features highlighted as important by LRP are genuinely pivotal in driving the model decisions. Therefore, we use the subset of important features highlighted by LRP as input to traditional ML techniques (e.g., logistic regression, random forest) and proposed 1D-CNN. The last five rows in Table 3 show that using features generated from LRP values did not yield good results with traditional ML classifiers. However, we achieved good results with the deep 1D-CNN (M-1D-CNN-4-16* and M-1D-CNN-5-16*) as shown in Table 3. The interesting point is that our 1D-CNN for LRP surpassed the existing kernel [59] results both in terms of accuracy and precision while trained on this subset of features. We achieved a highest accuracy of 85.54% with a precision of 73.995% and a F1-score of 72.603%. Moreover, we achieved higher a F1-score of 73.23% from the model with using LRP values as features using SMOTE for balancing the data and with batch size 200 and a LR of 0.00001. A key point to note is that using features derived from LRP values, all 1D-CNN models gave results close to 83%. In addition, it is to clarify that the main cause of improvement is the proposed strategy which achieved a high F1-score i.e., 72.603%. Whereas, using SMOTE gave an additional enhancement of 0.63% to  
\begin{table}
\begin{tabular}{l  
get a higher F1-score i.e., 73.23%. SMOTE without the proposed strategy was not able to achieve the achieved F1-score.  
The used subset of features contain 16 features. These are selected from the LRP values generated by the process as explained in Section 3.3. The good performance with this subset of features proves that the highlighted features are important and can generate an almost similar result through a simple classifier instead of a DL model. Hence, if needed in a situation where memory and processing is an issue e.g., when deployed in IoT or on edge device, a smaller simple classifier can be used rather than a deep neural network.
# A Robust Method Based on Deep Learning for Compressive Spectrum Sensing
## 2 System Model
### Compressed Signal Reconstruction  
The compressed signal reconstruction is introduced and formulated as an iterative problem based on ISTA. The reconstruction of the compressed signal can be formulated as a convex optimization problem, which is expressed as  
\[\min_{\mathbf{\mathrm{X}}}\frac{1}{2}\parallel\mathbf{\Phi}\mathbf{\mathrm{X}}-\mathbf{ \mathrm{Y}}\parallel_{2}^{2}+\lambda\parallel\mathbf{\mathrm{X}}\parallel_{1}, \tag{3}\]  
where \(\lambda\) is the preset regularization parameter.  
The ISTA is a popular first-order proximal algorithm that is well-suited for solving large-scale linear inverse problems. Specifically, the ISTA iteratively solves the above convex optimization problem through the following steps  
\[\mathbf{\mathrm{R}}^{(k)}=\mathbf{\mathrm{X}}^{(k-1)}-\rho\mathbf{\Phi}^{\top}(\mathbf{\Phi} \mathbf{\mathrm{X}}^{(k-1)}-\mathbf{\mathrm{Y}}), \tag{4}\]  
\[\mathbf{\mathrm{X}}^{(k)}=\min_{\mathbf{\mathrm{X}}}\frac{1}{2}\parallel\mathbf{\mathrm{X} }-\mathbf{\mathrm{R}}^{(k)}\parallel_{2}^{2}+\lambda\parallel\mathbf{\mathrm{X}} \parallel_{1}, \tag{5}\]spectrum energy detectors and deep learning-based methods can effectively address this problem. The spectrum energy detector determines the occupancy status of a subband by comparing its frequency-domain energy with a threshold \(\tau\). The energy of the \(n_{c}\)-th subband can be expressed as  
\[E_{n_{c}}=\frac{1}{N_{sc}}\sum_{n=1}^{N_{sc}}|\hat{\textbf{x}}_{f}((n_{c}-1)*N_{ sc}+n)|^{2},n_{c}=1,2,\cdots,N_{c}, \tag{6}\]  
where \(N_{c}\) denotes the number of the sub-channel, \(N_{sc}~{}=~{}\frac{N}{N_{c}}\) denotes the number of samples per sub-channel. If \(E_{n_{c}}>\tau\), the \(n_{c}\)-th channel is determined as occupied by a PU.  
Deep learning-based methods transform the WSS problem into a multi-label classification problem. The reconstructed signal is used as input to a neural network to determine the occupancy status of each sub-channel. We note that deep learning-based methods can leverage more information from the reconstructed signal, thus achieving higher sensing accuracy.
## 4 Proposed Framework
### Smart Contract Implementation for Access Control and Model Updates  
The IoMT network, running on the EdgeGuard framework, promises to have extremely complicated architectures for safe and verifiable smart contracts. Our implementation includes three leading types: device access control, model update verification, and secure aggregation protocols. The layer of smart contracts is important as it facilitates the transition between the federated learning process and the incorporation of blockchain security. The proposed smart contract manages device registration, validates model updates, and ensures secure aggregation according to the privacy and security requirements of healthcare data. Specifically, this study targets three main aspects:  
* **Access control**: Ensures only authorized IoMT devices participate in the federated learning process. * **Model update verification**: Validates and records model updates in an immutable manner. * **Secure aggregation**: Implements privacy-preserving model aggregation using multi-party computation. Algorithm 1 presents the comprehensive smart contract protocol that governs these interactions within EdgeGuard. This protocol further completes our description of the blockchain security layer B in Section 3.1, such that the federated learning process can remain robust and free of attacks only when legitimate devices are involved in the training model. It makes use of the adaptive aggregation function in Equations (34)-(36), in which quality scores \(q_{i}\) and reliability scores \(r_{i}\) are used for weighing each edge device. The implementation guarantees the following three important properties:  
1. **Security**: Through robust access control and validation mechanisms. 2. **Privacy**: Via secure multi-party computation during aggregation. 3. **Verifiability**: Through immutable blockchain records of all operations. This kind of architecture of the smart contract provides a needed base in the IoMT environment for secure and verifiable federated learning, yet it certainly aligns with EdgeGuard's decentralized approach to medical resource management.
# Advances on Water Quality Detection by UV-Vis Spectroscopy
## 3 Theoretical Basis of UV-Vis Spectrum Data Processing
### Data Preprocessing  
During data acquisition, due to high-frequency noise, personnel operation, external environment, and other factors of the instrument and equipment, some undesirable factors are often involved in the establishment of the model, which affect its prediction accuracy. This interference information cannot completely depend on the equipment modification and can be eliminated. Therefore, before the qualitative and quantitative analysis of spectral data, effective preprocessing should be completed. The prediction accuracy of the model can be improved by selecting the appropriate preprocessing method. Since [PERSON] and [PERSON] published their research results in 1967, the Savitzky-Golay (SG) smoothing filter has gradually become a broadly used method to improve the signal-to-noise ratio of most signals [15]. Therefore, the SG smoothing filter plays an important role as a data preprocessing method for the UV-Vis spectrum. If the window size changes, the smoothing effect will be different. The larger the window is, the smoother the spectral curve becomes. However, with the increase in the window size, the spectral data also loses some information. [PERSON] et al. [16] proposed a time-domain method to analyze the estimation performance of SG filters, which included estimation performance of SG smoothing filters and SG differentiation filters. \begin{table}
\begin{tabular}{c c} \hline
**Wavelength Range** & **Material Characteristics** \\ \hline
200–220 nm & nitrate, nitrite \\
220–250 nm & conjugated diene, unsaturated aldehyde, unsaturated ketone \\
250–380 nm & organic matter \\
380–750 nm & turbidity \\ \hline \end{tabular}
\end{table} 
Table 1: Absorption spectrum range and substance characteristics of various substances. In recent years, with continuous algorithm innovation, wavelet analysis has been gradually adopted by many researchers to effectively suppress spectral data noise. The wavelet algorithm can effectively solve the problem that spectral data are often disturbed by high-frequency noise. Wavelet analysis has more advantages in processing non-stationary process signals and broadband noise signals [17; 18]. Through the use of wavelet transform and other noise reduction algorithms to process UV-Vis spectrum data followed by complete wavelength modeling, the researchers found that the wavelet transform can not only remove noise from the spectrum data but also improve the modeling effect. Other common algorithms for reducing the noise impact of spectral data include standard normal variate (SNV), first-order differential, second-order differential, and multiple scattering correction (MSC) (Table 2). In addition to the above-mentioned common algorithms for noise reduction of spectral data, some researchers have explored some relatively novel noise reduction methods and made significant contributions in processing UV-Vis spectral data. [PERSON] et al. [19] conducted made a continuous sampling
## 3 CNN Model Construction  
### Improved Depth-Separable Convolution  
The proposed network model used a convolutional approach based on Depthwise Separable Convolution (DSC). DSC was initially introduced into the MobileNet network model and comprises two components, that is, Depthwise Convolution (DW) and Pointwise Convolution (PW) [42]. In conventional convolution operations, the number of channels in each convolution kernel is the same as that in the input image, and a multichannel convolution operation is performed. However, in the DW convolution operation, the number of convolution kernels is the same as the number of channels in the input image, a single-channel convolution operation is performed, PW convolution is introduced, and \(1\times 1\) convolution kernels are used for the conventional convolution operation. To further improve the feature extraction capability, reduce the overfitting phenomenon, and prevent the gradient explosion problem, this model adds a Batch Normalization layer (BN) and ReLU activation function after the DW and PW convolution, respectively [43], to build an improved depth-separable convolution. Please refer to Equation (1) for the BN layer and Equation (2) for the ReLU activation function, respectively.  
\[\left\{\begin{array}{c}\mu_{B}\leftarrow\frac{1}{m}\sum\limits_{i=1}^{m}x_{ i}\\ \sigma_{B}^{2}\leftarrow\frac{1}{m}\sum\limits_{i=1}^{m}\left(x_{i}-\mu_{B} \right)^{2}x_{i}\\ \hat{x}_{i}=\left(x_{i}-\mu_{B}\right)/\sqrt{\sigma_{B}^{2}+\varepsilon}\\ y_{i}\leftarrow\gamma\hat{x}_{i}+\beta\equiv BN_{\gamma,\beta}(x_{i})\end{array}\right. \tag{1}\]  
Equation (1), where: \(x_{i}\) denotes the convolutional layer input, \(\mu_{B}\) denotes the mean of a single factor, \(\sigma_{B}^{2}\) denotes the variance of a single channel, \(\hat{x}_{i}\) denotes the normalization, \(\gamma\) denotes the scaling factor, \(\beta\) denotes the translation factor, and \(y_{i}\) denotes the value normalized by introducing learnable parameters.  
\[\text{Re}lu(x)=\text{max}(x,0) \tag{2}\]  
Equation (2), where: \(x\) represents the input value and \(\text{Re}lu(x)\) represents the model output.
## 4 Results  
### Performance of the EF-LUE Model Driven by Ground Measurements at Eddy Covariance Flux Tower Sites  
Cropland flux tower sites were grouped according to their locations in the climate zones in Figure 1. The three model parameters, i.e., \(\varepsilon_{max}\), \(T_{opt}\) and \(VPD_{0}\), were optimized for each climate zone using ground measurements of GPP from all sites in the corresponding climate zone. The parameter optimization procedure was applied to both the EF-LUE model (denoted as 'with EF') and the model without the water availability constraint (denoted as 'without EF'), resulting in two sets of optimized parameters, respectively (Table 5), which would be used in the two LUE models to estimate GPP for comparison. The \"All\" values (also as 'default') in Table 5 were obtained by the optimization using data from all available cropland flux tower sites listed in Table 2. Obviously, these model parameters varied with different climate zones. In theory, there should be no significant differences in the optimized parameters between using the EF-LUE model and using the model 'without EF' in each climate zone because these three parameters are inherent properties of the crop physiology in response to environmental conditions and should not change with model structures (specifically here for the LUE models 'with EF' and 'without EF'). However, some differences in the parameter values between the model EF-LUE and the model 'without EF' were observed in Table 5, which might be attributed to the uncertainties in the model structure as mentioned by [PERSON] et al. (2017). The averaged values of \(\varepsilon_{max}\) over all climate zones were 2.812 \(\pm\) 0.887 with the EF-LUE model and higher than that used in other models (i.e., 1.044 g C MJ\({}^{-1}\) in MOD17). The highest \(\varepsilon_{max}\) (=3.999) appeared in climate zone 'Bwk' (Table 5) which came from 'DaMan' site with C4 crop (maize) (Table 2). The lowest value of \(\varepsilon_{max}\) (=1.506) was in climate zone 'Csa' which was contributed by 'IT-CA2' site with C3 crop (winter wheat). Our values of \(\varepsilon_{max}\) parameter are within the range in previous studies found at site-level with \(\varepsilon_{max}\) ranging from 2.25 to 4.06 g C MJ\({}^{-1}\) for C4 crops and from 1.43 to 1.96 g C MJ\({}^{-1}\) for C3 crops (3,7,1,7). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Product Name**} & **Temporal Resolution** & **Spatial Resolution** & **Algorithm** & **Temporal Coverage** & **Reference** \\ \hline MOD17 & 8 days & 500 m & LUE & 2000–present & [20] \\ Revised EC-LUE & 8 days & 500 m & LUE & 1982–2018 & [68] \\ GOSIF GPP & 8 days & 5 km & statistical & 2000–2018 & [10] \\ NIRv GPP & Monthly & 5 km & statistical & 1982–2018 & [7] \\ PML-V2 & 8 days & 5 km & canopy & 2002–2018 & [69] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Satellite observation-based GPP data products used for intercomparison in this study.
# An Electric Load Simulator for Engine
## 1 Introduction
Considering that the electro-hydraulic torque servo system has some disadvantages, such as nonlinearity and time-variable parameters, high cost, and bulkiness, [PERSON] selected the electric load simulator to reproduce load force. The velocity-loop compensation based on improved fuzzy control and combined with PID control through the exponential function was proposed to eliminate extraneous torque. Experimental results showed that the extraneous torque eliminated ratio could reach 92.2% [15]. However, there are many nonlinear factors in the actual system operation, including mechanical friction and structural clearance, which make it impossible to completely eliminate the interference of extraneous torque. [PERSON] designed an electric load simulator for a linear rudder. The load system used permanent magnet direct current actuators to provide aerodynamic force for the linear servo rudder. PID with forward feed control was used to eliminate the extraneous force. Simulation and experimental results demonstrated that the closed system had less than 5% error between the target force and feedback force. The load system had good tracking performance [16]. In order to study the influence of gas force on the exhaust valve and promote further developments and practical application of camless valvetrains, this paper mainly researches the gas force characteristics of the exhaust valve during engine exhaust stroke and designs the load simulator to simulate the load force under semi-physical conditions. In this paper, we propose a 3D engine model for finite element simulation to obtain gas force characteristics in the exhaust stroke. The system scheme and control strategy of the electric load simulator are designed to reproduce ideal gas force on the exhaust valve. Simulation and experimental results verify that the proposed electric load simulator has satisfactory dynamic performance and stability. The main work is organized as follows. Load characteristics of the exhaust valve for camless valvetrains are presented in Section 2. Section 3 presents the configuration and performance requirements of the electric load simulator. Section 4 proposes the mathematical model of the electric load simulator, which is verified by a computer-controlled experimental test bench. Section 5 analyzes the simulation results and the experimental results of the electric load simulator and discusses the application of the designed load system. Section 6 summarizes the whole work and contributions of this paper.
## 3 Concept of an Automaton for Data Validation
### Transitions  
The second parameter is the transitions. In our automaton, state transitions are triggered by one of the following four types:  
1. Incoming message. For each incoming message, an identifier switches its state.
2. Expiring timer. In case of an expiring timer, we terminate the run for the affected identifier.
3. Overtaking of a subsequent identifier. In case of an overtake, we terminate the run for all elements with a smaller rank than the actual identifier.
4. Content violation (Category 3 & 4). Content violations indicate an inconsistency in the considered data set.  
Figure 4 provides an overview of all automaton transitions of our example process from Figure 2. As we develop a finite \(\omega\) automaton, we have a transition function, mapping each possible input to exactly one output. Our function \(\delta\) takes as input the current state \(q\in Q\) and the trigger \(\sigma\in\Sigma\) and maps to a new state from \(Q\) (\(\delta=Q\times\Sigma\to Q\)) Since our alphabet \(\Sigma\) consists of four different types of triggers, our \(\sigma\) is of the form of \(m\), for an incoming message, \(ex\), for an expired timer, \(o\), for a detected overtake of a subsequent product, or \(cat_{3}/cat_{4}\), which denotes a content violation in our gathered data.  
Figure 3: Resulting states from example process. To determine states, we refer to the powerset of the process steps.
# A Review of Long-Term Skid Resistance of Asphalt Pavement
## 3 Skid Resistance Mechanism and Influencing Factors of
### Summary  
At present, a lot of achievements have been made in the research on the influencing factors of the skid resistance of asphalt pavements. In terms of pavement materials, the mineral composition, particle size and shape, and surface texture of aggregates, as well as the type and amount of asphalt, all significantly affect the skid resistance. High-quality aggregates combined with appropriate asphalt can lay a good foundation for skid resistance. In terms of construction technology, the improper control of any link will weaken the skid resistance. In terms of traffic loads, heavy-duty vehicles, high speeds, and large traffic volumes will cause the skid resistance to decline rapidly due to increased pavement wear and changed stress states. In the natural environment, precipitation, temperature, and sunlight can cause water films, deformations, and aging of asphalt pavements, and dust, oil pollution, and other contaminants filling the pavement texture will all have a negative impact on the skid resistance. In terms of maintenance management, the untimely cleaning and delayed repair of pavements will accelerate the deterioration of the skid resistance. However, there are still shortcomings in the current research. Most of the studies explore each factor in isolation, lacking an in-depth and systematic analysis of the variation law of the skid resistance under the interaction of multiple factors. Moreover, the quantitative research on some factors is insufficient, affecting the accuracy of the pavement skid resistance prediction model. In the future, it is necessary to strengthen the research on the coupling of multiple factors and quantitative analysis.