esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Canola Oil Biodiesel/Diesel Blends in a Common Rail Diesel Engine
## 4 Conclusions  
Canola oil is characterized by high density and viscosity, a high cetane number, and low compressibility compared with diesel fuel. Due to its unique characteristics, the application effects of canola oil biodiesel/diesel blends in a common rail diesel engine was experimentally investigated in detail, by observing combustion characteristics, engine performance, and emission characteristics. The following summarizes the findings in this work:  
* Canola oil biodiesel is an oxygenated fuel, and the oxygen components have played an important role in the combustion process.
* At high engine load (0.52 MPa), the combustion pressure of ULSD, BD20, and PCO increases 34.6%, 47.1%, and 72.4% compared with that of at low engine load (0.13 MPa), respectively.
* The ignition delay of PCO and BD20 are shorter than that of ULSD by about 5.5\({}^{\circ}\) CA and 3.25\({}^{\circ}\) CA.
* The CO and PM emissions decreased considerably, but the NOx emission increased only slightly by 6% and 14% at 0.39 MPa and 0.52 MPa compared with 0.26 MPa.  
Through this study, we showed that the common rail diesel engine can run well on PCO without engine modification. In addition, by testing under different combustion conditions, we demonstrated  
Figure 8: Effect of test fuels on exhaust emissions according to BMEP. (**a**) CO; (**b**) NOx; (**c**) PM (particulate matter).  
that BD20 is the most optimal alternative fuel, compared with ULSD and PCO, based on the combustion, performance, and emission characteristics.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (Project No. 2014059723). All authors designed the experimental apparatus, analyzed the data, discussed the results and implications and commented on the manuscript at all stages. [PERSON] performed the experiments and wrote the paper. [PERSON] performed the experiments and contributed PM emission analysis tool. [PERSON] led the development of the paper. [PERSON] performed the result analysis and discussion.  
The authors declare no conflict of interest.
###### Abstract
[22] implemented an enhanced binomial thresholding and multi-features selection-based technique to classify the brain tumor and attained enhanced result. The work of [PERSON] et al. [23] implemented a DLA to detect the glioblastoma using hyperspectral 3D and 2D brain images. [PERSON] et al. [24] implemented a DL based brain tumor detection procedure and attained better values of sensitivity and specificity. The higher order spectra feature-based detection and classification of the abnormal section in a brain MRI is discussed by [PERSON] et al. [25]. Further, a considerable number of approaches are proposed and implemented by a considerable number of researchers to improve the detection accuracy on a class of brain MRI images ranging from the benchmark datasets and clinical images [26, 27, 28, 29, 30, 31]. The work in this paper aimed to evaluate the performances of the existing DLAs, such as AlexNet, VGG16, VGG19, ResNet50 and ResNet101 [20] for detecting the BT using the 2D brain MRI slices. Initial detection was performed with a transfer learning procedure using the attained deep-features and the SoftMax classifier. After finding the best suitable DLA for the considered task, the concatenation of the deep and hand-crafted features was performed to enhance the classification accuracy with the SoftMax classifier. Further, a detailed comparative study with other classifiers, such as random forest (RF), decision tree (DT), k-nearest neighbor (KNN), SVM-linear and SVM-RBF was also performed to attain better classification accuracy. For the experimental investigation, the 2D brain MRIs recorded using Flair, T2 and T1C images of the dimensions \(227\times 227\times 1\) were considered, and the essential images for this task were collected using the benchmark datasets, such as BRATS (without skull section) [32,33] and TCIA (with skull section) [34, 35, 36, 37] to train, test and validate the DLAs. Further, a clinical level dataset [38] with the skull was considered to validate the DLA. This clinical level dataset was already used to test the machine-learning systems [2, 3, 4]. In this work, the performance of the proposed system was confirmed by computing the accuracy, precision, sensitivity, specificity, F1-Score and negative predictive value (NPV). This work also implemented a ten-fold cross validation, and based on the average value, the performance of the proposed DLA was confirmed. The remaining parts of this work are set out as follows: Section 2 presents the materials and methods. Section 3 presents the details of experimental investigation, and conclusion of this work is discussed in Section 4.
# Design and Mechanical Properties of Flat Anchorage Limit Plate
## 3 Establishment of Numerical Model of Flat Anchorage  
According to the flat anchorage limit plate described in the previous section, a numerical analysis model was established using ANSYS finite element software, and the hexahedron element in the solid element was adopted. The model included a flat anchorage, a clip, a steel strand, and the newly designed limit plate, as shown in Figure 5. The conventional flat anchorage model without a limit plate for a comparative study is also shown in Figure 5a. Figure 5b shows the combined model of the limit plate and flat anchorage, Figure 5c shows a model of the clip, Figure 5d shows the steel strand model, and Figure 5e shows the limit plate model. As the main component materials involved were all steel, the structural steel simulation in the material library was directly selected in the software, and the elastic model was selected as the constitutive model. The material parameters involved in the numerical simulation are detailed in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Component** & **Elastic Modulus (MPa)** & **Density (kg\(\cdot\)m\({}^{-3}\))** & **Poisson’s Ratio** &
\begin{tabular}{c} **Compressive Yield** \\ **Strength (MPa)** \\ \end{tabular} \\ \hline steel strand/clip/flat & \(2\times 10^{5}\) & 7850 & 0.3 & 250 \\ anchorage/limit plate & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material parameters.  
Figure 5: Numerical model: (**a**) conventional flat anchorage (numbers 1-5 represent the number of holes), (**b**) new flat anchor system, (**c**) clip, (**d**) steel strand, and (**e**) limit plate.  
The model dimensions were as follows: the outer contour size of the flat anchorage was 185 mm (length) \(\times\) 48 mm (width) \(\times\) 50 mm (height), the diameter of the bottom hole was 18 mm, the diameter of the top taper hole was 27 mm, the taper of the hole was 14\({}^{\circ}\), the center distance between holes was 34 mm, and the edge of the side hole was 24.5 mm away from the short side of anchorage and 10.5 mm away from the long sides of both sides. The diameter of the steel strand was 12 mm, the height of the clip was 54 mm, the diameter of the bottom was 18.9 mm, and that of the top is 27.72 mm. The length and width of the limit plate were the same as that of the flat anchorage, with a height of 50 mm; the bottom cylindrical hole had a diameter of 12 mm, and the upper cylindrical hole had a diameter of 29 mm and a depth of 8 mm.  
The boundary conditions and internal contact settings of the model were as follows: the bottom surface of the flat anchorage was arranged as a fixed support; the contact surface between the top surface of the flat anchorage and the bottom of the limit plate was arranged to be only supported by compression; friction contact was arranged between the clip and the steel strand and between the clip and the anchorage. No contact occurred between the limit plate and the steel strand.
# Survey on 5G Physical Layer Security Threats and Countermeasures
## 1 Introduction
### Entry Points for Attacks to 5G Networks
#### 1.1.4 The External or 3 rd Party Network  
An external or 3 rd party network serves as a fourth entry point for threats against mobile networks, offering various user services, including internet browsing, corporate network interconnection, and roaming partner networks. Non-3 GPP access networks, especially when interworking with a Wireless Local Area Network (WLAN) [2], introduce security issues, such as disclosure of user information, permanent identity tracking, and network impersonation [26]. Additionally, bypassing access control and authentication processes, interference with charging, and prevention of user access to services are concerns [26; 27; 28]. Converged networks utilizing technologies like Worldwide Interoperability for Microwave Access (WiMAX) also pose threats due to weaknesses in the physical and Multiple Access Channel (MAC) layers, thereby leaving them vulnerable to spoofing, MITM, and eavesdropping attacks [29].
## The Ocean
### Observability of the Coastal Ocean Dynamics
#### 2.2.1 Observability of the Coastal OceanDynamics in the Central Mediterranean Sea
\begin{table}
\begin{tabular}{l c c} \hline \hline  & SARAL-AltiKa & Jason-2 \\ \hline Correlation & 0.7382 & -0.2481 \\ Std (HF radar) (m/s) & 0.0695 & 0.0518 \\ Std(satellite)(m/s) & 0.1055 & 0.0622 \\ Rms Diff (HF radar - satellite)(m/s) & 0.0942 & 0.0969 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistics of the comparisons between HF radar and altimetric across-track geostrophic velocities over the period comprised between 18 April 2013 and 8 May 2014. Twelve measurements are used for SARAL/AltiKa (repeat cycle of 35 days) and 36 measurements are considered for Jason-2 (repeat cycle of 9.9156 days; note that several cycles were missing for Jason-2).
# IoT Implementation of Kalman Filter to Improve Accuracy of Air Quality Monitoring and Prediction
## 4 Results  
### Basic Dynamic System Model  
The mathematical basis of the Kalman Filter algorithm is the linear algebra and Hidden Markov model. We can use the following equation to describe a basic dynamic system:  
\[x_{k}=F_{k}x_{k-1}+B_{k}u_{k}+w_{k} \tag{1}\]  
It means that each \(x_{k}\) (the signal values) may be evaluated by using a linear stochastic equation. Any \(x_{k}\) is a linear combination of its previous value \(x_{k-1}\) plus a control signal \(u_{k}\) and a process noise \(w_{k}\). And most of the time, there is no control signal \(u_{k}\), which is a certain external factor that affects the system. \(F_{k}\) is a state transition matrix acting on \(x_{k-1}\), and \(B_{k}\) is a control input matrix acting on \(u_{k}\). \(w_{k}\) is the process noise at time \(k\), that is, the influence of external uncertainty factors on the system, and we assume that its statistical characteristics are in accordance with the mean normal value of 0, and the covariance matrix is a multivariate normal distribution of \(Q_{k}\), which satisfies:  
\[p(w_{k})\sim N(0,Q_{k}) \tag{2}\]  
At the same time, at time \(k\), the observed value \(z_{k}\) of the sensor to the real state \(x_{k}\) of the system satisfies the following equation:  
\[z_{k}=H_{k}x_{k}+v_{k} \tag{3}\]  
The equation tells that any measurement value \(z_{k}\) (which we are unsure of its accuracy) is a linear combination of the signal value \(x_{k}\) and the measurement noise \(v_{k}\). \(H_{k}\) is the observation transfer matrix, which maps the real space \(x_{k}\) of the dynamic system into the observation space. \(v_{k}\) is the measurement noise, and it conforms to the multivariate normal distribution with a mean of 0 and the covariance matrix of \(R_{k}\), which satisfies:  
\[p(v_{k})\sim N(0,R_{k}) \tag{4}\]  
The process noise \(w_{k}\) and measurement noise \(v_{k}\) are statistically independent.  
The basic structure of the KF algorithm can be obtained from the above equations, as shown in Figure 4. The circle represents the vector, the square represents the matrix, the asterisk represents the Gaussian noise, and the dotted square in the lower right corner of the asterisk represents the covariance matrix corresponding to the noise.  
Figure 3: Machine learning in edge-to-cloud environment. (AQ: air quality index).
## 2 Materials and Methods
### Implementation of Encrypting/Decrypting Algorithms With FPGA Platforms
Regarding the proposed 8-stage pipelined implementation, a significant reduction of resources utilization (of a 0.75 factor) and power consumption (of a 0.65 factor) compared to a similar 16-stages pipelined design was demonstrated. As above described, a LUT-based solution has been used to implement the Sbox in the proposed AES algorithm, which is not an optimal solution for area-limited hardware but offers better performances in terms of data throughput compared to other solutions, for instance, based on combinatorial logic, aimed to minimize the resource utilization, as demonstrated in Reference [30]. In this context, in Reference [31], the author proposed an overview of the different strategies to implement compact Sbox function, based on both polynomial and normal bases. Furthermore, they introduced a compact Sbox implementation based on a multi-level representation of GF operations, obtained properly selecting a particular basis (isomorphism) and making appropriate improvements to the circuital solution. The proposed solution has demonstrated improvements of 20% compared to the most compact Sbox implementation reported in Reference [32]. Besides, [PERSON] et al. proposed two new FPGA implementations of the AES algorithm [33]; the first one, implemented on Xilinx Spartan-III (XC3S2000) FPGA, relies on fully parallel loop unrolled architecture, reaching a 25 Gbit/s data throughput value. The latter, implemented on Spartan-II (XC2S15) FPGA, is based on state data and LUTs to carry out the AES operations, such as Substitute Bytes and Mix Columns, combined into a single matrix, called \"T-box\"; this implementation is featured by low area utilization, achieving 2.2 Mbit/s maximum throughput. The Sbox implementations proposed in these works can be applied to our solution to significantly reduce the used hardware resources but probably reducing the maximum throughput, representing the main prerogative of the Wireless Connector communication system.
# Cross-Technology Interference-Aware Rate Adaptation in Time-Triggered Wireless Local Area Networks
## 3 CTI-Aware Rate Adaptation Framework
### CTI-Aware Rate Selection
endif if\(i\bmod(T_{\text{scan}}/T_{\text{slot}})=0\)then  Identify CTI signals. if CTI signals are identified then  Update the CTI projection table. endif endif if CTI is predicted in the next \(m\)-th slot then  Determine the STA \(k\) assigned to the next \(m\)-th slot. Set \(flag_{\text{CTI}}^{(k)}\) to on. Estimate the signal power of STA \(k\). Calculate \(MCS_{\text{SNR}}\) and \(MCS_{\text{SINR}}\) for STA \(k\). Compute \(\Delta MCS^{(k)}=MCS_{\text{SNR}}-MCS_{\text{SINR}}\). if\(flag_{\text{CTI}}^{(k)}=\text{on}\) and \(\Delta MCS^{(k)}\) has changed then  Return \(MCS_{\text{SNR}}\) and \(MCS_{\text{SINR}}\). endif else  Find STA \(k\) where \(flag_{\text{CTI}}^{(k)}=\text{on}\). Set \(flag_{\text{CTI}}^{(k)}\) to off. Return \(flag_{\text{CTI}}^{(k)}\). endif endfor
```  
**Algorithm 2** Packet error-based CTI-aware rate selection  
The STA performs rate adaptation against CTI by receiving \(\Delta MCS^{(k)}\), the difference between the MCS index based on channel noise and the MCS index including CTI impact, from the AP and incorporating it into its rate control. Although a separate message format could be designed to send the \(\Delta MCS^{(k)}\), we choose to use the existing MCS feedback message. By utilizing the MFB subfield in the VHT control field, which can convey redundant information about the channel state [39], we send the \(\Delta MCS^{(k)}\) through this message. The receiver includes in the MFB's MCS subfield the MCS index calculated from the SINR (including CTI impact) and includes the average SNR value for channel noise in the SNR subfield. The transmitter receives these values, calculates \(\Delta MCS^{(k)}\), and performs rate control accordingly. The P-CARA algorithm mitigates interference from CTI at the receiver side through \(\Delta MCS^{(k)}\), independently of the existing transmitter-side rate adaptation algorithm that addresses interference based on channel conditions. Consequently, interference mitigation is activated only when CTI impact is anticipated. This management is handled using \(flag_{\text{CTI}}^{(k)}\) to perform rate adaptation only when the impact of CTI interference changes. The proposed algorithms (M-CARA and P-CARA) can effectively mitigate CTI interference through the framework without direct communication with CTI-causing devices. Each algorithm is derived from existing WLAN interference mitigation methods, M-Base and P-Base, retaining their respective advantages while addressing CTI-related interference. (M-Base, a channel state measurement-based algorithm, may be more complex to implement but is better suited for adapting to channel variations and interference. In contrast, P-Base, which relies on packet error-based methods, is relatively simpler to implement but may offer lower interference mitigation performance compared to measurement-based methods).
# Semantic-Aware Low-Light Image Enhancement by Learning from Multiple Color Spaces
## 2 Related Work
### Semantic-Guided Methods
#### 2.2.2 Feature-Level Semantic Guidance  
Diverging from loss-level approaches, feature-level methods focus on extracting intermediate features from semantic segmentation networks and integrating semantic priors into feature representation spaces. [PERSON] et al. [31] leveraged semantic maps to guide texture restoration in super-resolution through Spatial Feature Transformation (SFT) layers. These layers modulate network behavior via affine transformations conditioned on segmentation probability maps, dynamically adjusting feature maps. [PERSON] et al. [32] developed a semantic-aware Retinex framework comprising three components: information decomposition, reflectance restoration, and illumination adjustment. Semantic features enhanced reflectance estimation, while reconstructed reflectance guided illumination refinement. [PERSON] et al. [33] proposed a Semantic-Aware Embedding (SE) module that computes cross-modal similarity between segmentation and enhancement features to generate semantic-aware activation maps, enabling context-sensitive enhancement.
###### Abstract  
_Phaeocystis globose_ (_P. glob_) are the most frequent harmful algae responsible for red tides in Qinzhou Bay, Guangxi. They pose a significant threat to the coastal marine ecosystem, making it essential to develop an efficient indicator method tailored to _P. glob_ outbreaks. In remote sensing water quality monitoring, there is a strong correlation between _P. glob_ and cyanobacteria, with phycocyanin (PC) serving as an indicator of cyanobacterial biomass. Consequently, existing research has predominantly focused on remote sensing monitoring of medium to high PC concentrations. However, it is still challenging to monitor low PC concentrations. This paper introduced the BP neural network (BPNN) and particle swarm optimization algorithm (PSO). It selects spectral bands and indices sensitive to PC concentrations and constructs a PC concentration retrieval model, in combination with meteorological factors, offering a comprehensive exploration of the indicative role of low PC concentrations in predicting _P. glob_ red tide outbreaks in Qinzhou Bay. The results demonstrated that the PC concentration retrieval model, based on the backpropagation neural network optimized by the particle swarm optimization algorithm (PSO-BPNN), demonstrated better performance (MAE = 0.469, RMSE = 0.615). In Qinzhou Bay, PC concentrations were mainly concentrated around 2-5 \(\mu\)g/L. During the _P. glob_ red tide event, the area with undetectable PC concentrations (PC \(<\) 0.04 \(\mu\)g/L) increased by 4.97 km\({}^{2}\), with regions below 0.9 \(\mu\)g/L experiencing exponential growth. Considering the variations in PC concentrations along with meteorological factors, we proposed a straightforward early warning threshold for _P. glob_ red tides: PC \(<\) 0.9 \(\mu\)g/L and T \(<\) 20 \({}^{\circ}\)C. This method, from a remote sensing perspective, analyzes the process of _P. glob_ outbreaks, simplifies PC concentration monitoring, and provides a reasonably accurate prediction of the risk of _P. glob_ red tide disasters.  
PC retrieval; BPNN; _Phaeocystis globosa_; Qinzhou Bay; Sentinel-2
## 2 Methodology
### Applying Tcn to Semg-Based Continuous Estimation  
The temporal convolutional network (TCN) was initially designed by [PERSON] et al. [31] for sequence modeling tasks. Their experimental results showed that TCN outperforms canonical recurrent networks (such as RNN, LSTM and GRU) across a diverse range of sequence modeling tasks (such as Sequential MNIST, Music JSB Chorales and Word-level PTB) [31]. The main architectural elements in the TCN are dilated causal convolution (modified from causal convolution) and residual connections.  
As shown in Figure 3, causal convolution only looks back at a history with a size linearly proportional to the network's depth. Differently from the traditional convolution neural network, causal convolution can not see the future data. In other words, it is unidirectional structure, not bidirectional. Thanks to this, causal convolution ensures that the model only uses the time series before the moment when doing forecast.  
In order to extract the features of longer time series, the TCN uses a modified causal convolution called dilated causal convolution [31], as shown in Figure 4a. It can extract longer time series at the same depth. Differently from the causal convolution, dilation causal convolution allows the input of convolution to have interval sampling. The interval between sampling points of convolutional kernel is determined by \(d\), whose value generally increases with the depth of the layer. This means that the receptive field increases exponentially with the network's depth. Therefore, for a certain receptive field, the depth of the network with dilated causal convolution is significantly less than that with causal convolution.  
In order to make the network's error transfer across layers and effectively prevent the gradient disappearing, the TCN constructs a residual block to replace one layer of convolution. As shown in the Figure 4b, the residual block contained two layers of convolution and nonlinear mapping. In each layer, weightnorm and dropout were added to regularize the network.  
Figure 3: The structure of causal convolution, where each circle is a neuron. Solid arrows are used to depict the data flow of the right-most output neuron, and dashed arrows represent other data flow.  
As sEMG is one type of sequence modeling, TCN can be adopted to extract sEMG's features. In this paper, we propose a novel method that applies TCN to the sEMG-based continuous estimation. When directly applying this TCN to continuously estimating the angles of the 10 main joints of the finger (based on Ninapro dataset), it can only get a terrible precision rate (i.e., the Pearson correlation coefficient, PCC), 65%, which will be explained in the following section.